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  •  
    Ep 25: Finding your niche and focus with Magnus Ström, Ström Architects
    2021-10-29 (duration 29m)
    [transcript]
    23:37 magazine, or, Wallpaper or
    14:20 grand or something like that. Or
    12:43 of it, or the place of it or
     
    Ep. 24 - Marketing and Business Development Fundamentals, Iben Falconer, SOM
    2021-10-15 (duration 38m)
    [transcript]
    32:49 or half an hour or half a day,
    16:56 Or neighbourhood.
    11:17 things like that, or
     
    Ep 23, PT 2, Strategy, Purpose, Data and Events Danielle Regan and Dave Hendy, Mace
    2021-09-24 (duration 32m)
    [transcript]
    23:19 or diversity, inclusion, or
    10:07 What's going on there? Or? Or
    21:56 recognise colleagues or, you
     
    Ep 23, PT 1, Strategy, COVID 19 Response and Elevating The Role of Marketing, Danielle Regan and Dave Hendy, Mace
    2021-09-24 (duration 27m)
    [transcript]
    05:58 Were there particular things? Or
    06:38 planning space. Or really trying
    20:48 teams, or marcomms teams can
     
    Ep 22: Events, Career Development and Employee Ownership, Vivi Koroma Kala, Jestico + Whiles
    2021-07-16 (duration 26m)
    [transcript]
    13:30 more people or?
    17:52 known for? Or,
    24:10 clients or stakeholders, how
     
     
    Ep 21: People, Projects and Digital Technology with Stephen Melville and James Solly, Format Engineers
    2021-07-02 (duration 28m)
    [transcript]
    24:08 person or with James or with
    00:34 on Apple podcasts, or Castbox.
    03:07 or at least not traditional
     
    Ep 20: Communications, Reputation, and Being A Responsible Business with Rebecca Snow - Stiff + Trevillion
    2021-06-22 (duration 35m)
    [transcript]
    33:50 to get on frameworks or or
    32:54 marketing, or marketing
    07:45 outward facing activity or
     
    Ep 20: Communications, Reputation, and Being A Responsible Business with Rebecca Snow - Stiff + Trevillion
    2021-06-18 (duration 35m)
    [transcript]
    33:50 to get on frameworks or or
    32:54 marketing, or marketing
    07:45 outward facing activity or
     
    Ep 19: Part III: Clients, Copy and Websites with Nikita Morell, Amy Edwards and Dave Sharp
    2021-06-04 (duration 31m)
    [transcript]
    13:30 an avatar or a profile, or
    17:32 meeting or a conversation or if
    03:12 1000 LinkedIn connections or
     
    Ep 19: Part II: Marketing Channels (LinkedIn, Email, Tik Tok) with Dave Sharp, Nikita Morell and Amy Edwards
    2021-05-28 (duration 34m)
    [transcript]
    21:49 ceramic art or, or painters or
    07:10 photographer's you're hiring or
    09:27 to becoming unforgettable or
     
    Ep 19: Part I: Storytelling, Video and the fall of Instagram with Amy Edwards, Nikita Morell and Dave Sharp
    2021-05-17 (duration 32m)
    [transcript]
    19:55 stories or, or feel confident to
    12:46 the big thing or or do you
    19:40 or the the noise or you know,
     
    Ep 18: We're changing our name - Say hello to Marketing In Times of Recovery
    2021-05-14 (duration 1m)
    [transcript]
    00:11 crisis or panic for over a year.
     
    Ep 17: Focus, Fun and New Ventures with George Bradley and Ewald Van Der Straeten from BVDS
    2021-03-12 (duration 32m)
    [transcript]
    21:25 or, or income or anything like
    16:18 particularly eloquent or
    22:26 milestones or completions coming
     
    Ep 16: Campaigns, Strategy and Winning Work with Emily Binning from WSP
    2021-02-26 (duration 44m)
    [transcript]
    03:28 opportunity or something that's
    13:10 A strategic pursuit, or an
    15:06 actually on LinkedIn, or they
     
    Ep 15: Phones, People and Pancakes!!! with Graham Handley and Susie Lober
    2021-02-12 (duration 30m)
    [transcript]
    10:03 or overlooked. Or you say, Oh,
    16:11 Graham? Or
    27:31 your standout campaigns Susie or
     
    Ep 14: One Tip Special Compilation Episode
    2020-12-15 (duration 20m)
    [transcript]
    06:06 the world, or you know, what's
    11:15 approval, or your latest
    11:15 competition win or something,
     
    Ep 13: Global & Internal Communications and Campaigns with Vanessa Talbot-Brown, Buro Happold
    2020-12-04 (duration 39m)
    [transcript]
    20:55 article, or podcast, or social
    11:23 office, or
    18:50 were laughing or, literally,
     
    Ep 12: Winning Work Through Lockdown with Digital Marketing with Amos Goldreich
    2020-11-12 (duration 37m)
    [transcript]
    32:48 architecture world, or it could
    21:12 or tweak it or design
    21:23 Yeah. or changing the image or
     
    Ep 11: Networking, Social Media and Running for RIBA with Sumita Singha
    2020-11-05 (duration 21m)
    [transcript]
    18:55 marketing or certain
    19:02 captured your attention or
    01:48 together, you know, gardening or
     
    Ep 10: Branding and Marketing as an Investment with Renee O'Dobrinak, Hawkins Brown
    2020-10-22 (duration 31m)
    [transcript]
    05:17 brand refresh or assessing your
    08:45 reassessing their brand, or just
    19:07 animation they've done, or a
     
    Ep 09: Branding, Employee Ownership and Communications with Daire Hearne, Make
    2020-10-08 (duration 40m)
    [transcript]
    10:00 replicate or reflect or mirro
    04:34 know, 3D printing, or VR, or,
    31:43 could copy or emulate or you
     
    Ep 08: Communications, Community & Context with Julia Nicholls, Squire and Partners
    2020-09-29 (duration 37m)
    [transcript]
    08:00 But yeah, and you used to have that a big picture of the Thameslink platform in the bar. Yeah. Because I do remember that because like, because I love trains. I am that person, and so is my son. And I guess in terms of your comms approach, as you how has that changed, if you've moved from being a smallish, a small to mid sized practice from, say, 30? To 250? I mean, what stuff? Have you done differently? Or had to try? Or? Or what changed for you? Apart from just having a bigger team.
    32:57 That's so nice. That's brilliant. And on to my final two questions. So what standout marketing campaigns have you seen or admired during this kind of current kind of period?
    31:04 And then we're doing our fourth year of one of my favourite things to do every year, which is called the winter windows collaboration. The illuminations festival that happens every January or didn't happen last year.
     
    Ep 07: Clients, Business Development and Campaigns with Helen Shea & Christine Baltas
    2020-09-10 (duration 27m)
    [transcript]
    24:35 Yeah, definitely, definitely want to, you know, keep focused on your areas of strength and be that markets or practice areas or services or sectors, whatever it is, you know, just just stand firm, you know, certain, you know, just yeah, keep your cool, I think,
    23:51 Some people feel that there's that term busy fools. So everyone sort of runs around. Yeah, doing stuff sending emails and not doing the follow up or just doing a sort of massive campaigns. Just doing really massive campaigns and scatter gunning everything or, or not doing anything. thoroughly. So
    13:51 Yeah. And I guess in terms of have you introduced any kind of new or different things, so you know, when you've been doing your campaign Christine for example, I mean, was it long pieces or other other kind of different ways that you were communicating with your clients?
     
    Ep 06: Smart Cities, Branding & Digital with Rick Robinson, Jacobs
    2020-09-04 (duration 36m)
    [transcript]
    12:57 And then you know, The rest of it would be replying to things or would be just sharing links other people had shared or it happened to see, etc.
    07:18 How did your companies bosses feel about that? Was there any pushback on that? Or was that seen as a positive?
    14:01 So I guess it's how you package it right? Is that is that like anything else in the Facebook vein, or did they do it was wildly different?
     
    Ep 05: Architecture, Marketing & Communications with Celeste Bolte, Bowerbird UK
    2020-07-31 (duration 30m)
    [transcript]
    09:33 Particularly for people with children or who are not leaving sort of in the central area.
    17:53 Absolutely. I think it's interesting that you also sort of mentioned other technologies or podcasts like this. Fantastic one.
    24:42 Yeah. And my onto my final two questions. So in terms of marketing at the moment, are there any particular campaigns during lockdown that you've seen or admired?
     
    Ep 04: Strategy, Technology & Digital with Pascale Scheurer
    2020-07-07 (duration 34m)
    [transcript]
    07:43 Do you miss it? The architectural world?
    06:32 People actually use pencils. Absolutely, yeah. Yeah, that's amazing. Are they doing virtual consultations in Marlow at the moment, man or
    06:44 they would have to be now but it's been very much assitive. You know, you've got to understand that it's world of architecture is primarily I think 90% or so is, you know, sort of man and his dog. Yeah. Or one woman and her dog which is great, and it's fine and the world of of how, you know, housing extensions are residential extensions of that sort of world. So in a way, do you really need to spend the money to get AutoCAD or something like that? It's not what people need and it's not what plans require. So it's very different from London. And, you know, big firms are hundreds of people. But they're in the minority. That's the thing. You know, firms over 100 people are very much the minority in architecture, I know in engineering, they tend to have bigger firms. But yeah, architects are still very much a small practice as a profession.
     
    Ep 03: Communications, Relationships & Collaboration with Helen Gawor, GKR Scaffolding
    2020-07-03 (duration 32m)
    [transcript]
    27:04 Okay, so on to my final two questions. I'm in terms of marketing or communications campaigns during lockdown as or as we transition it out of it. Has there been any standout campaigns that you've seen and really thought were very, very good?
    19:02 I think so yeah, I think until you've actually visited a site, you don't know how they run, you know, you work, they work or whoever's in small, tight, you know, such tight sites, it's, it's a whole different world until you fully appreciate how tough that is, you can't do your job properly. I do completely agree with that.
    15:29 Okay. So in terms of tools that you've been using, and communications tools and marketing tools, what new things have you introduced, or what new things have kind of had to come to prominence during this time?
     
    Ep 02: Strategy, Agility & Change with Liz Earwaker, AECOM
    2020-07-03 (duration 30m)
    [transcript]
    06:05 Is that you're asking directly as a marketing team, or is it more from the people on the ground?
    23:27 What people did people feel confident to pick up the phone, or was it one of those we had to kind of chisel people along?
    21:06 And looking back at two recessions that you've been through because not just that one, and what do you what are any kind of key takeaways or key things that you'd learned man that you apply to what you're doing now?
  •  
     
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    Season 3: Episode 33 - A Midlife Less Ordinary
    2021-09-26 (duration 43m)
    [transcript]
    26:42 produced or directed. Or the guy
    38:35 or nay? Richard the greedy or
    29:47 recommendation or non
     
    Season 3: Episode 32. A Midlife Less Ordinary
    2021-09-19 (duration 41m)
    [transcript]
    03:59 news this week. or GB news or
    15:03 exclamation mark or question.
    34:26 commuter in Chicago or
     
    Season 3: Episode 31 A Midlife Less Ordinary
    2021-09-12 (duration 42m)
    [transcript]
    03:22 promoting their album or, or
    02:36 darker cheese or dark or milk.
    11:15 or movie?
     
