About this episode
Summary
Communication is a fundamental requirement for any program or application. As the friction involved in deploying code has gone down, the motivation for architecting your system as microservices goes up. This shifts the communication patterns in your software from function calls to network calls. In this episode Idit Levine explains how the Gloo platform that she and her team at Solo have created makes it easier for you to configure and monitor the network topologies for your microservice environments. She also discusses what developers need to know about networking in cloud native environments and how a combination of API gateways and service mesh technologies allow you to more rapidly iterate on your systems.
Announcements
- Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
- 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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Your host as usual is Tobias Macey and today I’m interviewing Idit Levine about what developers need to know about service-oriented networking and her work at Solo on the Gloo project
Interview
- Introductions
- How did you get introduced to Python?
- Can you describe what Solo is and the story behind it?
- How much should developers need to know about the ways that their applications and services are communicating?
- What is the current state of networking for applications across physical, cloud, and containerized environments?
- How do service mesh features influence the architectural decisions that software teams make while building their applications?
- What operational capabilities do they unlock?
- What are the aspects of application networking that are simplified or enhanced by service mesh platforms?
- In what ways has service mesh introduced new complexity to operating software systems?
- How can developers mirror the network topologies for production environments while working on new features?
- What are the most interesting, innovative, or unexpected ways that you have seen Gloo used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gloo?
- When is Gloo the wrong choice?
- What do you have planned for the future of Gloo?
Keep In Touch
Picks
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
Links
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec 2022
Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
The majority of machine learning projects that you read about or work on are built around batch processes. The model i ... Show More
1h 16m
Dec 2022
Declarative Machine Learning For High Performance Deep Learning Models With Predibase
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference ... Show More
59m 22s
Nov 2022
Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the mo ... Show More
47m 37s
Jul 2025
Revolutionizing Python Notebooks with Marimo
SummaryIn this episode of the Data Engineering Podcast Akshay Agrawal from Marimo discusses the innovative new Python notebook environment, which offers a reactive execution model, full Python integration, and built-in UI elements to enhance the interactive computing experience. ... Show More
51m 56s
Feb 2025
#495: OSMnx: Python and OpenStreetMap
See the full show notes for this episode on the website at <a href="https://talkpython.fm/495">talkpython.fm/495</a>
1h 1m
Oct 11
Context Engineering as a Discipline: Building Governed AI Analytics
SummaryIn this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Nick Schrock, CTO and founder of Dagster Labs, to discuss Compass - a Slack-native, agentic analytics system designed to keep data teams connected with business stakeholders. Nick shares his j ... Show More
51m 58s
Sep 2021
An Exploration Of The Data Engineering Requirements For Bioinformatics
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Biology has been gaining a lot of attention in recent years, even before the pandemic. As an outgrowth of that popularity, a new field has grown up that pairs statistics and compuational analysis with scientific research ... Show More
55m 10s
Aug 26
From Academia to Industry: Bridging Data Engineering Challenges
SummaryIn this episode of the Data Engineering Podcast Professor Paul Groth, from the University of Amsterdam, talks about his research on knowledge graphs and data engineering. Paul shares his background in AI and data management, discussing the evolution of data provenance and ... Show More
50m 54s
May 2022
Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional la ... Show More
58m 11s
Aug 18
High Performance And Low Overhead Graphs With KuzuDB
SummaryIn this episode of the Data Engineering Podcast Prashanth Rao, an AI engineer at KuzuDB, talks about their embeddable graph database. Prashanth explains how KuzuDB addresses performance shortcomings in existing solutions through columnar storage and novel join algorithms. ... Show More
1h 1m
Mar 2021
Data Quality Management For The Whole Team With Soda Data
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Data quality is on the top of everyone’s mind recently, but getting it right is as challenging as ever. One of the contributing factors is the number of people who are involved in the process and the potential impa ... Show More
58 m
Aug 2024
The Evolution of DataOps: Insights from DataKitchen's CEO
Summary
In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Chris Berg, CEO of DataKitchen, to discuss his ongoing mission to simplify the lives of data engineers. Chris explains the challenges faced by data engineers, such as constant system failures ... Show More
53m 30s
Feb 2025
The Future of Data Engineering: AI, LLMs, and Automation
Summary
In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large langua ... Show More
59m 39s