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.
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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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
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?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
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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