    Season 3: Episode 30 The New Season
    2021-09-05 (duration 44m)
    [transcript]
    03:22 promoting their album or, or
    02:36 darker cheese or dark or milk.
    11:15 or movie?
     
    The Secret Love Of Pet Owners
    2021-07-25 (duration 35m)
    [transcript]
    33:07 products or service or whatever
    03:31 or St. Trevor Southern rock
    05:28 gear solid game or something.
     
     
    Iron Imperium: Strength And Honour
    2021-07-11 (duration 45m)
    [transcript]
    19:48 shirts or Metallica, or if they
    03:25 or you draw?
    06:08 ever read or written
     
    Christopher Shayne - Ten High
    2021-07-04 (duration 47m)
    [transcript]
    08:06 that unless you're Motorhead or
    19:08 flashy or the most anything
    27:19 strength? 1967 1968 1969 or
     
    Learn Boxing: Raging Bulls*%t
    2021-06-20 (duration 41m)
    [transcript]
    08:13 this or that or we're you know,
    18:37 weight or featherweight?
    00:47 Yeah, Rocky free or rocky
     
    All About Cycling - Blazing Saddle Sore
    2021-06-13 (duration 45m)
    [transcript]
    43:06 been a born again one or or
    15:47 or poach Mario,
    19:53 Okay, or Yes, it
     
    Indie Horror Movies - With Dani Thompson
    2021-06-06 (duration 43m)
    [transcript]
    26:38 or something
    02:53 independent or a movies no
    13:51 like the location or Bulgaria
     
    Antique Bottle Collecting - I'm Digging Your Dump!
    2021-05-30 (duration 41m)
    [transcript]
    32:38 coking fork, or obiora, or
    29:55 or or angling where you could go
    23:11 1890s 1910s 1920s or
     
    All About Vaping: A Breath Of Fresh Air
    2021-05-23 (duration 30m)
    [transcript]
    21:15 valve or variable voltage?
    20:15 versatile generator or is it
    23:06 build a device differently or
     
    An Artists Life
    2021-05-16 (duration 42m)
    [transcript]
    19:52 a computer game or or or jigsaw
    27:25 Facebook or Instagram or the
    18:40 place, or for too long or
     
    Playing Chess: Queen Bashes Bishop
    2021-05-02 (duration 38m)
    [transcript]
    05:39 poems or problems,
    24:25 or Alexander Pavlov?
    32:04 or why don't
     
    All About Subbuteo: Flick My Ball
    2021-04-25 (duration 41m)
    [transcript]
    09:04 or half time or you know that
    09:55 over or something but
    01:57 sofa or something. But then
     
    Life of An Actor: Act Richard Act!
    2021-04-18 (duration 49m)
    [transcript]
    10:07 college three, three or four
    11:38 maybe, or something that you
    17:18 acted on that or was
     
    Riding Motorbikes: Show Us Your Helmet
    2021-04-11 (duration 45m)
    [transcript]
    10:03 particular school or training
    32:39 1885 1900 or 1915.
    08:31 Or something now I have
     
    Rock Band Interview: The Hazy Janes
    2021-04-04 (duration 40m)
    [transcript]
    14:47 the sixth or seventh or eighth
    06:37 Norwegian, or,
    27:32 or Dallas?
     
    Making The Most Of Your Life: Who Wants To Live Forever
    2021-03-28 (duration 48m)
    [transcript]
    17:32 Thanks for the world. Or
    30:44 just leave or, or just,
    41:59 yes or no? Or how in Russian
     
    Video Game Consoles: Hands Off My Joystick
    2021-03-21 (duration 39m)
    [transcript]
    04:41 or pounds.
    04:14 or something. Come on.
    09:32 computer stations or software
     
    Vintage Arcade Games: This Game Is Not Over
    2021-03-14 (duration 39m)
    [transcript]
    03:05 building society or bank,
    03:59 yes, reading a book or
    07:55 download them or not download
     
    Movie Nostalgia: Wayne & Trev's Excellent Adventure
    2021-03-07 (duration 40m)
    [transcript]
    11:46 or just
    16:31 Connection. Or for some reason
    38:28 Ghostbusters. Or you must read
     
    TV Nostalgia: Strike First, Strike Hard, Strike Nostalgia
    2021-02-28 (duration 39m)
    [transcript]
    15:23 animated or puppets or live
    32:23 or Erika eleniak.
    15:01 or Thunderbird. 2086. Yeah.
     
    UK Music Festivals - Ramblin Middle Aged Man
    2021-02-21 (duration 38m)
    [transcript]
    20:46 snoring or a buzzing sound or
    34:52 try and come home or or we stay
    27:09 filth. Joey jordison or Lars
     
    Midlife Music - Dancing In The Midlife
    2021-02-14 (duration 39m)
    [transcript]
    33:17 soccers or something.
    36:18 or linkin park?
    02:04 Eddie Cochran, Jerry Lewis, or
     
    Bearded Villains - Interview With A Villain
    2021-02-07 (duration 34m)
    [transcript]
    09:32 or wears or Jimmy or another
    26:31 window or something.
    09:28 length beard or something
     
    How To Grow A Beard - A Chin Full of Win
    2021-01-31 (duration 41m)
    [transcript]
    19:31 or female male or female. Yeah,
    33:03 or your wife or your partner
    34:08 boyfriends or their husbands
     
    Male Grooming - To Dye Or Not To Dye!
    2021-01-24 (duration 48m)
    [transcript]
    24:03 hair or you smell nice or
    25:46 nice, or they smell nice, or
    26:02 trainers or you've noticed
     
    Midlife Exercise Guide - Suns Out, Guns Out
    2021-01-17 (duration 41m)
    [transcript]
    07:01 shoulders or something like
    10:08 building muscle or wasn't
    27:09 overweight or anything, people
     
    Help Me Cool Older Dudes, You're My Only Hope
    2021-01-10 (duration 41m)
    [transcript]
    35:18 quid or something.
    03:02 another Transformers movie or
    03:02 another Hellboy movie or
  •  
     
  •  
    Unemployable Entrepreneur
    2021-04-10 (duration 17m)
    [transcript]
    02:52 or
    03:21 or
    04:05 or
  •  
     
  •  
    Living Fast, Dying Young or The 27 Club
    2021-01-13 (duration 45m)
    [transcript]
    00:05 or
    00:27 or
    00:59 or
     
    Rendlesham Forest UFO/UAP Incident
    2021-01-06 (duration 42m)
    [transcript]
    00:58 or
    01:07 or
    02:35 or
     
    Terror from the Woods
    2020-12-30 (duration 37m)
    [transcript]
    02:23 or
    06:00 Or
    06:53 or
     
    The Swimmers of Lake Baikal and USOs
    2020-12-23 (duration 45m)
    [transcript]
    01:16 or
    08:31 or
    08:37 or
     
     
    Yule Monsters and Good Saint Nick
    2020-12-16 (duration 55m)
    [transcript]
    03:18 Or
    04:20 or
    04:52 or
     
    The Pixie Behind the World's Best Known Tarot Deck
    2020-12-09 (duration 35m)
    [transcript]
    00:01 or
    00:55 or
    01:21 or
     
    Pen Pals With A Killer Clown
    2020-12-02 (duration 52m)
    [transcript]
    01:02 or
    01:15 or
    01:59 or
     
    Black Eyed Kids
    2020-11-25 (duration 39m)
    [transcript]
    05:54 or
    06:21 or
    06:45 or
     
    Death Is Optional
    2020-11-18 (duration 41m)
    [transcript]
    01:05 or
    01:17 or
    02:54 or
     
    The Fox Sisters and the Emergence of Spiritualism and Seances
    2020-11-11 (duration 56m)
    [transcript]
    01:45 or
    02:37 or
    02:39 or
     
    The True Story of the Exorcist
    2020-11-04 (duration 1h8m)
    [transcript]
    02:59 or
    03:10 or
    04:44 or
     
    The Human Monster of Halloween
    2020-10-31 (duration 30m)
    [transcript]
    01:30 or
    02:59 or
    03:37 Or
     
    The Mysterious Murder of Oakey "Al" Kite
    2020-10-28 (duration 40m)
    [transcript]
    01:25 or
    01:34 or
    02:25 Or
     
    Flatwoods Monster
    2020-10-27 (duration 45m)
    [transcript]
    01:17 or
    02:00 or
    07:06 or
     
    Mercy Brown and Black Aggie
    2020-10-27 (duration 55m)
    [transcript]
    01:04 or
    03:03 or
    04:29 or
  •  
     
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    United States: Voting during the Pandemic
    2020-12-20 (duration 31m)
    [transcript]
    17:08 right or wrong.
    05:34 Like you don't have to leave the country to get any perspective or, or learn something new.
    03:41 And like, new is not bad or scary.
     
    Bengaluru: Opening Up
    2020-12-13 (duration 23m)
    [transcript]
    07:55 So I can say that there's a small developing habit or a pastime.
    21:25 And it's about vaccinating that targeted group which needs or is suspect to transmit.
    03:03 And we wouldn't have taken up this topic or spoken about it at least for some foreseeable
     
    Athens: Waiting for Christmas
    2020-11-29 (duration 15m)
    [transcript]
    11:37 Really exciting, for these meat flavors, do you use Marmite or Vegemite?
    03:24 We won't have to pay any loans or any bills, they're suspended until March, so that's good.
    12:14 So like most of the non-vegans are coming like once or twice per week in our restaurants.
     
    Cape Town: Corona Aesthetics and Theater
    2020-11-15 (duration 34m)
    [transcript]
    17:42 or there's an interview with a top director or choreographer either from here or from elsewhere, or we'll put a webcam in
    29:07 There's this, sanitation is a problem in that particular country or there's a particular malaria outbreak or whatever.
    34:28 follow us on your favourite podcast provider or find out more about the podcast on Twitter, Instagram, YouTube, or our website, recordofochange.com.
     
    Istanbul: I just want this to be over
    2020-11-01 (duration 22m)
    [transcript]
    16:07 the world has offered has to offer to you rather than just hiring the next American writer or publishing the next American book set in Brooklyn.
    04:41 thinking, at least I have a lot to keep me busy and I didn't lose by jobs or lifeline or assignments.
    01:22 I thought this year was supposed to be just about survival and none of my pitches were or story ideas or projects were panning out.
     
     
    Bonus: Making a Pandemic Podcast
    2020-10-25 (duration 32m)
    [transcript]
    02:38 So you are a bit closer by profession by cultural background or location or so, and then there was this open call by the Bosch alumni network for online activities.
    03:59 Very briefly, because I think it's kind of unique or it's a bit
    06:38 Unfortunately my journalist friends were either too private or not confident in speaking English.
     
