Data quality is a concern that has been gaining attention alongside the rising importance of analytics for business success. Many solutions rely on hand-coded rules for catching known bugs, or statistical analysis of records to detect anomalies retroactively. While those are useful tools, it is far better to prevent data errors before they become an outsized issue. In this episode Gleb Mezhanskiy shares some strategies for adding quality checks at every stage of your development and deployment workflow to identify and fix problematic changes to your data before they get to production.
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RudderStack is the smart customer data pipeline. It takes the toil out of building data pipelines that connect your whole customer data stack. Its easy-to-use SDKs and source integrations, Cloud Extract integrations, transformations, and expansive library of destination and warehouse integrations makes building customer data pipelines for both event streaming and cloud-to-warehouse ELT simple. RudderStack’s warehouse-first approach and Warehouse Actions functionality makes your customer data stack smarter by enabling analysis and modeling in your data warehouse to trigger enrichment and activation in all of your customer tools. Start building smarter customer data pipelines today with RudderStack. Visit dataengineeringpodcast.com/rudder to learn more and sign-up for our no credit card required, no time limit free tier.
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- 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.
- We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial.
- Your host is Tobias Macey and today I’m interviewing Gleb Mezhanskiy about strategies for proactive data quality management and his work at Datafold to help provide tools for implementing them
- How did you get involved in the area of data management?
- Can you describe what you are building at Datafold and the story behind it?
- What are the biggest factors that you see contributing to data quality issues?
- How are teams identifying and addressing those failures?
- How does the data platform architecture impact the potential for introducing quality problems?
- What are some of the potential risks or consequences of introducing errors in data processing?
- How can organizations shift to being proactive in their data quality management?
- How much of a role does tooling play in addressing the introduction and remediation of data quality problems?
- Can you describe how Datafold is designed and architected to allow for proactive management of data quality?
- What are some of the original goals and assumptions about how to empower teams to improve data quality that have been challenged or changed as you have worked through building Datafold?
- What is the workflow for an individual or team who is using Datafold as part of their data pipeline and platform development?
- What are the organizational patterns that you have found to be most conducive to proactive data quality management?
- Who is responsible for identifying and addressing quality issues?
- What are the most interesting, innovative, or unexpected ways that you have seen Datafold used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datafold?
- When is Datafold the wrong choice?
- What do you have planned for the future of Datafold?
- 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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