logo
episode-header-image
Mar 2021
58 m

Data Quality Management For The Whole Te...

Tobias Macey
About this episode

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.

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.
  • 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
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

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

Support Data Engineering Podcast

Up next
Jul 6
Foundational Data Engineering At 2Sigma
SummaryIn this episode of the Data Engineering Podcast Effie Baram, a leader in foundational data engineering at Two Sigma, talks about the complexities and innovations in data engineering within the finance sector. She discusses the critical role of data at Two Sigma, balancing ... Show More
55m 5s
Jun 29
Enabling Agents In The Enterprise With A Platform Approach
SummaryIn this episode of the Data Engineering Podcast Arun Joseph talks about developing and implementing agent platforms to empower businesses with agentic capabilities. From leading AI engineering at Deutsche Telekom to his current entrepreneurial venture focused on multi-agen ... Show More
54m 18s
Jun 18
Dagster's New Era: Modularizing Data Transformation in the Age of AI
SummaryIn this episode of the Data Engineering Podcast we welcome back Nick Schrock, CTO and founder of Dagster Labs, to discuss the evolving landscape of data engineering in the age of AI. As AI begins to impact data platforms and the role of data engineers, Nick shares his insi ... Show More
1h 1m
Recommended Episodes
Jul 2022
IoT, IIoT and Managing Edge Data
Brian Gilmore (@BrianMGilmore, Director IoT/Emerging Technology @InfluxDB) talks about Edge and Industrial Edge Computing, as well as application and data challenges at the edge.SHOW: 634CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST - "CLOUDCAST ... Show More
35m 37s
Nov 2024
#262 Self-Service Business Intelligence with Sameer Al-Sakran, CEO at Metabase
We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here.We’re often caught chasing the dream of “self-serve” data—a place where data empowers stakeholders to answer th ... Show More
51m 33s
Apr 2023
2344: Cloudera: Moving Beyond Big Data to Hybrid Data Mastery
I sit down with Chris Royles, EMEA Field CTO at Cloudera, to discuss the evolution of Big Data and why hybrid data is the next challenge for businesses to tackle. In this episode, we explore how the term 'Big Data' has become dated and how the rapid rise of hybrid data has shifte ... Show More
39m 54s
Nov 2024
#259 Getting the Data For Your Data-Driven Decisions with Jonathan Bloch & Scott Voigt
We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here.Understanding where the data you use comes from, how to use it responsibly, and how to maximize its value has b ... Show More
46m 16s
Feb 2025
How Can GenAI Make Analytics More Accessible to Product Teams? (with Mario Ciabarra)
Whether you prefer the term data-driven, or data-informed, or data-dazzled, it doesn't matter—today's tech cannot survive without high quality data sets AND the tools to use them effectively. But we also can't afford to think about data as the responsibility of jus ... Show More
27m 46s
Feb 2025
Building Data Excellence at Nordstrom: Scaling Standards & Measurement for Impact
In this episode of the Data Science Salon Podcast, host Anna Anisin sits down with two data leaders from Nordstrom to explore how organizations can build a culture of technical excellence and measurement in data science. First, Gina Schmalzle, Principal Data Scientist at Nordstro ... Show More
34m 50s
Oct 2024
Understanding the World: The Power of Data
If money makes the world go round, then data tells you how fast it’s spinning and when it might stop. 90% of all data was generated in the last 2 years and every 2 years the volume of data doubles. With 11 billion devices connected to the internet today, the annual global data ge ... Show More
28m 54s
Jul 2024
803: How to Thrive in Your (Data Science) Career, with Daliana Liu
Daliana Liu is a big name in data science teaching, and she has always been generous in sharing everything she knows about getting a job in data science. In this episode, she continues to extend her generosity, helping listeners define their approach to achieving a fulfilling car ... Show More
1h 54m
Aug 2024
Containers at the Edge with David Aronchick
Large datasets require large computational resources to process that data. More frequently, where you process that data geographically can be just as important as how you process it. Expanso provides job execution infrastructure that runs jobs where data resides, to help reduce l ... Show More
40m 9s
Jan 2025
The Role of Analytics in Shaping the Future of MLOps
Sophia Rowland, Senior Product Manager at SAS, discusses her journey from data science to product management at SAS, focusing on the integration of AI and analytics. She explains the concepts of Model Ops and ML Ops, the challenges organizations face in operationalizing machine l ... Show More
32m 42s