    United States: Graduating into a Pandemic
    2020-10-18 (duration 29m)
    [transcript]
    07:30 or look like or, um, be.' That moment, specifically, things sunk in a little bit.
    21:20 Maybe seeing people here or there.
    07:16 But for people who like live in California or they live in China or like in other parts
     
    Gaza City: Not the Last Crisis
    2020-10-11 (duration 29m)
    [transcript]
    22:24 Those who do have studies abroad or do or have, some kind of trainings or, obligated with work contracts abroad.
    15:13 Do you remember any special moments of surprise or, or, for me personally, when I look back to the last four or five months I can see a lot of ups and downs.
    22:32 So, I'm wondering if you follow like news or anything from, let me say the global North, or for example, Europe.
     
    Bengaluru: Dog Days, Cat Days
    2020-10-04 (duration 28m)
    [transcript]
    17:01 too, or even have a one night stand or sex with others so that we don't frustrate and stay in isolation.
    14:59 So I can talk or be myself or be free and do whatever I want to because you're living under their roof.
    27:32 I'll reach out to you in a month or so.
     
    Cape Town: Still standing home
    2020-09-27 (duration 31m)
    [transcript]
    29:40 need to be engaged and you're coughing, or we're thinking that it's contributing somehow to a better world.
    18:17 Stand up comic or a really famous actor or director or whatever, and say to them, can you donate 10,000 Rand?
    26:14 that everybody knows people very close to them who have either died or are in the process of dying or whatever.
     
    Athens: Go vegan, go local
    2020-09-20 (duration 28m)
    [transcript]
    16:56 For me, I didn't use Instagram or Facebook.
    15:15 What was this an app or was it just SMS?
    21:01 But has the situation improved now or is it still…?
     
    Manila: Cabin Crew Barista
    2020-09-13 (duration 36m)
    [transcript]
    15:18 Or would you like to, explore or do some things?"
    26:23 Are your families or your loved ones or your relatives?
    22:39 Or the feeling so strange.
     
    Hong Kong: New Life, Postponed
    2020-09-13 (duration 28m)
    [transcript]
    09:10 after a week or so
    27:24 Are you really doing the, working towards what you value or you're working towards your goal or just letting life getting by?
    00:45 Did you hear that in January or even before that?
     
    Istanbul: Survival Mode
    2020-09-13 (duration 30m)
    [transcript]
    25:09 contacts are laid off or gone or they are having an election coming up this year, fingers crossed.
    17:07 So we just had a big religious festival Eid, Kurban Bayramı and in the Christian world, this would be the equivalent of Christmas or Easter.
    04:38 and suddenly when everyone was talking about the massages and or the weather or the food that they were eating to, suddenly,
     
    Trailer
    2020-09-05 (duration 2m)
    [transcript]
    02:00 Subscribe to Record of Change on your favourite podcast app, and find out more about the stories on Twitter, Instagram, YouTube or our website, recordofchange.com.
    00:45 Athens, Istanbul, Gaza, Hong Kong, Cape Town, Manila and other places around the world.
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    Learning assertiveness from Germans, and growing to love a city (Jenna from Canada)
    2020-12-14 (duration 41m)
    [transcript]
    20:04 friends or my family or
    25:12 or perhaps the UK or wherever in
    34:19 or take it to the beach or
     
    Coming to Germany during a pandemic (Cassie from Australia)
    2020-12-07 (duration 36m)
    [transcript]
    30:54 Christmas or something like
    02:29 build friendships or learn from
    07:15 either Goethe or Schiller before
     
    Singing soul in Germany, and BLM from a distance (Alicia from the USA)
    2020-11-30 (duration 47m)
    [transcript]
    26:01 tomorrow, or, you know, or
    01:17 podcasters face off, or
    06:34 professional musician? Or is
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    Episode 6
    2020-11-01 (duration 35m)
    [transcript]
    01:08 or
    04:09 or
    06:13 or
     
    Episode 5
    2020-10-11 (duration 33m)
    [transcript]
    06:16 or
    08:49 or
    10:14 or
     
    Episode 4
    2020-09-20 (duration 26m)
    [transcript]
    01:56 or
    02:42 or
    05:16 or
     
    Episode 3
    2020-08-30 (duration 38m)
    [transcript]
    01:35 or
    02:07 or
    02:36 or
     
    Episode 2
    2020-08-09 (duration 36m)
    [transcript]
    01:01 or
    01:36 or
    01:56 or
     
     
    Pilot
    2020-07-19 (duration 32m)
    [transcript]
    01:23 or,
    05:00 or
    10:53 or
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    Leading Your Church Through a Leadership Transition
    2020-10-01 (duration 28m)
    [transcript]
    26:16 Or maybe something a little more tangible?
    04:49 Good. So you serve a large church and our historic church and our church body. Get maybe as a as a word of advice to people who are either desire to serve others. large church or are serving one, what advice would you kind of, or insights you can give to them that would help them in that ministry.
    24:46 Well, yeah, one thing that comes to mind is I see a tendency of this in myself, and I especially aware of it because I've seen it in others. Otherwise I think it probably would have snuck up on me or sneaked up on me. And, and that really is, it's so easy, I believe, especially when we were coming towards the end of our ministry to, to have a sense of entitlement and that, you know, I've served the Lord faithfully. I've served this congregation for however many years. And so now I'm kind of old or something. And maybe, maybe I'm, I'm old, a little more relaxed pace of ministry for the last however many months or, or years or, or I don't know what but the, it's, I think it's very easy to lose sight of the fact that I got into ministry to serve. And for that, to kind of flip around to, I've served and I've served faithfully, and well, some of the time anyway. And now to be served a little bit sounds pretty good. And I think, of course, we would never, you know, intentionally, and consciously have those kinds of thoughts. But I think that's something just to be aware of, and on the lookout for.
     
    How Does The Black Lives Matter Movement Differ from The Dr. King, Jr. Led Civil Rights Movement
    2020-09-29 (duration 38m)
    [transcript]
    15:07 So you know, one of the thing I want to kind of cover before we get into kind of what's happening today in our world is those first group of black pastors came out, did not have or received the same benefits, and even salaries as other passions. When I was sharing this with other people. They were shocked that there was a different tier of compensation. Can you cover that?
    27:24 Right? If you if you if you could address qualified immunity, you know, if you could simply address community policing, you could simply address how do you how do you deal with these urban inner city communities, you could probably get further down the road. But what I see often times is basically people on the street, yelling and screaming at each other, or at least coming in ride gear. Now you got you got going back and forth at each other, all of a sudden, now you got an escalation of violence, and then pick buildings and burn down and all of that stuff. I mean, among the civil rights movement, itself, apart from the rioting in the, in the cities, when Dr. King was killing some of the other riots, usually the marches of the Civil Rights Movement never saw in the buildings being burned down. Right.
    00:03 Welcome to this edition of the light breakthrough. I am your host Keith Haney. it is conceivable the systems you are operating under and coach are crushing you, and you need consolation. In this season and time you may be seeking inspiration. The goal of this podcast is to give you inspiration, practical solutions and challenging conversations with a wide variety of guests and relevant topics. If you're not engaged a local church, I pray this podcast will encourage you to seek out a deeper connection with your Lord and Savior Jesus Christ. The world is changing our ministry methods, not our beliefs, you to reflect that. This may stretch you beyond your comfort zone, but you will never lose sight of who sits on the throne. So sit back, put on your seatbelt and get ready for transformation. today's podcast is a conversation about how we're seeing today is different from the civil rights movement. Dr. Martin Luther King Jr. civil rights are defined as a non political rights of citizens, especially those guaranteed to US citizenship by the 13th and 14th Amendments of the Constitution, and by acts of Congress, according to Webster dictionary, the 13th Amendment of the Constitution, abolish slavery in the US and the 14th amendment issued ensure that African Americans have their legal citizenship and equal protection under the law. The National Archives experience puts it that way. Movement is defined in part as a series of organized activities, working toward objective or an organized effort to promote or attain in. The Civil Rights Movement was an era dedicated to activism with equal rights and treatment of African Americans the United States. During this period, people rally for social, legal, political, and cultural changes to prohibit discrimination and in segregation. My guest today Reverend Roosevelt gray is a director of LCMS black ministry, a long established Missouri senate ministry serving predominantly black communities and ministering to African American immigrants. The Reverend Roosevelt gray Jr, provides a leadership and direction for the LCMS districts, congregations schools, and related organizations as they administered to minority groups in their communities across the country. Also, Dr. Gray is will serve as a liaison to the church wide black clergy caucus and oversee the development and resources to support LC Ms. Black ministry throughout the Senate. Prior to joining the staff and LC ms International Center in St. Louis, Dr. Gray served as Assistant to the President for missions and evangelism in the LC ms ms. Michigan district. He called accepted in 2001. He served as a pastor at faith Lutheran Church and listen to diminishes in Detroit, from 1971 to 2001, Director of Admissions and recruitment and vicars at Concordia Theological Seminary in Fort Wayne, Indiana, from 1994 to 1997. And as a pastor at Mount Calvary Lutheran Church in Houston, Texas, from 1998 to 1994. He graduated as an eight the master of divinity degree from Korea technical seminary 9970 got a bachelor's degree in printing production and management from Alabama a&m. rival to sell University have mentioned that Dr. Gray receives a doctorate a divinity degree from Concordia seminary St. Louis. It was dated 19 July June 19 1998, at St. Paul's a church in Jacksonville, Florida. His home congregation installed him on June 26 1998, as pastor of Calvary in Houston, Texas, and he's married to later, Vanessa. We're so great to have you. Thank you, Dr. Ray, for joining us on this important discussion. Wonderful being here with us today. So let me start out with an easy question. I like to get my my guests up and kind of warm up. So what's the best advice anybody ever gave you about race?
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    Exploring The TileDB Universal Data Engine
    2020-08-17 (duration 1h5m)
    [transcript]
    54:07 in terms of your own experience of building and growing the project and the business around tile dB, what are some of the most interesting or unexpected or challenging lessons that you've learned in the process?
    52:33 And as far as people who are using title DB to build applications on top of it, what have you found to be some of the most interesting or unexpected or innovative ways that it's being used?
    20:11 So can you dig a bit more into the actual on disk format of the multi dimensional arrays and how they're stored by title DB for being able to then query and analyze them. And just some of the ways that users of tile DB need to think about data modeling that might be different than the ways that they're used to using either relational structures or graph databases or some of the custom file formats that they might be coming from.
     
    Build More Reliable Distributed Systems By Breaking Them With Jepsen
    2020-07-28 (duration 49m)
    [transcript]
    45:04 as you continue to work on Jepson, I'm curious what you have planned for the future of that project or other projects or research in terms of distributed systems that you're looking forward to either continue or start a new.
    39:40 as you have been working on building Jepson and evaluating some of these mission critical systems. What have you found to be some of the most interesting or unexpected or challenging lessons that you've learned or outcomes of the work that you've done?
    06:46 and then as far as some of the real world usage of things like databases and other mission critical distributed systems, what are some of the real world impacts that can occur as a result of these failures that aren't properly vetted or properly guarded against,
     
    Open Source Production Grade Data Integration With Meltano
    2020-07-13
    [transcript]
    1:02:46 this together. Well, for anybody who wants to get in touch with you or follow along with the work that you're doing or contribute to the project. I'll have you add your preferred contact information to the shownotes. And as a final question, I would just like to get your perspective on what you see as being the biggest gap For the tooling or technology that's available for data management today.
    1:01:40 of your work on Mel Tano, or the overall space of data integration or some of the challenges in an end to end tool for managing the data lifecycle that we didn't discuss that you'd like to cover before we close out the show?
    55:20 And in your experience of taking over this team and working with Mel Tano, and helping to understand the direction to take and actually building out the platform, what have you found to be some of the most interesting or unexpected or challenging lessons that you've learned in the process?
     
