Summary

One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. With the improvements in streaming engines it is now possible to perform all of your data integration in near real time, but it can be challenging to understand the proper processing patterns to make that performant. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today 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!
Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold.
Your host is Tobias Macey and today I’m interviewing Ori Rafael about strategies for building stream and batch processing patterns for data lake analytics

Interview

Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of the state of the market for data lakes today?
- What are the prevailing architectural and technological patterns that are being used to manage these systems?
Batch and streaming systems have been used in various combinations since the early days of Hadoop. The Lambda architecture has largely been abandoned, so what is the answer for today’s data lakes?
What are the challenges presented by streaming approaches to data transformations?
- The batch model for processing is intuitive despite its latency problems. What are the benefits that it provides?
The core concept for data orchestration is the DAG. How does that manifest in a streaming context?
In batch processing idempotent/immutable datasets are created by re-running the entire pipeline when logic changes need to be made. Given that there is no definitive start or end of a stream, what are the options for amending logical errors in transformations?
What are some of the data processing/integration patterns that are impossible in a batch system?
What are some useful strategies for migrating from a purely batch, or hybrid batch and streaming architecture, to a purely streaming system?
- What are some of the changes in technological or organizational patterns that are often overlooked or misunderstood in this shift?
What are some of the most surprising things that you have learned about streaming systems in your time at Upsolver?
What are the most interesting, innovative, or unexpected ways that you have seen streaming architectures used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on streaming data integration?
When are streaming architectures the wrong approach?
What do you have planned for the future of Upsolver to make streaming data easier to work with?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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@dataengineeringpodcast.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 The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

WordPress Briefing - A WordPress Podcast

Episode 36: Beginner’s Guide to Contributions 2.0

Remember Everything You Learn from Podcasts