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    News

    The Python Podcast.__init__

    The podcast about Python and the people who make it great

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    Copyright: © 2023 Boundless Notions, LLC.

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    Latest Episodes:
    Python Powered Journalistic Freedom With SecureDrop Sep 10, 2019
    Show notes

    Summary

    The internet has made it easier than ever to share information, but at the same time it has increased our ability to track that information. In order to ensure that news agencies are able to accept truly anonymous material submissions from whistelblowers, the Freedom of the Press foundation has supported the ongoing development and maintenance of the SecureDrop platform. In this episode core developers of the project explain what it is, how it protects the privacy and identity of journalistic sources, and some of the challenges associated with ensuring its security. This was an interesting look at the amount of effort that is required to avoid tracking in the modern era.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/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 as usual is Tobias Macey and today I’m interviewing Jen Helsby and Kushal Das about SecureDrop, a secure platform for submitting and receiving documents anonymously

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what SecureDrop is and how it got started?
      • How did you get involved in the project?
    • Can you give some background on where and why it is useful?
    • For someone using a running instance, what does their workflow look like?
      • What are some of the ways that you minimize user experience hurdles to prevent them from circumventing the security through laziness or apathy?
    • I was a bit surprised to see the references to the messaging system that is included. Why is that an important feature?
    • What form do the submissions generally take and what are the limits on formats that you can accept?
    • How is the system itself architected and how has the design evolved since the first implementation?
    • In terms of the security protocols and technologies that are implemented, what factors are you considering as you develop the project?
      • What are the weak points or edge cases that could lead to compromise and how do you guard against them?
    • In terms of the deployment and maintenance of a SecureDrop instance, how much technological sophistication is necessary for the organization running it, and how much effort do you put into simplifying it?
    • What are some of the notable uses of a SecureDrop deployment and what motivates you to continue working on it?
    • What are the most interesting/innovative/unexpected uses of SecureDrop that you have seen?
    • How do you approach the sustainability of the platform?
    • What have you found most challenging/interested/unexpected in your work on SecureDrop?
    • What is in store for the future of the project?

    Keep In Touch

    • Jen
      • @redshiftzero on Twitter
      • redshiftzero on GitHub
      • Blog
    • Kushal
      • Website
      • @kushaldas on Twitter
      • kushaldas on GitHub

    Picks

    • Tobias
      • Laser Tag
    • Kushal
      • Permanent Record by Edward Snowden
    • Jen
      • Permanent Record by Edward Snowden

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • SecureDrop
    • Aaron Swartz
    • Freedom Of The Press Foundation
    • SecureDrop Directory
    • TOR Browser
    • TOR == The Onion Router
    • Tails OS
    • Ubuntu
    • IDS == Intrusion Detection System
    • Ansible
    • DEF CON
    • Mozilla Open Source Support (MOSS)
    • Testinfra
    • Flask
    • Molecule unit test library for Ansible
    • Bandit
    • Safety
    • Qubes OS
    • Qt

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Combining Python And SQL To Build A PyData Warehouse Sep 02, 2019
    Show notes

    Summary

    The ecosystem of tools and libraries in Python for data manipulation and analytics is truly impressive, and continues to grow. There are, however, gaps in their utility that can be filled by the capabilities of a data warehouse. In this episode Robert Hodges discusses how the PyData suite of tools can be paired with a data warehouse for an analytics pipeline that is more robust than either can provide on their own. This is a great introduction to what differentiates a data warehouse from a relational database and ways that you can think differently about running your analytical workloads for larger volumes of data.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • Taking a look at recent trends in the data science and analytics landscape, it’s becoming increasingly advantageous to have a deep understanding of both SQL and Python. A hybrid model of analytics can achieve a more harmonious relationship between the two languages. Read more about the Python and SQL Intersection in Analytics at mode.com/init. Specifically, we’re going to be focusing on their similarities, rather than their differences.
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/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 as usual is Tobias Macey and today I’m interviewing Robert Hodges about how the PyData ecosystem can play nicely with data warehouses