    DataOps For Streaming Systems With Lenses.io
    2020-07-06 (duration 45m)
    [transcript]
    32:36 And so in terms of the lenses platform itself, what are some of the most interesting or unexpected or innovative ways that it's being used or insights that you've seen people gain from it as they have adopted it for their streaming architectures?
    38:39 for both in your own work on the lenses platform and using it within your own systems. What have you found to be some of the most interesting or unexpected or challenging lessons that you've learned in the process?
    42:50 Are there any other aspects of the lenses platform itself or data ops principles in general are some of the ways that people are using lenses or fits into the overall workflow of building streaming applications that we didn't discuss that you'd like to cover before we close out the show.
     
    Data Collection And Management For Teaching Machines To Hear At Audio Analytic
    2020-06-30 (duration 57m)
    [transcript]
    41:54 not going to be acceptable to their customers. And in terms of the audio that you're working with. What about Some of the most interesting or unusual or strange sounds that you've had to try and collect and categorize.
    07:07 you know, a coffee shop and sort of whether it's a physical scene in that case, or whether it's an acoustic scene, so whether it sounds calm, whether it sounds lively or not, or indeed, whether it sounds like it's inside or outside, those would be examples of acoustic and physical scene detection itself. And both of those sit under what's called sound recognition, which is the field in which the company leads. And it seems that at least the majority of the use cases that you're discussing now are more consumer oriented for people to be able to take advantage of some of this intelligence to enhance their sense of well being or get some sort of feedback about their environment. I'm wondering if you've also experimented at all with use in industrial contexts where particular types of sound might be indicative of some type of imminent failure in terms of structural or issues with manufacture Or, you know, maybe in mining where certain sounds might be indicators of some type of physical risk. I'm wondering if that's something that you've looked at at all or something that you're intending to branch out into
    49:03 And so as you continue to build out the technical and business capacity for the company, what are some of the plans that you have for the future either in terms of new problem spaces or use cases that you're looking to reach into or enhancements to your existing processes of data collection and machine learning?
     
     
    Bringing Business Analytics To End Users With GoodData
    2020-06-23 (duration 52m)
    [transcript]
    46:20 And what do you have on the roadmap for the future of good data in terms of new capabilities, or just overall improvements or new use cases that you're looking to provide?
    40:44 And in your experience of building the platform and working on it yourselves and working with your customers to ensure that they're having successful outcomes. What are some of the most interesting or unexpected or challenging lessons that you've learned in the process,
    29:02 Absolutely. So what you're referring to is what pieces of the good data architecture what the client wants to leverage. So we're talking about the data warehousing piece that Phil was talking about with ADF, where we could potentially get the aggregation of lots of different data sources all in one place. Whether or not that's something that the client wants to leverage from the good data side or own on their side. There's also the loading mechanism, the ATP piece, where we're talking about how the client would be able to load that data, whether or not they want to keep it on their side or actually keep it on the good data side. So the ways we're able to manage that is really the flexibility of the types of sources. We're able to download from whether or not we are doing the transformations or just loading directly into the platform. So with all the connectors that we have with these pre packaged Ruby bricks that are leveraging the good data API's as well as the source API's, were able to integrate their data and load into ADF through those connectors, or if the client wants to own, a lot of the transformations themselves, match the exact metadata output for the semantic layer or the models that are on the workspaces. They're able to load that directly in with their data warehousing source through our automated data distribution or add, especially if they're using things like snowflake, redshift, or BigQuery.
     
    Accelerate Your Machine Learning With The StreamSQL Feature Store
    2020-06-15 (duration 46m)
    [transcript]
    41:08 And then for people who are working on providing data to machine learning teams or working as a machine learning engineer or a data scientist, what are the cases where either using a feature store in general or stream sequel in particular is the wrong choice?
    39:45 And as far as your own experiences, what have been some of the most interesting or challenging or unexpected lessons that you've learned in the process of building stream sequel?
    37:47 and what have you found to be the most challenging or complex aspects of working on or with a feature store as you build out the capabilities of stream sequel and use it for your own work for personal use and Triton
     
    Data Management Trends From An Investor Perspective
    2020-06-08 (duration 54m)
    [transcript]
    02:42 Yeah. Do you remember how you first got involved in the area of data management or working with data companies?
    21:35 And another element that ties into the overall data quality question is the idea of discoverability of the information and being able to track its origin and its lineage to ensure that the processes that are being run on it aren't aligning important information or introducing inaccuracies or older bias data. And that is a big portion of what's covered in the overall concept of data catalogs or metadata management. And I'm wondering what you're seeing as being the main challenges that businesses face in establishing and maintaining those data catalogs and being able to have robust mechanisms for managing all that metadata.
    05:36 From your perspective, as an investor and somebody who's working with these companies, what is it about the overall category of data oriented businesses that you find appealing or attractive?
     
    Building A Data Lake For The Database Administrator At Upsolver
    2020-06-02 (duration 56m)
    [transcript]
    42:00 it. And what are some of the features of your platform or capabilities or ways of using it that are either often overlooked or underutilized by your customers that you think they would benefit from using more frequently?
    08:56 and how do the clouds data warehouses such as snowflake or BigQuery differ from the full fledged data lake in terms of what they're available to offer, and maybe some of the cost issues or performance capabilities compared to using the native data lake technologies, whether that's things like presto and spark or managed platform,
    50:15 of the work that you've done on up solver or using SQL as the interface for data lakes, or just overall data lake technologies and usage that we didn't discuss they'd like to cover before we close out the show?
     
    Mapping The Customer Journey For B2B Companies At Dreamdata
    2020-05-26 (duration 46m)
    [transcript]
    43:57 And are there any other areas of the world work that you're doing at dream data, or the overall space of b2b sales and revenue tracking or any of the other challenges that you're facing in the data landscape that we didn't discuss, they'd like to cover before we close out the show.
    39:19 And in terms of your experience of building and growing both the technical and business elements of dream data, what have you found to be some of the most challenging or interesting or unexpected lessons that you've learned?
    40:59 One of the pieces that you were mentioning before too about the content being one of the strongest drivers of revenue in a particular case, I'm wondering how the overall evolution of the marketing landscape and different types of media or content distribution, how that impacts your overall approach to building out your platform, as well as some of the ways that you're approaching trying to grow your own business or what you found to be some of the most useful mediums or channels for being able to grow revenue or grow the audience.
     
    Power Up Your PostgreSQL Analytics With Swarm64
    2020-05-18 (duration 52m)
    [transcript]
    42:35 in terms of your experience of building this product and growing the business around it. What are some of the most interesting or unexpected or challenging lessons that you've learned?
    44:33 And one of the swarm 60 for the wrong choice. And somebody might be better suited either just using vanilla, Postgres or some of the other plugins in the ecosystem or migrating to a different set of database technologies.
    48:47 Are there any other aspects of the work that you're doing at swarm 64, or the Postgres ecosystem or some of the analytical use cases that we've highlighted that we didn't discuss that you'd like to cover before we close out the show?
     
    StreamNative Brings Streaming Data To The Cloud Native Landscape With Pulsar
    2020-05-11
    [transcript]
    43:12 architecture, wondering what you have seen as being some of the most interesting or innovative or unexpected ways that you've seen pulse are used and the applications of streaming data.
    15:23 Am I talking about the kind of streaming capability that influenced the whole deca functionality development or pasa?
    47:07 And for people who are evaluating pulser or considering it as a component of their architectures, what are the cases where a pulsar is the wrong choice and they might be better served with either an entirely different approach or a different set of tooling.
     
    Enterprise Data Operations And Orchestration At Infoworks
    2020-05-04 (duration 45m)
    [transcript]
    42:47 And are there any new features that you have planned for the near to medium term or overall improvements or enhancements to the platform that you'd like to share before we close out the show?
    39:03 And in your experience of building and growing info works as a business and as a technical platform, what are some of the most challenging or interesting or unexpected lessons that you've learned in the process?
    08:12 And the big data technologies that we have now are generally fairly built for purpose, either by the original organization that used it and then open sourced it or by the academic institution that was using it for a particular area of research, which can lead to some sharp edges or difficulties and integrating it into the larger ecosystem. But from your perspective, what have you found to be some of the design or technical limitations of those existing technologies? And how does that contribute to the overall difficulty of using or integrating them effectively into an enterprise organization? Yeah,
     
    Building Real Time Applications On Streaming Data With Eventador
    2020-04-20 (duration 50m)
    [transcript]
    41:43 And what are some of the most interesting or innovative or unexpected ways that you've seen your customers using your platform and building on top of these boundless event streams?
    42:56 and in terms of your experience of building and scaling the event to To our platform and just working in the data management space, what are some of the most interesting or unexpected or challenging lessons that you've learned in the process?
    38:57 see it heading and is developed uppers are working on building out these streaming workloads and building applications on top of them and trying to schematize the input data, what are some of the sharp edges or design pitfalls or data modeling considerations that they should be aware of?
     
    Making Data Collection In Your Code Easy With Rookout
    2020-04-14 (duration 26m)
    [transcript]
    21:59 and are there Any other aspects of the process of collecting these metrics and information from the software that we're running, or the value that can be obtained from the information that's hiding in those systems, or the overall process of leveraging dark data in an organization that we didn't discuss yet that you'd like to cover before we close out the show or any other aspects of the work that you're doing a workout?
    18:58 and in your experience, so building out this platform for being able to do more ad hoc data collection and bring more people into the process of defining these collection points, what are some of the more interesting or unexpected or challenging lessons that you've learned in the process?
    23:36 All right. So for anybody who wants to follow along with the work that you're doing the Ron or get in touch, I'll have you add your preferred contact information to the show notes. And as a final question, I would just like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today.
     
    Building A Knowledge Graph Of Commercial Real Estate At Cherre
    2020-04-07 (duration 45m)
    [transcript]
    41:09 Are there any other aspects of knowledge graphs in particular, or the work that you're doing at cherry or the challenges that you're facing that we didn't discuss that you'd like to cover? Before we close out the show,
    36:32 And for anybody who is interested in building a knowledge graph of their own, or in the early phases of that process, what are some of the pieces of advice that you have or any useful references that you can point them to?
    34:18 Looking to the near and medium term, what are some of the improvements or enhancements that you have planned to the actual content of the Knowledge Graph itself, or the pipeline and tooling that you have to be able to build and power the graph.
     
