One of the oldest aphorisms about data is "garbage in, garbage out", which is why the current boom in data quality solutions is no surprise. With the growth in projects, platforms, and services that aim to help you establish and maintain control of the health and reliability of your data pipelines it can be overwhelming to stay up to date with how they all compare. In this episode Egor Gryaznov, CTO of Bigeye, joins the show to explore the landscape of data quality companies, the general strategies that they are using, and what problems they solve. He also shares how his own product is designed and the challenges that are involved in building a system to help data engineers manage the complexity of a data platform. If you are wondering how to get better control of your own pipelines and the traps to avoid then this episode is definitely worth a listen.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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.
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- Your host is Tobias Macey and today I’m interviewing Egor Gryaznov about the state of the industry for data quality management and what he is building at Bigeye.
- How did you get involved in the area of data management?
- Can you start by sharing your views on what attributes you consider when defining data quality?
- You use the term "data semantics" – can you elaborate on what that means?
- What are the driving factors that contribute to the presence or lack of data quality in an organization or data platform?
- Why do you think now is the right time to focus on data quality as an industry?
- What are you building at Bigeye and how did it get started?
- How does Bigeye help teams understand and manage their data quality?
- What is the difference between existing data quality approaches and data observability?
- What do you see as the tradeoffs for the approach that you are taking at Bigeye?
- What are the most common data quality issues that you’ve seen and what are some more interesting ones that you wouldn’t expect?
- Where do you see Bigeye fitting into the data management landscape? What are alternatives to Bigeye?
- What are some of the most interesting, innovative, or unexpected ways that you have seen Bigeye being used?
- What are some of the most interesting homegrown approaches that you have seen?
- What have you found to be the most interesting, unexpected, or challenging lessons that you have learned while building the Bigeye platform and business?
- What are the biggest trends you’re following in data quality management?
- When is Bigeye the wrong choice?
- What do you see in store for the future of Bigeye?
- 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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