    Interview

    • Introductions
    • How did you get introduced to Python?
    • To start with, can you give a quick overview of what a data warehouse is and how it differs from a "regular" database for anyone who isn’t familiar with them?
      • What are the cases where a data warehouse would be preferable and when are they the wrong choice?
    • What capabilities does a data warehouse add to the PyData ecosystem?
    • For someone who doesn’t yet have a warehouse, what are some of the differentiating factors among the systems that are available?
    • Once you have a data warehouse deployed, how does it get populated and how does Python fit into that workflow?
    • For an analyst or data scientist, how might they interact with the data warehouse and what tools would they use to do so?
    • What are some potential bottlenecks when dealing with the volumes of data that can be contained in a warehouse within Python?
      • What are some ways that you have found to scale beyond those bottlenecks?
    • How does the data warehouse fit into the workflow for a machine learning or artificial intelligence project?
    • What are some of the limitations of data warehouses in the context of the Python ecosystem?
    • What are some of the trends that you see going forward for the integration of the PyData stack with data warehouses?
      • What are some challenges that you anticipate the industry running into in the process?
    • What are some useful references that you would recommend for anyone who wants to dig deeper into this topic?

    Keep In Touch

    • LinkedIn
    • hodgesrm on GitHub

    Picks

    • Tobias
      • Foundations Of Architecting Data Solutions: Managing Successful Data Projects by Ted Malaska & Jonathan Seidman
    • Robert
      • Reading old academic papers such as CStore
      • Python Machine Learning by Sebastian Raschka

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Altinity
    • Clickhouse
      • Data Engineering Podcast Interview
    • MySQL
    • Data Warehouse
    • Column Oriented Database
    • SIMD == Single Instruction Multiple Data
    • PostgreSQL
      • Data Engineering Podcast Episode
    • Microsoft SQL Server
    • Pandas
    • NumPy
    • Tensorflow
    • Jupyter
    • Data Sampling
    • Dask
      • Data Engineering Podcast
    • Ray
    • Map/Reduce
    • Vertica
    • Sharding
    • Hadoop
    • SnowflakeDB
    • Delta Lake
      • Data Engineering Podcast Episode
    • BigQuery
    • RedShift
    • Snowflake Data Sharing
    • OracleDB
    • Kubernetes
    • DBT
      • Data Engineering Podcast Episode
    • CSV
    • Parquet
      • Data Engineering Podcast Episode
    • Kafka
    • UC Davis
    • Web Scraping
    • Clickhouse Python Driver
    • SQLAlchemy
      • Altinity Blog Post
    • Materialized View
    • PyTorch
      • Podcast Interview
    • scikit-learn
    • Spark
      • Data Engineering Podcast Interview
    • BigQuery ML
    • Apache Arrow
    • Wes McKinney
      • Podcast Interview
    • User Defined Function
    • KDB
    • CStore Paper by Dr. Michael Stonebraker, et al
    • Kinetica
    AI Driven Automated Code Review With DeepCode Aug 26, 2019
    Show notes

    Summary

    Software engineers are frequently faced with problems that have been fixed by other developers in different projects. The challenge is how and when to surface that information in a way that increases their efficiency and avoids wasted effort. DeepCode is an automated code review platform that was built to solve this problem by training a model on a massive array of open sourced code and the history of their bug and security fixes. In this episode their CEO Boris Paskalev explains how the company got started, how they build and maintain the models that provide suggestions for improving your code changes, and how it integrates into your workflow.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/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 as usual is Tobias Macey and today I’m interviewing Boris Paskalev about DeepCode, an automated code review platform for detecting security vulnerabilities in your projects

    Interview

    • Introductions
    • Can you start by explaining what DeepCode is and the story of how it got started?
    • How is the DeepCode platform implemented?
    • What are the current languages that you support and what was your guiding principle in selecting them?
      • What languages are you targeting next?
      • What is involved in maintaining support for languages as they release new versions with new features?
        • How do you ensure that the recommendations that you are making are not using languages features that are not available in the runtimes that a given project is using?
    • For someone who is using DeepCode, how does it fit into their workflow?
    • Can you explain the process that you use for training your models?
      • How do you curate and prepare the project sources that you use to power your models?
        • How much domain expertise is necessary to identify the faults that you are trying to detect?
        • What types of labelling do you perform to ensure that the resulting models are focusing on the proper aspects of the source repositories?
    • How do you guard against false positives and false negatives in your analysis and recommendations?
    • Does the code that you are analyzing and the resulting fixes act as a feedback mechanism for a reinforcement learning system to update your models?
      • How do you guard against leaking intellectual property of your scanned code when surfacing recommendations?
    • What have been some of the most interesting/unexpected/challenging aspects of building the DeepCode product?
    • What do you have planned for the future of the platform and business?