    The Life Of A Non-Profit Data Professional
    2020-03-31 (duration 44m)
    [transcript]
    42:02 Well, for anybody who wants to follow along with you, or get in touch or offer their help, I'll have you add your preferred contact information to the show notes. And as a final question, I would just like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today.
    40:59 Are there any Other aspects of your work at the NRDC or the tech workers Task Force, or your just overall experience of working in nonprofits as a data professional that we didn't discuss that you'd like to cover? Before we close out the show, I think
    38:00 and going back out to the point of tools and platforms that exist that are available off the shelf, either in terms of open source where you can build to fit or on hosted platforms, what have you found to be some of the most useful or beneficial in the current landscape of data management systems and best practices? And what are the areas that you feel need to be addressed or improved, particularly for workers in the nonprofit sector?
     
    Behind The Scenes Of The Linode Object Storage Service
    2020-03-23 (duration 35m)
    [transcript]
    21:43 right. I mean, in some ways, if you replicate the data across the world, you are creating a CDN, or at least most of it
    31:17 and other any other aspects of object storage or your work on the linode product or anything about your experiences of getting it deployed that we didn't discuss the you'd like to cover before we close out the show. Um,
    30:15 all right. And so as far as your experience of building out this object storage platform and releasing it publicly, what have you found to be some of the most interesting or unexpected or challenging lessons that you've learned in the process?
     
    Building A New Foundation For CouchDB
    2020-03-17 (duration 55m)
    [transcript]
    10:01 Yeah, definitely. Yeah, the CR DT or conflict free replication, or
    44:18 And what are some of the most interesting or unexpected or innovative ways that you've seen couchdb used?
    51:19 Are there any other aspects of the couchdb project and its ecosystem and community or foundation dB, or the work that you're doing to re platform onto the foundation DB engine that we didn't discuss that you'd like to cover? Before we close out the show?
     
    Scaling Data Governance For Global Businesses With A Data Hub Architecture
    2020-03-09 (duration 54m)
    [transcript]
    49:57 and are there any other aspects of the data architecture or some of the ways that it's being used or the benefits that it provides that we didn't discuss yet do you think we should cover before we close out the show? I think
    26:22 And then another issue, particularly if you have a deeply layered topology is how you handle the transformations between hubs where they have different rules in terms of how the records should be represented or data quality or cleanliness issues, and being able to handle issues of potential data loss across those different nodes.
    51:14 Well, for anybody who wants to follow along with the work that you're doing or get in touch, I'll have you add your preferred contact information to the show notes. And as a final question, I would just like to get your perspective on what you see as being the biggest gap in the tooling or technology for data management today.
     
    Easier Stream Processing On Kafka With ksqlDB
    2020-03-02 (duration 43m)
    [transcript]
    38:14 And what are some of the most interesting or unexpected or challenging lessons that you've learned in the process of working with and on k SQL DB?
    37:38 In terms of the use cases, and usages of K SQL DB, what are some of the most interesting or unexpected or innovative projects that you've seen people build with it?
    36:35 when his case equal dB, the wrong choice, and somebody would be better suited going with just a traditional relational database or a data warehouse or some other type of streaming platform.
     
    Shining A Light on Shadow IT In Data And Analytics
    2020-02-25 (duration 46m)
    [transcript]
    25:49 Or you can't afford to live anywhere there. Right. So, yes,
    20:18 aspect of this is that as we mentioned before the term shadow IT can have this negative connotation, and it can lead to people trying to hide their activities from the central it or just from the organization at large so that they don't get called out on embarking on some maybe unapproved project or incorporating some technology that hasn't been vetted by the powers that be. And so I'm wondering what are some of the ways that we can try and either eliminate that stigma so that people are more willing to be upfront about the fact that, hey, I tried this thing, it's having this useful outcome and then being able to then in incorporate that into the rest of the organization or popularize it or add a way for them to integrate the work that they've been doing into the data sources or data processing systems that are being used throughout the organization.
    29:23 And then shifting gears a bit. We mentioned at the outset that some of the reason that shadow IT projects, particularly in the data and analytics space are starting to become a bit more prevalent is because of the availability of these different cloud tools or, you know, one click provision applications or easy to use databases. So I'm wondering what types of tools or platforms in particular are well suited for being provisioned by people who don't necessarily work in a primarily engineering role or for people who are not necessarily Looking for a end to end integrated solution, they just want something that they can start using in conjunction with existing tools. And some of the potential pitfalls that exist as a result of these tools being so easy to use, and maybe the people who are initially setting them up not having the context or training necessary to be able to foresee some of those potential problems.
     
    Data Infrastructure Automation For Private SaaS At Snowplow
    2020-02-18 (duration 49m)
    [transcript]
    34:04 And in terms of your experience of building out this automation and managing this platform, what are some of the most interesting or unexpected or challenging lessons that you've learned in the process?
    40:04 And if you were to start over today with all of snowplow and the infrastructure automation that you're using for it, what are some of the things that you would do differently or ways that you would change some of the evolution of either the snowplow pipeline itself or the way that you've approached the infrastructure management?
    19:31 And so in the overall system, which components are the ones that are most subject to variability in traffic or resource pressure, and what are some of the strategies that you use to ensure proper capacity as there might be burstiness and the events that are being ingested or being able to meet some of those latency SLA is that you mentioned so
     
    Data Modeling That Evolves With Your Business Using Data Vault
    2020-02-09 (duration 1h6m)
    [transcript]
    1:01:18 So are there any other aspects of the data vault methodology or data warehouse modeling or anything tangential to what we talked about today that you think we should discuss further.
    56:28 And so, in terms of the foundational skills and experience and knowledge that are necessary for effective data modeling, what have you found to be some of the core elements and for listeners who want to learn more about data modeling in general or data vault specifically what are some of the references or exercises that you recommend? Sure, so I mean,
    50:34 So it's definitely easy to as we talked about this, start thinking that data vault is the solution to all of my problems in terms of being able to handle modeling and accessing and storing all of my data in a very agile fashion to get quick time to value. But what are some of the cases where the data vault approach doesn't really fit the needs of an organization or a use case or it's unnecessarily cumbersome the Because of the size and maturity of the data or the institution that's trying to implement it.
     
    The Benefits And Challenges Of Building A Data Trust
    2020-02-03 (duration 56m)
    [transcript]
    16:35 And for an existing data trust that's already been established. Have you found that there are general approaches to how an individual or an organization might gain access to be either a member of the trust or be able to have some limited access to the data contained there and to be able to do some sort of analysis or build additional products on top of it?
    42:49 And in terms of the types of trust that you've worked with and some of the outcomes of the I'm curious what you have seen as being the most interesting or innovative or inspirational ways that You have seen the bright hive platform used as well as this broader concept of data trust being leveraged,
    13:30 And one of the things that you mentioned there that I'm interested in digging more into is this idea of the ownership of the derivative data sets or aggregate information about the different entities contained within the data owned by the different members of the trust and some of the complications that arise in terms of where the intellectual property would lie as far as any algorithms or derivative data products that come out of the information that's available in this trust.
     
    Pay Down Technical Debt In Your Data Pipeline With Great Expectations
    2020-01-27 (duration 46m)
    [transcript]
    35:24 And I know that we've spoken a bit about some of the interesting or innovative or unexpected ways that Great Expectations is being used within these different contexts of communication and execution. But I'm wondering if there any other areas that or any other interesting examples that we didn't touch on that you think are worth calling out?
    44:02 all right? Well, for anybody who wants to follow along with the work that you're doing or get involved in the Great Expectations project or just get in touch with you, I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today.
    41:14 And one of the other things that I think is interesting to briefly touch on is the types of data that are usable with great expectations where a lot of times people are going to be defaulting to things that are either in a SQL database or a textual or numeric data. But then there are also potential for things like binary data, or images or videos. And I'm wondering what are some of the ways that Great Expectations works well, with those are some of the limitations to think about what types of data sets are viable for this overall approach to testing?
     
    Replatforming Production Dataflows
    2020-01-20 (duration 39m)
    [transcript]
    20:54 And Sheila, I'm curious if there were any other edge cases that you ran into as you were migrating on to it. And that were easy in that stream based approach, but became either difficult or impractical, or you just needed to think about a different way of approaching it in this declarative model.
    31:46 And are there any other aspects of your experience of migrating the overall data platform either anything specifically about ascend or the process of identifying new platforms or re architecting your systems or from years Side shot, anything that we didn't discuss from your end of bringing Maven on board and working with them to identify the optimal way to take advantage of your platform. any of that, that we didn't discuss yet, either you'd like to cover before we close out the show?
    36:06 And Sean, do you have anything to add as far as your perspective on the biggest gap that you see in the tooling or technology that's available for data management today?
     
    Planet Scale SQL For The New Generation Of Applications
    2020-01-13 (duration 1h1m)
    [transcript]
    57:08 And are there any other aspects of yoga by DB or your position in the overall landscape of data management or any of the other aspects of your business or your work on the platform that we didn't discuss yet that you'd like to cover? Before we close out the show?
    06:44 Yeah, absolutely. And back then you don't realize it right. Like when, like, we were putting this thing together or, like, I mean, open source wasn't that popular back then databases? Definitely not. There was no nothing called NO SEQUEL back then. So it was it was a lot of interesting twists. sentence that the world went through. And it's been pretty rapid, right? The most equals now such a staple, staple thing. But back then it wasn't even a term.
    57:25 Yeah, I'm sure that there are a number of different sub elements that we could probably spend a whole other episode talking about in great detail, but I think we've done a good job of the overview. So for anybody who does want to follow along with the work that you're doing or get in touch, I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today. Ah,
     
    Change Data Capture For All Of Your Databases With Debezium
    2020-01-06 (duration 53m)
    [transcript]
    44:23 And what are some of the most interesting or unexpected or innovative ways that you've seen DBZ? amused?
    42:14 that every enterprise has, from time to time. And I think it's interesting that you call that the sort of close connection between the design of DBZ and its initial build target of Kafka, and I'm curious if you have explored or what the sort of level of support is for other streaming back end, such as pasar, or per Vega, or if you've looked at other any other architectures or sort of deployment substrates for the DBZ and project itself.
    09:08 And so being able to work across these different systems is definitely valuable. I'm curious if you've seen cases where people are blending events from different data stores to either populate a secondary or tertiary data store or be able to provide some sort of unified logic in terms of the types of events that are coming out of those different systems. Or if you think that the general case is that people are handling those change sets as their own distinct streams for separate purposes.
     
    Building The DataDog Platform For Processing Timeseries Data At Massive Scale
    2019-12-30 (duration 45m)
    [transcript]
    30:59 you've been there for A few years now, I'm curious what you are most proud of, or what have been some of the most interesting projects that you have been engaged with. And out of those any of the lessons that you have found to be particularly valuable or unexpected or just interesting issues that you've had to confront? Oh,
    38:52 Are there any other aspects of your work at data dog or the types of projects that you're building or the platform in general that we didn't discuss yet? You'd like to cover before we close out the show.
    36:25 as you look forward to some of the projects that you've got planned for some of the coming months and years, I'm curious, what are some of the types of technologies or best practices or overall patterns and systems designs that you're trying to keep an eye on or that you're hoping to adopt? And just some of the overall types of challenges that you're anticipating as you move forward?
     
