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    Stones that calculate
    2020-09-17 (duration 1h24m)
    [transcript]
    1:14:49 Benjamin äh Wapens of Matt Distruction, Café und Neer, ähm weil.
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    Influencer-Culture, Silicon Valley Wahlkampf, Gamescon, Greta bei Angela, Tenet
    2020-08-31 (duration 4h33m)
    [transcript]
    2:09:01 Und was ich wirklich krass finde an den Tag als Apple 2 Millionen wert war kam diese Nachricht auch von Matt Mühlenweg der Gründer von WordPress.
     
    Audiotwitter, Free Speech, Motivation fürs Leben, Trump ohne Base
    2020-06-29 (duration 3h52m)
    [transcript]
    3:29:59 Das ist ein gutes Stichwort. Nochmal zu den Redaktionen Es gab da einen Text von Matt Tibi oder Tipp Talib Talib.
     
    01/20 Neujahrsansprachen, EZB Sinnsuche, Gesichtserkennung, elektronisches Make Up & Kurzfilmrevival
    2020-01-31 (duration 4h55m)
    [transcript]
    4:10:45 man kann das Leben von John Carpenter irgendwie sich merkwürdig verhalten zumindest so leicht verschoben und wenn ich mir,
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    Das offene Web, Domain of One's Own und Hochschule
    2020-08-15 (duration 59m)
    [transcript]
    19:37 Wie wie würdest du's denn technisch äh implementieren? Also wenn du Hochschule, das eben noch nicht matt macht, was würdest du sagen, was sie dafür braucht.
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    FG080 Medien und Meinungsbildung
    2020-05-25 (duration 1h27m)
    [transcript]
    19:17 Das war übrigens Jean-Remy von Matt… 19:30 Also es gab ursprünglich von Jung von Matt diese Kampagne, „Du bist Deutschland“. 17:48 Anderes Beispiel, das auch für Aufsehen gesorgt hatte, war, dass von Jung von Matt, ich weiß den Namen gerade nicht mehr, wer es war,
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    In diesem Sinne Regenrinne
    2020-02-19 (duration 2h28m)
    [transcript]
    53:02 Ja also ein bisschen Sachs ist mit dem matt wieder so ein bisschen ach na ja das läuft schon die Eintracht wir machen das schon wir sind super Verein und wir sind hier Klasse mehr ruht sich schon wieder finde ich so ein bisschen auf den Lorbeeren auf die man die letzten Jahre hart erarbeitet hat und das will ich einfach da
     
    Die zwei Gesichter der Eintracht
    2020-02-05 (duration 1h48m)
    [transcript]
    14:30 komplett jetzt irgendwie matt mit dem ganzen Ding bin sondern schon denke okay sie scheinen es verstanden zu haben aber so
     
    Topflappen
    2020-01-15 (duration 2h3m)
    [transcript]
    27:39 Genau die ist jetzt glaube ich im Aufsichtsrat von Adidas und Geschäftsführerin von Jung von Matt Sport und die war ja Torhüterin und FSV Frankfurt so da habe ich mit David viel mit Spielerinnen geredet,
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    The Benefits And Challenges Of Building A Data Trust
    2020-02-03 (duration 56m)
    [transcript]
    01:47 Sure. Hi, my name is Tom Plagge. I came to bright hive round about the time at formed actually myself and the CEO Matt gie. had been working together at the University of Chicago and then had been doing Some consulting work. And so as we were creating bright hive, I was working for another data science startup and was convinced by by Matt and by the vision that you put forward to come over and lead the product and engineering team here.
     