    Keep In Touch

    • LinkedIn

    Picks

    • Tobias
      • Redwall Series by Brian Jacques
    • Boris
      • Artifical Intelligence
      • Get outside

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • DeepCode
    • Zurich, Switzerland
    • BigCode
    • ETH Zurich
    • Datalog
    • F Strings
    • Data Classes
    • DeepCode Research

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Security, UX, and Sustainability For The Python Package Index Aug 19, 2019
    Show notes

    Summary

    PyPI is a core component of the Python ecosystem that most developer’s have interacted with as either a producer or a consumer. But have you ever thought deeply about how it is implemented, who designs those interactions, and how it is secured? In this episode Nicole Harris and William Woodruff discuss their recent work to add new security capabilities and improve the overall accessibility and user experience. It is a worthwhile exercise to consider how much effort goes into making sure that we don’t have to think much about this piece of infrastructure that we all rely on.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, Corinium Global Intelligence, and Data Counsil. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
    • Your host as usual is Tobias Macey and today I’m interviewing Nicole Harris and William Woodruff about the work they are doing on the PyPI service to improve the security and utility of the package repository that we all rely on

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by sharing how you each got involved in working on PyPI?
      • What was the state of the system at the time that you first began working on it?
    • Once you committed to working on PyPI how did you each approach the process of identifying and prioritizing the work that needed to be done?
      • What were the most significant issues that you were faced with at the outset?
    • How often have the issues that you each focused on overlapped at the cross section of UX and security?
      • How do you balance the tradeoffs that exist at that boundary?
    • What is the surface area of the domains that you are each working in? (e.g. web UI, system API, data integrity, platform support, etc.)
      • What are some of the pain points or areas of confusion from a user perspective that you have dealt with in the process of improving the platform?
    • What have been the most notable features or improvements that you have each introduced to PyPI?
      • What were the biggest challenges with implementing or integrating those changes?
    • How do you approach introducing changes to PyPI given the volume of traffic that it needs to support and the level of importance that it serves in the community?
    • What are some examples of attack vectors that exist as a result of the nature of the PyPI platform and what are you most concerned by?
    • How does poor accessibility or user experience impact the utility of PyPI and the community members who interact with it?
    • What have you found to be the most interesting/challenging/unexpected aspects of working on Warehouse?
      • What are some of the most useful lessons that you have learned in the process?
    • What do you have planned for future improvements to the platform?
      • How can the listeners get involved and help out?
    • How was this work funded?

    Keep In Touch

    • Nicole
      • @nlhkabu on Twitter
      • Website
      • If you’re using CI to upload to PyPI and would like to speak with Nicole please book a time here
      • If you’re using assistive technology and would like to speak with Nicole please book a time here
    • William
      • @8x5clPW2
      • Website
      • Email
      • Please get in touch if you’d like to work with Trail of Bits on your next security project!

    Picks

    • Tobias
      • The Expanse TV Series
    • Nicole
      • The Great Hack documentary
    • William
      • Abraham Lincoln Autobiography by Carl Sandburg

    Links

    • PyPI
    • Warehouse
      • Issue Tracker
      • Good First Issues
    • PeopleDoc
    • Trail of Bits
    • OSQuery
    • Django
    • Ruby
    • Python Software Foundation
      • Python Packaging Working Group
      • Podcast Episode
    • Donald Stufft
      • Podcast Episode
    • UX (User Experience) Design
    • OTF == Open Technology Fund
    • Bootstrap
    • TOTP
    • WebauthN
    • Yubikey
    • Changeset Consulting
    • Sumana Harihareswara
    • WCAG (Web Content Accessibility Guidelines) 2.0
    • Macaroon Security Tokens
    • Docker Compose
    • MOSS = Mozilla Open Source Support

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Learning To Program In Python With CodeGrades Aug 12, 2019
    Show notes

    Summary

    With the increasing role of software in our world there has been an accompanying focus on teaching people to program. There are numerous approaches that have been attempted to achieve this goal with varying levels of success. Nicholas Tollervey has begun a new effort that blends the approach adopted by musicians and martial artists that uses a series of grades to provide recognition for the achievements of students. In this episode he explains how he has structured the study groups, syllabus, and evaluations to help learners build projects based on their interests and guide their own education while incorporating useful skills that are necessary for a career in software. If you are interested in learning to program, teach others, or act as a mentor then give this a listen and then get in touch with Nicholas to help make this endeavor a success.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, Corinium Global Intelligence. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
    • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
    • Your host as usual is Tobias Macey and today Nicholas Tollervey is back to talk about his work on CodeGrades, a new effort that he is building to blend his backgrounds in music, education, and software to help teach kids of all ages how to program.