    Building The Materialize Engine For Interactive Streaming Analytics In SQL
    2019-12-23 (duration 48m)
    [transcript]
    42:17 are there any other aspects of your work on materialize or the underlying libraries or the overall space of being able to build a real time analytics engine on streaming data that we didn't discuss yet that you'd like to cover before we close out the show?
    08:06 And in terms of how it fits into the overall life cycle and workflow of data, wondering if you can just give an overview of maybe a typical architecture as to where the data is coming from how it gets loaded into materialize and sort of where it sits on the axis of the sort of transactional workload where it's going into the database to the analytical workload where it may be in a data lake or a data warehouse or any of the other sort of surrounding ecosystem that it might tie into or feed the materialized platform.
    42:11 that it doesn't exactly do what they need. Well, we probably should have fixed that beforehand. So we're doing a bit of that the next next month or two,
     
    Solving Data Lineage Tracking And Data Discovery At WeWork
    2019-12-16 (duration 1h1m)
    [transcript]
    49:53 And what are some of the interesting or unexpected or challenging aspects of building a Maintaining the Marquez project that you have learned in the process of going through it.
    20:31 yeah, being able to identify some of the downstream consumers that are going to be impacted by a job changes I can see as being very valuable because it might inform whether or not you actually want to push that job to production now, or maybe wait until somebody else is done using a particular version of a data set, or at least as you said, having that visibility into what are all the potential impacts. Whereas if you're just focusing on the one job, it can be easy to ignore the fact that there are downstream consumers of the data that you're dealing with and then terms of the inputs to Marquez, we've been talking a lot about some of the sort of discrete jobs and batch oriented workflows. But I'm curious to if there is any capability for being able to record metadata for things like streaming event pipelines, where you have a continuous flow of data into a data lake or a given table, or that might be fed into a batch job that's maybe doing some sort of windowing functions and how the breakdown falls as far as batch versus streaming workloads.
    43:39 So we have some tagging features and in can be used to leverage to you know, to implement privacy or security aspect or encoding SLA s, right. He's my data experimental is my data production, really, those kind of aspects that people can use it for? other aspect is adding data quality metrics in the data set. So we've been experimenting with great expectations to do this. And you then people can decide. Usually it's it's using two ways whether when you're producing the data, and just having some declarative properties and force in your data set and fail, you know, you don't want to let anybody see that data set, if it's the code may run and not declare any errors, but the result is not correct. And so that can be used as a, you know, circuit breaker to not start the downstream jobs and never not publish these data set. All the ways people use it is actually the consumers may have different opinions of what the data quality should be for them to run their job. So they can also use as a pre validation check, like enforcing certain data quality metrics, before consuming a job in preventing you Bad data to percolate through the system, right? Because then it can be expensive or of impacting production, especially if you're doing machine learning or recommendation engine or things like that. If you have beta, bad data going in, then you have bad recommendation coming out, right. And that's has a real impact on the production systems. So those are some of the ways people are using it. So there are always two aspects. Either you have a more January January tagging or flexible type of metadata adding to an existing entity, or if it's something that can benefit that's from being including in the core model, then it can become like an actual attribute or an entity in the model.
     
    SnowflakeDB: The Data Warehouse Built For The Cloud
    2019-12-09 (duration 58m)
    [transcript]
    54:28 Are there any other aspects of the snowflake platform or the ways that it's being used or the use cases that it enables that we didn't discuss yet that you'd like to cover before we close out the show?
    50:12 And what are some of the plans for the future of snowflake DB either from the technical or business side?
    33:10 And then in terms of the overall system architecture and the implementation details, I'm wondering if there are any sort of edge cases or limitations that you're dealing with or any of the specific challenges for being able to design and implement this across multiple different cloud vendors?
     
    Organizing And Empowering Data Engineers At Citadel
    2019-12-03 (duration 45m)
    [transcript]
    36:57 And are there any tools or practices or industry trends that you're keeping an eye on that you're excited to try and incorporate into your workflow?
    38:10 And are there any other aspects of your work at Citadel or the challenges that you're facing or the ways that you're using data that we didn't discuss yet that you'd like to cover before we close out the show?
    34:54 And as you continue to evolve the capabilities requirements of the data organization at Citadel what are some of the challenges, whether technical or business oriented or team oriented that you are facing and that you're interested in tackling in the coming weeks and months?
     
    Building A Real Time Event Data Warehouse For Sentry
    2019-11-26 (duration 1h1m)
    [transcript]
    51:00 Are there any other upcoming new projects or any other major data challenges that you're facing at century that are either causing enough pain that you need to do a major refactor or anything that is forward looking that you're able to spend time and focus on now that you freed yourself from the burden of trying to maintain these multiple different systems? And they're lagging consistency?
    37:55 Yeah, that's a that's a good question. So I think I think this entire process from start to like being at 100% live probably took roughly a year, give a give or take.
    48:20 And the snoozefest system itself, it is an open source project. Is it something that is potentially useful outside of the context of century where somebody might be able to adapt it to different search implementations? Or is it something that's fairly closely linked to the way that the century application is using it? And it wouldn't really be worth spending the time on trying to replicate the functionality for a different back end or for a different consumer?
     
    Escaping Analysis Paralysis For Your Data Platform With Data Virtualization
    2019-11-18 (duration 55m)
    [transcript]
    48:48 Are there any other aspects of the scale platform and the work that you're doing there or the ideas around data virtualization or data engineering automation that we didn't discuss yet that you'd like to cover before? close out the show.
    47:15 And what do you have planned for future iterations of the scale platform and business either in terms of feature improvements or new product areas,
    30:44 Ah, alright. Well, that depends. I think on the organization, I think that you would be best served to have your data engineering team, working on building out what some people call the real time enterprise or streaming and figuring out How to Improve the latency and the quality of the data as it pertains to collection in all the ingest stuff, and then potentially, I think that has much higher yield because garbage in garbage out faster data, you know, faster is better. The streaming use cases, nobody's really figured that out yet, at a sort of an enterprise scale. So that's a great place to spend your data engineering time. There are, you know, I read this statistic somewhere in I wish I could remember where so I could attribute it but I went back and checked and it's true. There are 7500 job openings for data engineers in San Francisco. There are 7400 people with the title of a data engineer in LinkedIn for the US. So we have more demand for data engineering skills in San Francisco, then we have supply in the US I'm sure they're going to find something to use those data engineers to do if they don't have to go and get this data element, make it available in Tableau for, you know, for the marketing group that's, that should be automated, I think we can all agree, things that should be automated, or can be automated, probably should be automated. That's an Fs that we really believe in. And it's not about taking away jobs at data engineering. In fact, it's about making data engineers much more happy in the work that they do. Look at what they're doing. If you join data engineering teams that at Facebook or or, or Amazon or Google or any of the big cloud vendors. Those are really interesting challenges. Making a data element available for an end user is super high value from a business perspective, but not a fun engineering challenge. And in terms of challenges that you have been faced with and the process of building growing the at scale platform, I'm curious what have been some of the most interesting or unexpected ones you've had to overcome. And some of the most useful lessons that you've learned in the process. Hire, I think hiring it finding the right people to work on. The types of problems that we work on that are extremely algorithmic in nature. We every single thing needs to be scalable to multi petabytes, you know, on the engine side of things, which is the Scala based software that we develop, it's, it's all about getting the right person in there and then getting them up to speed on, you know, essentially how databases are built. Even though we're not a database. We're kind of doing database like development, but even harder, because we have to support all the databases out there.
     
    Designing For Data Protection
    2019-11-11 (duration 51m)
    [transcript]
    14:00 Yeah, some of the ways that you can determine whether or not the data that you're dealing with is actually subject to these regulations. And I think that the blanket approach that a lot of companies are taking is that it's too hard to identify at a granular level, whether or not somebody is a European citizen or isn't or is in some way related to the European Union or California. And so they just apply the same sets of principles in a blanket sense. And I'm wondering what your thoughts are on some of the sort of best strategies to approach the regulatory environment that we're in now.
    16:44 And then, from the organizational and technical perspective, what are some of the conflicts or constraints that act against some of the efforts that they might try to put in place to implement data protection whether it's because The technical systems design that they have doesn't really allow for proper segregation or tracking or whether it's a matter of policy as far as helping the different people within the organization understand the importance of these different regulations and their enforcement.
    37:51 undertake. And then another layer where this manifests particularly in terms of updating data, or having a custom dumber allied bits of information from their records is how it's being used in downstream use cases, whether it's business analytics or doing some sort of machine learning on aggregate data, and how that plays into the need to either regenerate a model after it's gone through a training regimen once you get the data updated, or how the data is actually being used, or what particular attributes of a record are being used within those analytics and some of the technologies and techniques that are viable for still remaining within compliance of these regulations, especially as far as some of the explain ability requirements that come up.
     
    Automating Your Production Dataflows On Spark
    2019-11-04 (duration 48m)
    [transcript]
    37:54 and what are some of the most interesting or unexpected lessons that you've had to learn in the process or edge cases that you've encountered? Well, building us and both from the technical and business perspective?
    45:26 And are there any other aspects of the work that you're doing at ascend or other aspects of the idea of declarative data pipelines or anything along those lines that we didn't discuss yet that you'd like to cover? Before we close out the show,
    40:27 And then in terms of the overall capabilities of the system, or business success that you've achieved so far, what are some of the elements that you're most proud of? And in terms of feature sets or capabilities, any that have gained the greatest level of adoption?
     
    Build Maintainable And Testable Data Applications With Dagster
    2019-10-28 (duration 1h7m)
    [transcript]
    1:02:03 Are there any other aspects of Dexter or your work elemental or your thoughts in the space of data applications that we didn't discuss yet that you'd like to cover before we close out the show?
    31:57 and then for somebody who wants to Extended Dexter and either integrate it with other systems that they're running or add new capabilities to it or implement their own scheduler logic. What are the different extension and integration points that Dexter exposes?
    25:39 Yeah, and if there's nothing else, the other thing that I really noticed coming at this industry fresh is just how heterogeneous and fractured it was. Meaning that in when you have teams building these data applications, even in the simplest case of like a kind of a coherent or typical European Crossing three or four technology boundaries with dealing with these
     
    Data Orchestration For Hybrid Cloud Analytics
    2019-10-22 (duration 42m)
    [transcript]
    31:26 and shift approach. And we've highlighted a few different tools in the data orchestration space in the form of Alexia and presto and spark etc. But I'm wondering what you see as being some of the missing pieces or gaps in the landscape where there's an opportunity for either extending some of the existing tools or building a new tool or platform to fill that particular gap and any efforts that you're seeing on those friends?
    39:48 And are there any other aspects of data orchestration or hybrid cloud migration projects that we didn't discuss yet that you'd like to cover before we close out the show?
    14:43 Yeah, definitely. And I'm wondering if you have any specific instances or an example topology of their a customer that you've worked with, or a story that you've heard from people who are either using Alexia or somebody These other tools in the space to give a bit more of a concrete feel for somebody who is maybe still a little uncertain about how they might go about approaching this particular type of problem of having a constraint in terms of the amount of computer storage that they're able to access and then needing to be able to leverage some of these technologies to be able to expand their footprint and expand their capabilities.
     
