Data Quality Management For The Whole Team With Soda Data - Episode 178

Summary

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 impact on the business if something goes wrong. In this episode Maarten Masschelein and Tom Baeyens share the work they are doing at Soda to bring everyone on board to make your data clean and reliable. They explain how they started down the path of building a solution for managing data quality, their philosophy of how to empower data engineers with well engineered open source tools that integrate with the rest of the platform, and how to bring all of the stakeholders onto the same page to make your data great. There are many aspects of data quality management and it’s always a treat to learn from people who are dedicating their time and energy to solving it for everyone.

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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!
  • 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. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.
  • RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today.
  • Your host is Tobias Macey and today I’m interviewing Maarten Masschelein and Tom Baeyens about the work are doing at Soda to power data quality management

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by giving an overview of what you are building at Soda?
  • What problem are you trying to solve?
  • And how are you solving that problem?
    • What motivated you to start a business focused on data monitoring and data quality?
  • The data monitoring and broader data quality space is a segment of the industry that is seeing a huge increase in attention recently. Can you share your perspective on the current state of the ecosystem and how your approach compares to other tools and products?
  • who have you created Soda for (e.g platform engineers, data engineers, data product owners etc) and what is a typical workflow for each of them?
  • How do you go about integrating Soda into your data infrastructure?
  • How has the Soda platform been architected?
  • Why is this architecture important?
    • How have the goals and design of the system changed or evolved as you worked with early customers and iterated toward your current state?
  • What are some of the challenges associated with the ongoing monitoring and testing of data?
  • what are some of the tools or techniques for data testing used in conjunction with Soda?
  • What are some of the most interesting, innovative, or unexpected ways that you have seen Soda being used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while building the technology and business for Soda?
  • When is Soda the wrong choice?
  • What do you have planned for the future?

Contact Info

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.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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