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what CodeGrades is and what motivated you to start this project?
      • How does it differ from other approaches to teaching software development that you have encountered?
      • Is there a particular age or level of background knowledge that you are targeting with the curriculum that you are developing?
    • What are the criteria that you are measuring against and how does that criteria change as you progress in grade levels?
    • For someone who completes the full set of levels, what level of capability would you expect them to have as a developer?
    • Given your affiliation with the Python community it is understandable that you would target that language initially. What would be involved in adapting the curriculum, mentorship, and assessments to other languages?
      • In what other ways can this idea and platform be adapted to accomodate other engineering skills? (e.g. system administration, statistics, graphic design, etc.)
    • What interesting/exciting/unexpected outcomes and lessons have you found while iterating on this idea?
    • For engineers who would like to be involved in the CodeGrades platform, how can they contribute?
    • What challenges do you anticipate as you continue to develop the curriculum and mentor networks?
    • How do you envision the future of CodeGrades taking ship in the medium to long term?

    Keep In Touch

    • ntoll on GitHub
    • Website
    • @ntoll on Twitter

    Picks

    • Tobias
      • Parsy
      • Nevermoor: The Trials of Morrigan Crow
    • Nicholas
      • Kivy
      • Wittgenstein: The Duty Of Genious
      • The Hitchhiker’s Guide To The Galaxy by Douglas Adams

    Links

    • CodeGrades
    • Blog Post
    • C#
    • .NET
    • London
    • IronPython
    • Musical Grades
    • Autodidact
    • Lambda School
    • How To Draw An Owl
    • Dunder (double underscore) methods
    • Duck Typing
    • Impostor Syndrome
    • Django Girls
    • Mu Editor
    • Baroque Music
    • Chamber Music
    • PyData
    • Adafruit
    • CircuitPython
      • Podcast Interview
    • PyPortal
    • Hypercard
    • Pypercard
    • Kivy
      • Podcast Interview
    • Alan Turing

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Build Your Own Knowledge Graph With Zincbase Aug 05, 2019
    Show notes

    Summary

    Computers are excellent at following detailed instructions, but they have no capacity for understanding the information that they work with. Knowledge graphs are a way to approximate that capability by building connections between elements of data that allow us to discover new connections among disparate information sources that were previously uknown. In our day-to-day work we encounter many instances of knowledge graphs, but building them has long been a difficult endeavor. In order to make this technology more accessible Tom Grek built Zincbase. In this episode he explains his motivations for starting the project, how he uses it in his daily work, and how you can use it to create your own knowledge engine and begin discovering new insights of your own.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
    • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
    • Your host as usual is Tobias Macey and today I’m interviewing Tom Grek about knowledge graphs, when they’re useful, and his project Zincbase that makes them easier to build

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by explaining what a knowledge graph is and some of the ways that they are used?
      • How did you first get involved in the space of knowledge graphs?
    • You have built the Zincbase project for building and querying knowledge graphs. What was your motivation for creating this project and what are some of the other tools that are available to perform similar tasks?
    • Can you describe how Zincbase is implemented and some of the ways that it has evolved since you first began working on it?
      • What are some of the assumptions that you had at the outset of the project which have been challenged or updated in the process of working on and with it?
    • What are some of the common challenges when building or using knowledge graphs?
    • How has the domain of knowledge graphs changed in recent years as new approaches to entity resolution and data processing have been introduced?
    • Can you talk through a use case and workflow for using Zincbase to design and populate a knowledge graph?
    • What are some of the ways that you are using Zincbase in your own projects?
    • What have you found to be the most challenging/interesting/unexpected lessons that you have learned in the process of building and maintaining Zincbase?
    • What do you have planned for the future of the project?

