Tobias Macey

@TobiasMacey

Tech geek, programmer, avid reader, lifetime learner, family man host of and

Boston, MA
Vrijeme pridruživanja: listopad 2012.

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  1. 27. sij

    Data pipelines are difficult to test and maintain due to their complexity and scale. Great Expectations works to mitigate the technical debt that you have accrued by providing runtime checks that you can use in production. Give a listen and try it out!

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  2. 27. sij

    Adding login buttons for all of the social platforms that your users want can be tedious. Fortunately Python social auth can simplify the process for you. In this episode Matías Aguirre describes how it works, why he created it, and how you can use it.

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  3. 20. sij

    Becoming data driven is more about collaboration and easy access to data than just about having the information. In this episode Arik Fraimovich shares his experience building Redash to simplify that aspect of data sharing and discovery.

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  4. 20. sij

    Getting an inside look at how companies manage their data pipelines, particularly the evolution and adoption ofnew technologies, is a great way to learn about how to approach your own challenges. In this episode Mayvenn's head of data shares their journey.

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  5. 13. sij

    In this episode Reuven Lerner returns to reflect on his experiences as a Python trainer since he first appeared 5 years ago on episode 2. He also shares how deliberate practice has helped his students learn more effectively.

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  6. 13. sij

    In this episode Karthik Ranganathan talks about how YugabyteDB simplifies the work of building planet scale applications without having to sacrifice the convenience of SQL. An impressive feat of engineering that is fully open source!

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  7. 6. sij

    Moshe Zadka has been using Python to manage servers for years. In this episode he shares his experiences working in the field and writing an introductory book for helping other systems operators learn to use Python in their work.

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  8. 6. sij

    Debezium has become the de facto framework for managing your change data capture pipelines. In this episode the project founder and the current lead maintainer explain how it came to be, how it works, and how you can use it for your pipelines.

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  9. 30. pro 2019.

    DataDog has seen massive growth recently, and the driving force for that has been their ability to process timeseries data in real time at scale with a high degree of reliability. In this episode Vadim Semenov shares the systems and teams behind the growth

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  10. proslijedio/la je Tweet
    19. pro 2019.

    Are you a nerd? Individuals, employers, & gov't are being challenged by rapid technological changes. Take our Challenge & develop a solution for forecasting skills trends! Submissions due by Jan. 15th.

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  11. 23. pro 2019.

    The IDLE editor has been part of Python for over 20 years. In this episode Tal Einat shares the history of the module, his experience maintaining it, and some of the ways that it is being used. He also shares some of the new work being done on it.

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  12. 22. pro 2019.

    The middle ground between transactional databases and data warehouses is a sweet spot for interactive analytics with SQL. Materialize is aiming to own that use case with their engine for continuously updated views. Give this episode a listen to learn more!

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  13. 16. pro 2019.

    The career of Pete Fein has evolved alongside the growth of the Python language and the shifting focus of the community. In this episode he reflects on his fortunes as a result of choosing Python early in his career and where he thinks it will take him now

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  14. 16. pro 2019.

    Having a reliable and up to date metadata repository helps to solve numerous challenges in a data platform. At WeWork the Marquez project is being used for tracking data lineage, health, and discovery, and it's open source. Give it a listen and try it out.

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  15. 8. pro 2019.

    SnowflakeDB has seen huge growth in recent years. In this episode we dig into some of the technology behind it and why it might be the right choice for building your next data platform.

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  16. 8. pro 2019.

    Debugging with print statements is easy, but time consuming. Ram Rachum built PySnooper to capture that ease of use but with more powerful results. In this episode he talks about his motivations and how it works, definitely a tool worth checking out.

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  17. 2. pro 2019.

    Thomas Hatch is at it again with his work on the POP framework for experimenting with the paradigm of plugin oriented programming. Lots of great conversation about developing large and complex software, open source communities, and keeping an open mind.

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  18. 2. pro 2019.

    This was an interesting look at how the data engineering teams at Citadel are organized and how they work to power a successful business in the financial sector.

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  19. 26. stu 2019.

    Feature flags are useful for a variety of development and architecture patterns, including canary releases, A/B testing, and trunk based development. In this episode Pete Hodgson shares some valuable advice on how you can use them for your projects.

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  20. 26. stu 2019.

    An inside look at how and why Sentry built Snuba on top of Clickhouse for a faster and more maintainable platform to store and search on real time streams of error events for their customers. Lots of great lessons in this episode.

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