List of videos

"How we run GraphQL APIs in production on our Kubernetes cluster [EuroPython 2019 - Talk - 2019-07-12 - Singapore] [Basel, CH] By Alexys Jacob In this talk I would like to share the workflow and tools we use to build, deploy and operate GraphQL APIs on our on-premise Kubernetes cluster. I will share code and command examples explaining how we are operating our applications since our recent transition from REST APIs on Web servers to GraphQL APIs containers on Kubernetes. This talk will not be about the difference between REST and GraphQL but focus on the workflow, tools and experience we gained in switching our run time environments and API models. At Numberly, we have built and are operating our own on-premise Kubernetes cluster so we will also be talking about its capabilities and share some of the experience we gained in doing so. Proposed agenda: - Our previous workflow and its limitations - How we designed our Kubernetes cluster, its capabilities and the choices we made - Developer workflow, environments management and deployment - Our GraphQL stack, featuring a sample application - What we're still working on to improve License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/ "

"Evolving a helper script into a 180,000-lines-of-Python-code project [EuroPython 2019 - Talk - 2019-07-12 - Singapore] [Basel, CH] By Mikhail Bushkov, Max Vogler GRR Rapid Response (https://github.com/google/grr) is an incident response framework focused on remote live forensics. It consists of a Python client (agent) that is installed on target systems, and Python server infrastructure that can manage and talk to clients. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR was started at Google in 2009 as a simple Python helper script used by Incident Response engineers. Eventually a little Python script got a little server component, was adapted to run on multiple systems (Mac, Linux, Windows), then a little UI was added and a few nice features were introduced (large-scale hunts, collection of predefined artifacts, memory analysis). A helper script has eventually evolved into a sophisticated framework with 180,000 lines of Python code. In the presentation we’ll talk about the process of evolving a small prototype-like Python project into a production-ready system, using GRR as an example. The topics that we’ll cover are: * Taking shortcuts - both in terms of design and implementation. Reasons for taking them and their eventual costs. * Relying on Python’s power features (i.e. meta-classes, mixins)? Long-term consequences on maintainability and readability. * Organising the project into separate PyPI packages - benefits of doing that. * Continuous integration, testing and automated builds for various platforms - implementation costs and maintainability effects. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"The Agile comedy: from hell to paradise [EuroPython 2019 - Talk - 2019-07-12 - Shanghai] [Basel, CH] By Anastasiia Tymoshchuk Nowadays everyone wants to bring Agile Best Practices into all the teams, but it's a hard task to implement it and adapt based on different teams. Setting up all necessary Agile meetings and using buzz words is not enough for the team to be happy and successful while working in Agile environment. Inspired by the ""Divine Comedy"" by Dante Alighieri, I decided to create a short guide into Agile best practices. It will guide through Failures, Challenges to the Success in building a happy dream team! This talk is divided into three parts: 1. Failures in setting up Agile processes for a team. 2. Challenges on the way to happy and successful team. 3. Successful examples how to have happy and productive Agile team and constantly deliver a great product. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

Closing Session [EuroPython 2018 - - 2018-07-27 - Smarkets] [Edinburgh, UK] License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/

"Opening Session [EuroPython 2019 - - 2019-07-10 - MongoDB [PyData track] [Basel, CH] License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Getting Your Data Joie De Vivre Back! [EuroPython 2019 - Keynote - 2019-07-10 - MongoDB [PyData track] [Basel, CH] By Lynn Cherny Most of us work too much and play too little.  When was the last time you smiled at something you made?  Playing with fun datasets, especially big data sets, opens up weird new forms of technical recreation.  Why not train an amusing model in a browser tab while you're waiting for that day-job Spark query to finish?  I'll show you some data toys I've built using AI and interesting data sets: Most of them involve both backend data science and front-end visualization tricks.  They range from poetry-composition helpers to game log analysis to image deconstruction and reconstruction. All of them taught me something, often about myself and what I like artistically, and sometimes about what ""big data"" actually means. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Wait, IPython can do that?! [EuroPython 2019 - Talk - 2019-07-10 - Shanghai] [Basel, CH] By Sebastian Witowski I've been programming in Python for over 7 years. And from the time someone introduced me to IPython, it immediately became my default Python REPL. I enjoyed the syntax highlighting, code completion or the instant access to the documentation. Over the years, I've picked up more and more tricks that IPython could do: embedding IPython sessions, starting IPython automatically when a test fails, taking advantage of the startup files, not to mention all the various magic functions. It took me some time to discover all those features (if there is one thing I would like you to take away from this talk, it’s that reading the documentation of a tool that you are using emevery day/em is probably a good idea) and in this talk, I will give an overview of what you can do with IPython REPL. Some of the things I will talk about: - Documentation with ? and ??, running shell functions in IPython, magic functions - Profiles, startup files, configuration file, kernels, and extensions - How to better use IPython for debugging - How to use IPython for profiling - Alternatives to IPython REPL This talk is for you if you are a casual user of IPython - you know some of its features, but maybe not all of them and instead of reading through the documentation, you would prefer to see someone giving an overview of IPython features and how you can use them. This talk is NOT for you if you are up to date with IPython documentation and you think you know most of its features. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/ "

"What about recommendation engines? [EuroPython 2019 - Talk - 2019-07-10 - Singapore [PyData track] [Basel, CH] By Adriana Dorneles How recommendation engines are taking part in our daily routine and how companies as Netflix and Amazon implement it? This talk aims to show the elements that compound a recommendation engine to people who have never been in touch with the matter or want to know a bit more. At the end of this session, you might be able to reproduce your own recommendation system and also know where to find more about it. Talk structure: 1. What is and why use a recommendation engine? 2. Recommendation engine importance 3. Steps of a recommendation 4. Recommendation algorithms 5. Basic Statistics for distance and correlation 6. Example License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Python vs Rust for Simulation [EuroPython 2019 - Talk - 2019-07-10 - PyCharm] [Basel, CH] By Alisa Dammer Both Python and Rust are getting more and more popularity. Although it is unfair to compare them directly, this talk aims to provide a clear comparison with a pre-defined criteria applied to a specific use-case. Writing a simulation engine is very similar to writing a game engine and requires certain features from a language or framework. Possible comparison criteria: 1. Performance 2. Simplicity 3. Amount of code necessary for a MVP 4. Utilities: docs, tests, profile 5. Compatibility Tl;dr Do I want to go back to Python after Rust? Maybe+) License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/