    Keep In Touch

    • tomgrek on GitHub
    • Website
    • @tomgrek on Twitter
    • LinkedIn

    Picks

    • Tobias
      • Banana Blueberry Oat Bars
    • Tom
      • Pickled Habañero

    Links

    • Zincbase
    • Commodore 64
    • Electronic Engineering
    • Artificial Intelligence
    • Primer.ai
    • Artificial General Intelligence
    • Matlab
    • IPython
    • NumPy
    • Excel
    • Jupyter
    • Pandas
    • Knowledge Graph
      • Data Engineering Podcast Episode About Enigma Knowledge Graph
    • The Matrix
    • Keanu Reeves
    • Ontology
    • Semantic Web
    • Word2Vec
    • SparQL
    • Neo4J
    • Graph Database
      • Data Engineering Podcast Episode About DGraph
    • AWS Neptune
    • PostgreSQL
      • Data Engineering Podcast Episode
    • Dask
      • Data Engineering Podcast Episode
    • BBC Micro
    • BASIC
    • Prolog
    • NLP
    • ELMO
    • BERT
    • GPT-2
    • Winograd Schema Challenge
    • PyTorch BigGraph
    • Ampligraph
    • SpaCy
      • Podcast.__init__ Episode
    • AI Winter
    • PyTorch
      • Podcast Episode
    • scikit-learn
    • NetworkX
    • SciPy
    • CircleCI
    • Read The Docs
      • Podcast Episode
    • Project Gutenberg
    • Allen NLP
    • Doctest
    • Reinforcement Learning
    • Metacognition

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Docker Best Practices For Python In Production Jul 29, 2019
    Show notes

    Summary

    Docker is a useful technology for packaging and deploying software to production environments, but it also introduces a different set of complexities that need to be understood. In this episode Itamar Turner-Trauring shares best practices for running Python workloads in production using Docker. He also explains some of the security implications to be aware of and digs into ways that you can optimize your build process to cut down on wasted developer time. If you are using Docker, thinking about using it, or just heard of it recently then it is worth your time to listen and learn about some of the cases you might not have considered.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • To connect with the startups that are shaping the future and take advantage of the opportunities that they provide, check out Angel List where you can invest in innovative business, find a job, or post a position of your own. Sign up today at pythonpodcast.com/angel and help support this show.
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
    • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
    • Your host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about what you need to know about running Python workloads in Docker

    Interview

    • Introductions
    • How did you get introduced to Python?
    • For anyone who is unfamiliar with it, can you describe what Docker is and the benefits that it can provide?
    • What was your motivation for dedicating so much time and energy to the specific area of using Docker for Python production usage?
    • What are some of the common issues that developers and operations engineers run into when dealing with Docker and its build system?
    • What are some of the issues that are specific to Python that you have run into when using Docker?
    • How does the ecosystem for Python in containers compare to other languages that you are familiar with?
    • What are some of the security issues that engineers are likely to run into when using some of the advice and pre-existing containers that are publicly available?
    • One of the issues that you call out is the speed of container builds. What are some of the contributing factors that lead to such slow packaging times?
      • Can you talk through some of the aspects of multi-layer packages and useful ways to take proper advantage of them?
    • There have been some recent projects that attempt to work around the shortcomings of the Dockerfile itself. What are your thoughts on that overall effort and any specific tools that you have experimented with?
    • When is Docker the wrong choice for a production environment?
      • What are some useful alternatives to Docker, for Python specifically and for software distribution in general that you have had good luck with?

    Keep In Touch

    • Website
    • @itamarst on Twitter
    • itamarst on GitHub

    Picks

    • Tobias
      • Shazam Movie
    • Itamar
      • Veronica Mars

    Links

    • Itamar’s Best Practices Guide
    • Docker
    • Zope
    • GitLab CI
    • Heresy In The Church Of Docker
    • Poetry
    • Pipenv
    • Dockerfile
    • 40 Years of DSL Disasters (Slides)
    • Ubuntu
    • Debian
    • Docker Layers
    • Bitnami
    • Alpine Linuxhttps://alpinelinux.org?utm_source=rss&utm_medium=rss
    • PodMan
    • Nix
    • Heroku Buildpacks
    • Itamar’s Docker Template
    • Hashicorp Packer
    • Rkt
    • Solaris Zones
    • BSD Jails
    • PyInstaller
    • Snap
    • FlatPak
    • Conda

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Protecting The Future Of Python By Hunting Black Swans Jul 22, 2019
    Show notes