    Escaping Analysis Paralysis For Your Data Platform With Data Virtualization
    2019-11-18 (duration 55m)
    [transcript]
    00:11 Hello, and welcome to the data engineering podcast the show about modern data management. When you're ready to build your next pipeline or want to test out the projects you hear about on the show, you'll need somewhere to deploy it. So check out our friends over at Lynn node. With 200 gigabit private networking, scalable shared block storage and a 40 gigabit public network. You've got everything you need to run a fast, reliable and bulletproof data platform. And if you need global distributions, they've got that covered to with worldwide data centers, including new ones and Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to data engineering podcast.com slash Linux that's LINOD today to get a $20 credit and launch a new server and under a minute, and don't forget to thank them for their continued support of this show. This week's episode is also sponsored By data coral and AWS native server lists data infrastructure that installs in your VPC data coral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs rather than pipeline maintenance. Revenue Murthy founder and CEO of Dana coral builds data infrastructures at Yahoo and Facebook scaling from terabytes to petabytes of analytic data. He started data coral with the goal to make sequel the universal data programming language. Visit data engineering podcast.com slash data coral today to find out more. And having all of your logs and event data in one place makes your life easier when something breaks. Unless that's something is your Elasticsearch cluster because it story too much data. Chaos search frees you from having to worry about data retention, unexpected failures and expanding operating costs. They give you a fully managed service to search and analyze all of your logs and s3 entirely under control all for half the cost of running your own Elasticsearch cluster or using a hosted platform. Try it out for yourself at data engineering podcast.com slash chaos search and don't forget to thank them for supporting the show. You listen to this show to learn and stay up to date with what's happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet listen and learn from your peers you don't want to miss out on this year's conference season. We have partnered with organizations such as O'Reilly Media, cranium global intelligence Alexey own data Council. Upcoming events include the data orchestration summit and data Council in New York City. Go to data engineering podcast.com slash conferences to learn more about these and other events and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey, and today I'm interviewing Matthew Baird about at scale, a platform for data virtualization and a universal semantic layer. So Matt, can you start by introducing yourself
     
    Evolving An ETL Pipeline For Better Productivity
    2019-06-04 (duration 1h2m)
    [transcript]
    45:39 Aaron Gibralter: That's a fantastic question. That's, that's kind of like the next. I think that's one of the next big things for our team. As we scale as we hire more data scientists, that's going to be extremely important for us to have that discovery ability. And that, you know, that structure that makes sense. Because if we don't, I think there's going to be a lot of rework or you know, stuff on each other's toes. I think this is also an area that data coral is working on. And it's another piece that I feel like, I know that we feel the data corals working on this lot. And I hope that they feel that we're contributing again, on this Friday in terms of feedback. But I think there's some work to do here in terms of how to standardize these workflows. So to get into the nuts and bolts, basically did a coral provides a CLA tool, you know, you run data coral, and then a command like data, coral organize. And then you can say, Matt view, materialized view create, and then you specify a path to a file, a dp l file data programming language file, that's essentially a sequel, a sequel command, and with some comments and some annotations on the top of it, let's say you know, what kind of a materialized view it is, and what what is the frequency with which it should be refreshed, and so on. And so that sequel file is kind of like the source of that is the transformation that what's going to happen. And so in a naive world, you basically have people just to write, you know, write some sequel, and then run into through the CLA and create these map views. Obviously, we want to be doing code review, we want to have, you know, we want you know, someone if someone's going to create a new materialized for you, we want someone else to approve it. And we want it all under version control. So we have a single get repository called Data coral that contains all of our materialized views and a structure that makes sense to us. So we basically have the different schemas as top level directories. So you can imagine a schema roughly correlated with a kind of a use case. So can say like analytics underscore CS, like Customer Success analytics, all the materialized views that power, the dashboards that the CS team uses all the materials for us there are in that directory and that schema, but what we've had to do is write some, you know, make files or scripts to kind of make the process a little bit more streamlined. And then I think there's the the risk that we don't have any kind of CIC is continuous integration or continuous deployment of these things. So we still have to run them manually. So even when we open a pull request, we've had to come up with our own process, where will open a pull request, say, Hey, I'm going to create this materialized for you? Can someone take a look at it? And once it's approved, then I use the CLA to deploy it. But there's no like, it's not being enforced. And it's not being automated. And I'd love to get there. But I don't, I think in some ways, like there's some work for us to do. And in some ways, there's work that data coral is doing to make this a bit more streamlined as well.
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    Faster And Safer Software Development With Feature Flags
    2019-11-26 (duration 1h1m)
    [transcript]
    32:24 I think it's a great, it's a really good question. I think like, in general, if you can, if you can make the flagging decisions static in terms of, for any request for this version of the code, it's going to go this way through the system all the way through the system. That's the ideal because because then you get that audit trail via source control, right. So if, if I if my decision as to whether to show that social login button is powered entirely by configuration that's kind of maybe hopefully checked into the same repository as my code itself or maybe this is kind of system repository, then you get this awesome audit trail and available audit trail, you also get like nice things around availability because you don't have some external system you need to talk to etc, etc. That's great. And I would, I would kind of, there's a, there's kind of an argument for, for doing that in the 31st the toggles or the flags that that work that way. The problem is almost always there's some need for that, for those, that flagging configuration to be more dynamic. So either, so it's like an operations toggle, for example. The ideal if you've got a really good Continuous Delivery practice and I just talked to a company the other day that does this, if they need if they're their hair's on fire, and they need to turn off, you know, turn off the external tax calculation vendor or maybe, you know, switch from recommendation system. Recommendation system B because recommendation system A is, is eating all of the CPU in the system. If you've got really healthy CD practices, continuous delivery practices, you just update the configuration in encode and you run it through your delivery pipeline. And that's how you make that change in production. And if you can do that, then good for you. That's, that's amazing. That's awesome. The most real life organizations, they need like an OSHA capability to do it at runtime without having to to make that configuration change. So Matt case, you need it to be more dynamic. And if you think about it, in terms of things like a B testing, and if you think about it in terms of toggles that are used to kind of incrementally roll out, you know what, let's roll this. Let's roll out the social button to 10% of our users and make sure that we don't get any five hundreds and then let's roll it out to 50% of our users, using using feature toggles for feature flags for controlled rollout you generally need it to be more dynamic than a code change. And so in that case, you, you basically need to get that auditability. From the, there's two ways to get it.
     