    Keeping Your Data Warehouse In Order With DataForm
    2019-10-15 (duration 47m)
    [transcript]
    37:50 And then in terms of your experience of building and running the data form project and business, what have you found to be some of the most interesting or Unexpected or challenging lessons that you've learned?
    43:24 Yeah, having the pre packaged data tests, I'm sure would be quite interesting and useful, especially for cases where you're providing validation for some common data sources where somebody might be pulling from Google Analytics, or maybe Twitter or LinkedIn for being able to ensure that there's an appropriate schema or that you have some sort of common needs that are able to be encapsulated in those pre baked tests.
    44:41 Well, for anybody who wants to get in touch with you or follow along with the work that you're doing. I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get your perspective on what you see as being the biggest gap in the tooling or technology that's available for data management today.
     
    Open Source Object Storage For All Of Your Data
    2019-09-23 (duration 1h8m)
    [transcript]
    53:20 because of the fact that you have gained this measure of popularity, I'm sure that there have been some interesting use cases that have come about and I'm wondering what you have found to be some of the most interesting or innovative or unexpected ways that you've seen mid IO used.
    1:02:23 Are there any other aspects of the work that you're doing admin IO, or the overall space that you're working in, that we didn't discuss yet that you'd like to cover? Before we close out the show?
    35:28 So can you talk through the overall clustering strategy that you use and some of the way that you actually manage the file metadata, given that you don't have a centralized storage layer or a database for being able to reference that.
     
    Navigating Boundless Data Streams With The Swim Kernel
    2019-09-18 (duration 57m)
    [transcript]
    42:50 object handle or an object
    38:39 let the app application data or let the data build the app, or most of the app can bonus in response
    09:29 And then as far as the primary use cases that you are enabling with the swim platform, and some of the different ways that enterprise organizations are implementing it, what are some of the cases were using something other than swim, either the OS or the Data Fabric layer would be either impractical or intractable if they were trying to use more traditional approaches such as Hadoop, as you mentioned, or data warehouse and more batch oriented workflows?
     
    Building A Reliable And Performant Router For Observability Data
    2019-09-10 (duration 55m)
    [transcript]
    39:19 that they need. Another aspect of the operational characteristics of the system are being able to have visibility into particularly at the aggregate or level, what the current status is of the buffering or any errors that are cropping up, and just the overall system capacity. And I'm curious if there's any current capability for that, or what the future plans are along those lines.
    36:03 And then, in terms of the deployment, apologies that are available, you mentioned one situation where you're forwarding to a Kafka topic. But I'm curious what other options there are for ensuring high availability, and just the overall uptime of the system for being able to deliver messages or events or data from the source to the various destinations.
    51:28 Well, I'm definitely going to be keeping an eye on this project. And for anybody who wants to follow along with you, or get in touch with either of you and keep track of the work that you're doing, I'll have you each add your preferred contact information to the show notes. And as a final question, and I would just like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today.
     
    Building A Community For Data Professionals at Data Council
    2019-09-02 (duration 52m)
    [transcript]
    49:14 Are there any other aspects of your work on data council or your investment, or your overall efforts in the data space that we didn't discuss yet that you'd like to cover before we close out the show?
    50:09 And for anyone who does want to follow up with you, or keep in touch or follow along with the work that you're doing. I'll have your contact information in the show notes. And as a final question, I just like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today.
    39:47 in terms of the challenges that are often faced for an engineering founder in growing a business and making it viable. What are some of the common points of conflict or misunderstandings or challenges that they encounter? And what are some of the ways that you typically work to help them in establishing the business and setting out on a path to success? Well, I
     
    Building Tools And Platforms For Data Analytics
    2019-08-26 (duration 48m)
    [transcript]
    44:02 So for anybody who wants to get in touch with you or follow along with the work that you're doing, I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today.
    44:27 but monitor in knowing again, when I have a question and I see something out of place, I can very quickly tied out whether or not it was because I did have changes whether or not it was because some assumption that I made got it validated whether or not it was because a data pipeline didn't work, or a pipeline ran in a way that was an ordering That was unexpected, all those sorts of things, I think are super valuable, and save analysts tons of time, from actually having to dig through kind of the weeds of these problems. There's another place that I think we're starting to see some movement. But we still sort of don't have a real solid four, which is a centralized modeling layer,
    31:12 actually a great point to to be made, as far as the relationship between data engineers, and data analysts and ways that data engineers can help make the analyst job easier is actually making sure that they are integrating those quality checks and unit tests and being able to have an effective way of exposing the output of that as well as incorporating the analyst into the process of designing those quality checks to make sure that they are asserting the things that you want them to assert. So in the context of the sort of semantics of distributed systems, there's the concept of exactly once delivery or at least once delivery or at most once delivery, and that understanding how that might contribute to duplication of data or missing data, and what are the actual requirements of the analyst as far as how those semantics should be incorporated into your pipeline? And what should you be shooting for? And how are you going to create a certs whether it's using something like DVT, as you mentioned, or the Great Expectations project, or some of the expectations, capabilities that are built into things like data lake, and then having some sort of dashboard or integration into the metadata system, or a way of showing the analyst at the time that they're trying to execute a query against a data source, these are the checks that ran, these are any failures that might have happened so that you can then take that back and say, I'm not going to even bother wasting my time on this, because I need to go back to the data engineer and tell them, this is what needs to be fixed before I can actually do my work.
     
    A High Performance Platform For The Full Big Data Lifecycle
    2019-08-19 (duration 1h13m)
    [transcript]
    26:01 co I love to so very well let's let's set up something very simple. As an example, you have a number of data sets that are coming from the outside, you need to load those data sets into HPC. So the first operation that happens is something that is known as spray spray is simple process is an spray comes from the concept of spray painting the data across the cluster, right. So this runs on a Windows box or a Linux box and it will take the data set, let's say that your data set is just given number in million records long. It will unusual as it can be in any format, CSV or or any other or fixed length limited or whatever. So it will look at your data total data set, it will look at the size of the four cluster where the data will be saved initially for processing. And let's say that you have a million records in your data set and you have MN nodes on your for let's just make round numbers and the small numbers. So it will a petition the dataset into 10 partitions because you have to note and it will a then just copy transfer each one of those partitions to the corresponding to full node This is done. If it can be better lies in some way, because for example, your latest fix link, it will automatically use pointers and paralyze this if the data is in either no and XML format or in the limited format where it's very hard to find the partition points, you will need to do a pass in the data, find the friction points and eventually do the panel copying to the thought system. So now you will end up with 10 partitions of the data with the data in no particular order, the Netherlands, all of them that you had before, right. So the first 100,000 records will go to the first note the second 100,000 Records, we go to the second node and so on so forth until you go to the end of the data set this put each one of the nodes in a similar amount of records per node, which tends to be a good thing for most processes. Once the data is spread or
    1:09:48 it's great to hear that you have all these outreach opportunities as well for trying to help bring more people into technology as a means of giving back as well as as a means of helping to be your community and contribute to the overall use cases that it empowers. So for anybody who wants to follow along with you or get in touch, I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today,
    56:41 that level of abstraction that is pretty high anyway, in ECL, wasn't enough for prolific data linkage. So we created another language we called it sold and we the unrelated language is open source, by the way, it's still providing, but that language is a language that is you're going to consider it a domain specific language for data Liggett productively only get and data integration, so that a compiler for salt, compile salt into CL, and they feel compelled by this EL into c++, c++, clang or GCC compiler into assembler. So you can see how abstraction layers or like layers in an audience, of course, every time you apply an improvement and optimization in the sale compiler, or sometimes the GCC compiler team applies an optimization. And you see everyone else on top of that, of that layer benefits from the optimization, which is quite interesting. We like it so much that eventually we have another problem, which is dealing with graphs. And when I say graphs, I mean social graphs rather than
     
    Digging Into Data Replication At Fivetran
    2019-08-12 (duration 44m)
    [transcript]
    37:49 And as far as the overall challenges or complexities of the problem space that you're working with, I'm wondering what you have found to be some of the most difficult overcome, or some of the ones that are most noteworthy and that you'd like to call out for anybody else who is either working in this space or considering building their own pipeline from scratch.
    20:00 One of the other issues that comes up with normalization. And particularly for the source database systems that you're talking about is the idea of schema drift, when new fields are added or removed, or a data types change, or the overall sort of the sort of default data types change. And we're wondering how you manage schema drift overall, in the data warehouse systems that you're loading into well, preventing data loss, particularly in the cases where a column might be dropped, or the data type changed.
    39:45 on both the technical and business side, I'm also interested in understanding what you have found to be as far as the most interesting or unexpected or useful lessons that you've learned in the overall process of building and growing five Tran?
     
    Solving Data Discovery At Lyft
    2019-08-05 (duration 51m)
    [transcript]
    47:40 Are there any other aspects of the Amundson project itself or the ways that it's being used at Lyft, and in the open source community, or the engineering work that has gone into it that we didn't discuss yet that you'd like to cover before we close out the show?
    27:16 And that brings up another interesting question, as far as how you determine whether or not a given data set should be surfaced to somebody based on compliance or regulatory reasons, or just read what the general access control is for that data set. Because if somebody's searching for something, and their role is not going to grant them access to it, but then they see it listed and Amundson, I'm wondering what the just overall processes for being able to integrate and surface that information at the appropriate time?
    49:18 Well, for anybody who wants to follow along with the work that you're doing, or get in touch, or provide any feedback on the tool that you've built in the form of Amundson, I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get the perspective of each of you on what you see as being the biggest gap in the tooling or technology that's available for data management today. So Mark, if you want to go first and answer that,
     
    Simplifying Data Integration Through Eventual Connectivity
    2019-07-29 (duration 53m)
    [transcript]
    46:46 And for anybody who wants to dig deeper into this idea, or learn more about your thoughts on that, or some of the Jason technologies, what are some of the resources that you recommend they look to?
    49:04 Is there anything else about the ideas of eventual connectivity or ATL patterns that you have seen, or the overall space of data integration that we didn't discuss yet that you'd like to cover? Before we close out the show?
    40:53 And for certain scales or varieties of data, I imagine that there are certain cases that come up when trying to load everything into the graph store. And so I'm wondering what you have run up against as far as limitations to this pattern, or at least alterations to the pattern to be able to handle some of these larger volume tasks.
     
    Straining Your Data Lake Through A Data Mesh
    2019-07-23 (duration 1h4m)
    [transcript]
    34:21 backbone messaging or
    1:02:08 Standardization, I would just, if I could wish for one thing was a little bit of a convergence and standardization in that allows still a polyglot world, you know, you can still have a polyglot world, but I want to see something like, you know, convergence that happened around Kubernetes in the infrastructure and operational world, some similar similar or the standardization that we had with, you know, history, TP and re, you know, rest or gr PC and so on in the world of data so that we can support a polyglot, you know, an ecosystem. So I think I'm looking for tools that are ecosystem players and kind of supported distributed in a polyglot data world, not data that can be managed just because we put it in one database or one data store, just because it's used by one party owned and ruled by one particular tool. So open standardization around data is what I'm looking for. And there are some, you know, small movements. Like, if you look at the end, they're not coming, unfortunately, not coming from the data world. Like, for example, the work of CNC f off the back of the circle is thinking about if the events are one of the fundamental concepts, you know, talking about cloud events as a standard way of describing events. But that's coming from left field, again, that's coming from an operational world trying to play in an ecosystem, not a data world. And I hope we can get more of that from the data world.
    56:32 And in your experience of working with different organizations, and through different problem domains and business domains. I'm wondering if there are any other architectural patterns or anti patterns or design principles that you think that data professionals should be taking a look at that aren't necessarily widespread within that community?
     