    Summary

    The Python language has seen exponential growth in popularity and usage over the past decade. This has been driven by industry trends such as the rise of data science and the continued growth of complex web applications. It is easy to think that there is no threat to the continued health of Python, its ecosystem, and its community, but there are always outside factors that may pose a threat in the long term. In this episode Russell Keith-Magee reprises his keynote from PyCon US in 2019 and shares his thoughts on potential black swan events and what we can do as engineers and as a community to guard against them.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • And to grow your professional network and find opportunities with the startups that are changing the world then Angel List is the place to go. Go to pythonpodcast.com/angel to sign up today.
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, and the Open Data Science Conference. Upcoming events include the O’Reilly AI Conference, the Strata Data Conference, and the combined events of the Data Architecture Summit and Graphorum. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
    • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
    • Your host as usual is Tobias Macey and today I’m interviewing Russell Keith-Magee about potential black swans for the Python language, ecosystem, and community and what we can do about them

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by explaining what a Black Swan is in the context of our conversation?
    • You were the opening keynote for PyCon this year, where you talked about some of the potential challenges facing Python. What motivated you to choose this topic for your presentation?
    • What effect did your talk have on the overall tone and focus of the conversations that you experienced during the rest of the conference?
      • What were some of the most notable or memorable reactions or pieces of feedback that you heard?
    • What are the biggest potential risks for the Python ecosystem that you have identified or discussed with others?
    • What is your overall sentiment about the potential for the future of Python?
    • As developers and technologists, does it really matter if Python continues to be a viable language?
    • What is your personal wish list of new capabilities or new directions for the future of the Python language and ecosystem?
    • For listeners to this podcast and members of the Python community, what are some of the ways that we can contribute to the long-term success of the language?

    Keep In Touch

    • BeeWare
    • freakboy3742 on GitHub
    • @freakboy3742 on Twitter
    • Website

    Picks

    • Tobias
      • Jethro Tull
    • Russell
      • pytest-tldr
      • pdbpp

    Links

    • PyCon 2019 Keynote Presentation
    • Perth
    • Western Australia
    • Django
    • BeeWare
    • RedHat
    • Emacs
    • Vim
    • Lisp
    • Glyph
    • Twisted
    • Cal Henderson
    • Flickr
    • Slack
    • Black Swan
      • Animal
      • Book
      • Metaphor
    • Nassim Nicholas Taleb
    • PyCon US
    • Ewa Jodlowska
    • Python Software Foundation
      • Podcast Interview
    • Swift
    • JavaScript
    • Django Girls
    • Briefcase packaging tool
    • PyPy
    • Web Assembly (WASM)
    • COBOL
    • Tidelift
    • Cricket unit test runner

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    A Modern Open Source Project Management Platform Jul 15, 2019
    Show notes

    Summary

    Project management is a discipline that has been through many incarnations, spawning an entire industry of businesses and tools. The challenge is to build a platform that is sufficiently powerful and adaptable to fit the workflow of your teams, while remaining opinionated enough to be useful. It also helps to have an open and extensible platform that can be customized as needed. In this episode Pablo Ruiz Múzquiz explains the motivation for creating the open source tool Taiga, how it compares to the other options in the market, and how you can use it for your own projects. He also discusses the challenges inherent to project management tools, his philosophies on what makes a project successful, and how to manage your team workflows to be most effective. It was helpful learning from Pablo’s long experience in the software industry and managing teams of various sizes.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
    • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
    • Your host as usual is Tobias Macey and today I’m interviewing Pablo Ruiz Múzquiz about Taiga, a project management platform for agile developers & designers and project managers who want a beautiful tool that makes work truly enjoyable

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by explaining what Taiga is and the reason for building it?
      • Project management platforms have been available for a long time. Can you describe how Taiga fits into that market and what makes it stand out?
    • Can you describe how you view project management and some of the unique challenges that it poses when building a tool for it?
      • How do the requirements differ between project management for software teams vs other disciplines?
    • How is Taiga implemented and how has the system design evolved since it was first started?
    • For someone who is using Taiga can you talk through the features of the platform and how it fits into a typical workflow?
    • How do you maintain a balance between usability and structure in managing project workflows against flexibility and customization?
    • Within an engineering team how do you view the responsibility for driving and maintaining the lifecycle of a project?
    • What are the most common points of friction within a project management workflow and how are you working to address them in Taiga?
      • Onboarding and discovery for a new contributor in a given project is often painful. What are some steps that a project manager or product team can take to make that process more palatable?
    • How has the landscape of project management practices and tools changed since you first began working on Taiga and how has that influenced your roadmap?
    • What have been the most challenging or difficult aspects of building and growing the Taiga project and community?
      • What lessons have you learned in the process that have been particularly valuable or unexpected?
    • What are some of the most interesting/unexpected/innovative ways that you have seen Taiga used?
    • When is Taiga the wrong choice for a given project or team?
    • What do you have planned for the future of Taiga?