    From Simple Script To Beautiful Web Application With Streamlit
    2019-11-18 (duration 49m)
    [transcript]
    08:39 So Well, first of all, I have to give a shout out to Jupiter. Because they really like lead the way for having this amazing widget support in, in the Python community really. And so now a lot of great JavaScript libraries, like Dec GL, which is Uber's amazing geographic visualization library has Python bindings and the reason is because of Jupyter. Basically and a stream it's, we it's a it's a different use case from Jupyter. We we actually use both side by side to Jupiter is really for interactive exploration and and disseminating ideas. And it has many use cases actually, I'm streaming it's really for app building. But it turns out that we were really rapidly able to assimilate almost all of the major visualization libraries industry. So you know, Doc GL Matt plot lib plot Lee seaborne let's see, I'm missing a whole bunch of Altair, which is an amazing library. And then we have a bunch of the basically the standard widgets, so you know, various kinds of inputs, sliders, text, input, data, inputs, those kinds of things. And, and those are sort of the basic, you know, atoms in the periodic table of the stream. And then the real innovation is in the ability to mix and match those sort of almost instantly without having to define a complex declarative, you know, web layout with that gives and spans and all these HTML and CSS things just really write it as an ordinary Python script. So in that sense, so you know, that's allowed us to see a whole bunch of applications, I'm happy to share with you some of the some of the ones that we've seen if you're interested.
     
    Brian Granger and Fernando Perez of the IPython Project
    2015-06-13 (duration 1h21m)
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    1:14:50 through him how to implement a lot of the core algorithms, in data science, from scratch without relying on psychic learn, for example, or pandas. And it's at a really nice level, where someone who knows the basics of Python and none pie, and Matt plot live is going to be able to pick this up and learn a lot about the internal details. And so I I've been really enjoying that, especially as I think about how to teach data science to undergrads. And then the second book is with Cleveland's elements of graphing data. I got this a few months ago, and I've been spending time with it. And it's just an extremely practical, nice book about visualizations and plotting. And it's, it's a type of book that, you know, I spent five minutes minutes with it. And I feel like a lot of things that I had fuzzy thinking about in terms of visualization, all of a sudden became crystal clear, like simple questions of should your tick marks on your axes be pointing inside or outwards? And he says, Well, you have data on the inside of the frame. And if your tick marks point in words, there's a good chance that the data is going to overlap, the tick marks make it difficult to see them. So they should point out words. And obviously, it's not a universal rule that you always must follow a very practical way of thinking about choices like that. But otherwise, I in the past, I think I would approach them a more of a visual aesthetic perspective. And so it's a wonderful book.
     
    Reuven Lerner
    2015-04-23
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    47:13 I bet it must be really exciting, actually, for those people moving to Python from that lab, because Matt labs an awesome tool. But with something like Python, you have this incredibly rich ecosystem of not only can you do your numerical analysis, you can bring in like, you know, statistics and data analysis with panda or like, you know, or image processing with Phil or one of these other tools. And I mean, you just have this amazing, you know, plethora of tools to draw from, and you don't have to think in terms of, well, gee, if I want to use this, I'm going to have to pay another $5,000, or I have no concept of the amounts involved. Money just isn't an issue because most of them are free. It's got to be incredibly liberating for them.
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