    Maintaining Your Data Lake At Scale With Spark
    2019-06-17 (duration 50m)
    [transcript]
    49:09 Tobias Macey: And are there any other aspects of delta lake or Spark or the work that you're doing a data bricks or just the overall landscape of data, lakes and data warehouses that we didn't cover yet that you'd like to discuss before we close out the show?
    43:08 Tobias Macey: And going back to the duplication of data that your customers have been seeing and issues in terms of cost control. I'm wondering what some strategies are that you can potentially employ, whether it's something like compaction, or going back and pruning old versions of data and any cases where that might be a bad idea, or cases where it would be necessary, either in terms of space savings, or cost savings, particularly when using cloud resources.
    49:28 Tobias Macey: All right, well, for anybody who wants to follow along with the work that you're doing or get in touch, I'll have you add your preferred contact information to the show notes. And as a final question, I just like to get your perspective on what you see as being the biggest gap in the tooling or technology that's available for data management today.
     
    Managing The Machine Learning Lifecycle
    2019-06-10 (duration 1h2m)
    [transcript]
    03:26 Stepan Pushkarev: So I did not remember exactly. It's probably it was probably the earlier versions of spark back to 2016. Probably not 16. Or even like, 14, I don't remember exactly. So it's, it's kind of the older older software engineering world and,
    28:57 Tobias Macey: And you highlighted a couple of things, they're that can contribute to the overall model drift or model degradation in terms of changes in the usage patterns for end users or changes to some of the input data. But I'm wondering if you can just talk through in general, some of the factors that will contribute to that sort of model drift and any type of contextual information that you're monitoring and alerting can feed back to the data scientists or data engineers or machine learning engineers to understand what what alterations to make the training process to correct for that, and just some of the overall contextual knowledge that's necessary to be able to engineer in sort of resistance or just tightening the feedback loop for keeping those models in proper working order and ensuring that they're doing what they're intended to do.
    45:34 Tobias Macey: And as the availability of different tooling. And the overall level of understanding and sophistication of practitioners in the field has grown in the past few years. I'm wondering how that has influenced overall design and architecture of Hydrosphere, and some of the types of tooling or platforms that are often used in conjunction with Hydrosphere and how that might break down across the different sort of roles across the data team as far as data engineer versus data scientist or machine learning engineer.
     
    Evolving An ETL Pipeline For Better Productivity
    2019-06-04 (duration 1h2m)
    [transcript]
    50:03 Tobias Macey: And so as you continue to work with data, coral, and data and revenue as you can do to work with greenhouse, I'm wondering what you're hoping to see in the future in terms of the platform evolution, or any plans that you have going forward to add new capabilities or capacity to data coral?
    52:56 Tobias Macey: And are there any other aspects of the work that you're doing at greenhouse saw the work that you're doing at data coral, or the sort of interaction between the two companies that we didn't discuss yet they'd like to cover before we close out the show?
    27:47 Tobias Macey: and Ragu. I'm also curious about some of the sort of edge cases or sort of sharp points in your infrastructure and architecture that ended up getting ironed out in the process, onboarding Aaron, and greenhouse and any of the other customers that you were working with? And in the sort of similar time frame?
     
    Data Lineage For Your Pipelines
    2019-05-27 (duration 49m)
    [transcript]
    40:51 Tobias Macey: And so in terms of your overall experience of building and maintaining and scaling the packer and project and business, what have you found to be some of the most challenging or useful or unexpected lessons that you've learned?
    43:14 Tobias Macey: And what are some of the limitations or edge cases a pack under man? When is it the wrong choice?
    36:15 Tobias Macey: so earlier, you were saying how package arm because it is so flexible, the ways that people are using it is sort of up to everyone's imagination. And so I'm curious what you have seen as far as being the most interesting or innovative or unexpected ways that people have been leveraging the platform,
     
    Build Your Data Analytics Like An Engineer With DBT
    2019-05-20 (duration 56m)
    [transcript]
    47:29 Tobias Macey: of you know, the world that is like materialize, if you will, by dbt. And in terms of your experience of building and maintaining the dbt project, what have you found to be some of the most interesting or useful or challenging lessons that you've learned?
    44:42 Tobias Macey: And so what have you found to be some of the most interesting or unexpected or innovative ways that you've seen dbt used?
    05:33 Drew Bannon: Sure. So these days, it's pretty easy to get data into a data warehouse. And so there are off the shelf tools like stitcher five train, or you might roll your own email with air flow. Once that data is there, you want to transform it. And so I mentioned views. That's how dbt worked in the early days. But since then, we've built that diabetes capabilities to build tables and what we call incremental models that are like incrementally refresh tables, for instance, and so on, these things happen inside of your warehouse, dbt will connect to redshift, or snowflake, or Big Query, etc. and run sort of create table as or create view as or insert statements to transform this data based on a select statement that you've written to codify a data model. And so dbt fundamentally works by materializing these data models in your warehouse is tables and views. So once you've created these things, there's a whole suite of things you can do with them, or testing and documenting etc. But probably the thing you want to do is connect your BI tool or your Jupyter Notebooks to them. So so you can query a clean data set that you've prepared with dbt.
     
    Unpacking Fauna: A Global Scale Cloud Native Database
    2019-04-22 (duration 53m)
    [transcript]
    22:41 Tobias Macey: And in terms of people who are first getting started on working with Fatah, in interacting with the SQL syntax, or starting to work with some of these higher level interfaces. I'm wondering what are some of the common points of confusion or surprise or edge cases that they run up against?
    45:14 Tobias Macey: And in terms of your experience of building and growing the technical and business aspects of Fatah, I'm wondering what you have found to be some of the most interesting or unexpected or challenging lessons that you've encountered in the process,
    26:15 Tobias Macey: the types of use cases, that fauna is built for in the types of application design patterns that enables, I'm wondering if there are any sort of unique architectures that it lends itself well to that would be impractical with a single purpose database, whether it's a relational database, or NoSQL, document store or something like that,
     
    Building An Enterprise Data Fabric At CluedIn
    2019-03-25 (duration 57m)
    [transcript]
    46:59 been some of the most notable customer success stories that you've experienced, or interesting or unexpected ways that people have used the gluten platform.
    56:07 And are there any other aspects of clued in or a data integration or data engineering that we didn't discuss yet you'd like to cover before we close out the show?
    52:39 And so are there any other cases where you found that gluten is not the right choice and a company or organization is better off using a different system, whether it's because of the size of the organization and complexity that they're dealing with? Or because of just the overall sort of goals that they have for managing their data integration? Or any sorts of issues with control or visibility into the system?
     
    Deep Learning For Data Engineers
    2019-02-25 (duration 42m)
    [transcript]
    40:48 right. And for anybody who wants to follow along with you, or get in touch or see the work that you've been doing, I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today.
    32:47 What is your personal litmus test for determining when it is useful and practical to use deep learning as opposed to traditional machine learning algorithm, or even just a basic decision tree for providing a given prediction or decision on whatever the input data might happen to be?
    12:16 And I think your point to about the fact that deep learning is particularly applicable to these projects that are focused on rich media, as you put a video or images or audio, it starts to look more like a content delivery pipeline than necessarily the traditional data pipeline that we're used to where we might be working more with discreet records, or, you know, flat files on disk, or things like that, that have a lot of structured aspects to it, where there might be similarities between records that are conducive to different levels of compression, or aggregation. Whereas with video, in particular, and even audio, there is a lot less of that similarity from second to second within the content, but also between files, because there are so many different orientations that are possible for an image frame or anything like that. So just conceptually, it requires a much different tack as to how you're managing the information, and how you're providing it to the algorithms that are actually processing it,
     
    The Alluxio Distributed Storage System
    2019-02-19 (duration 59m)
    [transcript]
    08:00 adds more enhancements in security or in large scale or high availability, this kind of enterprise readiness features like owning a lot of these large corporations they are looking for when you run services in their environment. Yeah. So this is our business model.
    49:36 Bin Fan: the system requirements is not really I would say, for the master of depends on the master or worker nodes. For our managed service running on the master node, you probably want some relatively beefy nodes with enough memory, or see like, under order 10, 200
    51:03 Tobias Macey: and what are some of the cases where you would recommend against using Alexia for a given use case. And I'm curious if there are any other projects or products that are working in a similar or possibly adjacent space, that might be a better fit for those situations,
     
    Apache Zookeeper As A Building Block For Distributed Systems with Patrick Hunt
    2018-12-03 (duration 54m)
    [transcript]
    48:35 comes to mind. And going forward. What are some of the plans that you have for the future of zookeeper either in terms of improvements or new features or capabilities or anything in terms of the community or educational aspect of the project.
    15:08 beyond things like leader election or locking, what are some of the other types of system level features that somebody who's building an application or a distributed systems on on top of zookeeper will typically need to implement themselves that are still too special case to have just a general solution for,
    50:55 So we're we're learning things as we go long as well, which is great. And are there any other aspects of the zookeeper project or distributed systems primitives or centralized coordination for these types of systems that we didn't cover yet, which you think we should discuss before we close out the show?
     
    A Primer On Enterprise Data Curation with Todd Walter
    2018-09-24 (duration 49m)
    [transcript]
    31:50 Absolutely. And in the display room, you have lots of metadata, they're linked together bye bye time or, you know, timelines or, or geography or all of that. They're easily understood, they're all articulated so that you can, you know, the thought it like joining together, you don't you you can you can see it all in in its relationships, in addition to just the data points in individual form.
    20:20 And in the data warehouse, where you're storing these aggregates, or these condensed analyses of the raw data, would you generally also have some record of the original source of the data and the provenance so that somebody who is interested in doing a different type of analysis or trying to use a different algorithm for generating these aggregates can then go back to those records easily to be able to try and either replicate or discover new reflections of that information?
    32:23 And for organizations or individuals who are first starting to plan out the overall data architecture and the associated infrastructure and systems that they're going to be building and their curation processes. What have you found to be some of the common mistakes that ultimately result in failure of either a lesser or greater degree,
     
    Data Serialization Formats with Doug Cutting and Julien Le Dem
    2017-11-22 (duration 51m)
    [transcript]
    13:54 file formats or storage from it.
    01:24 Yeah, I'm Doug cutting. I've been building software for 30 or more years now, often with a serialized component. The last 15 or so years have been dominated by work on open source, most notably Hadoop. But for the purposes of this podcast, we're think we're going to talk about a project I started called Apache arrow.
    38:57 How do you think that the event evolution of hardware, and the patterns and tools for processing data are going to influence the types of storage formats that either maintain or grow in popularity?
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