search for: Earthquake Mexico
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    DML333 Gone Home
    2021-11-21 (duration 38m)
    [from description] ...[ ] Game: It takes two - [ ] Netflix ohne Star Trek Discovery - [ ] Serie: Narcos Mexico (Staffel 1) - [ ] Nachgefragt-Podcast https://nachgefragt-podcast.de
    [from itunes:summary] ...[ ] Game: It takes two - [ ] Netflix ohne Star Trek Discovery - [ ] Serie: Narcos Mexico (Staffel 1) - [ ] Nachgefragt-Podcast https://nachgefragt-podcast.de
    [from content:encoded] ... Game: Gone HomeGame: It takes twoNetflix ohne Star Trek DiscoverySerie: Narcos Mexico (Staffel 1)Nachgefragt-Podcast https://nachgefragt-podcast.de Discord: https://discord.gg/uQYt8R5YP4
    [transcript]
    33:18 Und Narcos Mexico
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    Traci Harrell - It's All Bigger Than Me. Leadership Perspective From The Ultimate Optimist.
    2021-11-19 (duration 37m)
    [transcript]
    05:49 York and North Carolina, Mexico
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    Branded Podcasts and Getting Featured in Apple with Fatima Zaidi
    2021-08-20 (duration 1h2m)
    [transcript]
    49:03 tuning in from New Mexico,
     
    The Average Podcast Episode Gets This Many Downloads in the First 7 Days (feat. Tom Buck)
    2020-11-20 (duration 1h4m)
    [transcript]
    26:02 Mexico, and 2.6. In the
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    An Artists Life
    2021-05-16 (duration 42m)
    [transcript]
    28:35 Mexico City and then I think 28:38 any follows Mexico How do I get
     
    Riding Motorbikes: Show Us Your Helmet
    2021-04-11 (duration 45m)
    [transcript]
    36:16 New Mexico or Wisconsin? A 36:24 is a B's New Mexico C is
     
    Making The Most Of Your Life: Who Wants To Live Forever
    2021-03-28 (duration 48m)
    [transcript]
    31:26 leave Mexico and sit down to
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    Rendlesham Forest UFO/UAP Incident
    2021-01-06 (duration 42m)
    [transcript]
    19:19 Mexico
     
    Yule Monsters and Good Saint Nick
    2020-12-16 (duration 55m)
    [transcript]
    45:16 earthquake.
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    Episode 4
    2020-09-20 (duration 26m)
    [transcript]
    24:47 Mexico.
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    Interview Sylvia Unger
    2020-04-06 (duration 36m)
    [transcript]
    11:43 ich weiß gar nicht sind das mexikaner der jüngste von den brüdern der ist ja also der ist eben auch ein bisschen kleiner wie alle mexico ich das jetzt hier sagen vielleicht wird es auch rausgeschnitten,
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    SSP Interview (X): Kai Schwind und der dunkle Taipan
    2020-03-06 (duration 1h34m)
    [transcript]
    23:51 Diesen schlagfertigen humor und das meinte ich glaube ich mit der amerikanische ist da ein bisschen direkter wenn sie dann mit ausgestreckten zeigefingern pistolen gehst machen ein mexico stand im büro haben.
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    A High Performance Platform For The Full Big Data Lifecycle
    2019-08-19 (duration 1h13m)
    [transcript]
    03:10 Oh, absolutely. So in Mexico, we solutions, we started with management, I say, our core competency back in the mid 90s. And as we go into risk management, one of the core assets when you are trying to assess risk, and predict outcomes is data. Even before people spoke about big data, we had a significant amount of data, mostly structured, semi structured data to but the vast majority structured. And we used to use the traditional platforms out there, whatever we could get our hands on. And again, this is old, back in the day before Hadoop. And before MapReduce was applied as a distributed paradigm for data management or anything like that. So databases, Sybase, Oracle, whatever was Microsoft SQL, data management platforms of initio information, whatever was available at the time. And certainly the biggest problem we had with a scalable, but was twofold one was the scalability, all of those solutions typically run in a single system. So there is a limit to how much bigger you can go vertically. And certainly, if you're trying to also consider cost affordability of the system. And that limit is that is much lower as well, right, there is a point where you go beyond what the commodity system is, and you start paying a premium price for whatever it is. So that was the first piece. So one of the one of the attempts of solving this problem was to split the data and use different systems but splitting the data, it creates also challenges around data integration, if you're trying to link data, surely you can take the traditional approach, which is you segment your data into tables. And you put those tables in different databases, and then use terms of the foreign key to join the data. But that's all good and dandy as long as you have a foreign key that is unique, handheld is reliable. And that's not the case with data that you acquire from the outside. If you didn't read the data, you can have that if you bring the data from the outside, you might have a record that says these records about john smith, and you might have another record that says this liquid Mr. john smith, but do you know for sure he does. Two records are about the same john smith. And that's, that's a Lincoln problem. And the only way that you can do Lincoln effectively is to put all the data together. And now you have a we have this particular issue where in order to scale, we need to segment the data, in order to be able to do what we need to do, we need to put the data in the same data lake as a dome team. Later, we used to call this data land, eventually we teach it term in the late 2000s. Because data lake become became more more well known. So at that point, the potential bats to overcome the challenge where well, we either split all of the data as we were before, and then we come up with some sort of meta system that will leverage all of these 3d data stores. And potentially, when when you're doing prolific linkage, you have problems that are in have the computational complexity always square or worse. So that means that we will be a significant price and performance but potentially can be done if you have enough time and your systems are big enough, and you have enough bandwidth in between the systems. But the complexity you're gaining from a programming standpoint is also quite significant. And
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    Being Muslim
    2019-06-30 (duration 39m)
    [transcript]
    30:58 Mexico
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