    Added by Pablo

    1. Why did you choose AGPLv3 for a license?
    2. How can Taiga integrate itself with other platforms that are typically used by teams?

    Keep In Touch

    • @diacritica on Twitter
    • LinkedIn
    • Website

    Picks

    • Tobias
      • Marchway Hydration Pack
    • Pablo
      • Archery
      • 3D Archery

    Links

    • Taiga
    • Madrid, Spain
    • Traditional Archery
    • Kaleidos
    • Perl
    • Monty Python
    • Blender
    • Agile
    • Project Management
    • Redmine
    • Trac
    • Agile Manifesto
    • REST
    • Django
    • AngularJS
    • Django REST Framework
    • Scrum
    • Kanban
    • Taiga Mobile App
    • Webhooks
    • AGPLv3
    • FOSDEM
    • Iocaine
    • The Princess Bride
    • Taiga Tribe
    • Fedora
    • Atlassian
    • Jira
    • Trello

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Domain Driven Design For Python Jul 08, 2019
    Show notes

    Summary

    When your software projects start to scale it becomes a greater challenge to understand and maintain all of the pieces. In this episode Henry Percival shares his experiences working with domain driven design in large Python projects. He explains how it is helpful, and how you can start using it for your own applications. This was an informative conversation about software architecture patterns for large organizations and how they can be used by Python developers.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. 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, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
    • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
    • Your host as usual is Tobias Macey and today I’m interviewing Harry Percival about domain driven design and enterprise application architecture in Python

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by explaining what "application architecture" is and how it compares to the types of application designs that Python developers and teams typically rely on? how does it contrast with "enterprise architecture"?
      • What are the influences that tend to lead engineers into sub-optimal architectures and how can they guard against them?
    • One of the core concepts in this problem space is that of "domain driven design". Can you unpack that term and explain the benefits that it provides to software architecture?
    • What are some of the other concepts that are common among application architecture patterns?
    • What are some of the common points of confusion among engineers who are first working with DDD?
    • Is there any particular size or scope of project and organization that merits the approach of domain driven design or is it applicable even at small scales of complexity and team size?
    • Now that we’ve convinced everyone that they should be using DDD can you talk through the steps involved in identifying and encapsulating the various implementation details that they will need to work through?
      • How does that process change when dealing with an existing application as opposed to a "greenfield" project?
    • How do Python language constructs and libraries impact the approach to implementation of application architecture patterns as compared to more traditional "enterprise" languages such as Java and C#?
    • What are some of the architectural anti-patterns to watch out for when implementing DDD?
    • On any given team, who is responsible for identifying and ensuring adherence to proper architectural principles?
    • Are there any publicly visible projects that implement DDD which listeners can look at and learn from?
    • To help Python developers in their efforts to learn and implement DDD and other aspects of enterprise architecture you have been working on a book. Can you talk about your motivation for that undertaking, what listeners can expect to learn when the read it, and any challenges that you have encountered in the process?
    • What are some trends in terms of system design and architecture, or technology influences, that you are keeping an eye on?

    Keep In Touch

    • @hjwp on Twitter
    • hjwp on GitHub
    • Website
    • LinkedIn

    Picks

    • Tobias
      • Dragon Pearl by Yoon Ha Lee
    • Harry
      • Tremé
      • Why We Sleep: Unlocking The Power Of Sleep and Dreams by Matthew Walker PhD

    Links

    • MADE
    • Obey The Testing Goat
    • Python Anywhere
    • XP (eXtreme Programming)
    • Django
    • Dive Into Python
    • Domain Driven Design
    • Design Patterns
    • Gang Of Four Book
    • MVC (Model View Controller)
    • Microservices
    • µCon
    • "Uncle" Bob Martin
    • Clean Architecture book
    • Python LEAP Book
    • Dependency Injection
    • Inversion Of Control
    • Test Pyramid
    • Gary Bernhardt
      • Podcast Interview
      • Functional Core, Imperative Shell
    • Harry’s Blog
    • The "Blue" Book by Eric Evans
    • Gartner Hype Cycle
    • The Clean Architecture In Python by Leonardo Giordani
    • DRY Python

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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