Power Your Real-Time Analytics Without The Headache Using Fivetran's Change Data Capture Integrations

Summary Data integration from source systems to their downstream destinations is the foundational step for any data product. With the increasing expecation for information to be instantly accessible, it drives the need for reliable change data capture. The team at Fivetran have recently introduced that functionality to power real-time data products. In this episode Mark Van de Wiel explains how they integrated CDC functionality into their existing product, discusses the nuances of different approaches to change data capture from various sources. 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. 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Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing Mark Van de Wiel about Fivetran’s implementation of change data capture and the state of streaming data integration in the modern data stack Interview Introduction How did you get involved in the area of data management? What are some of the notable changes/advancements at Fivetran in the last 3 years? How has the scale and scope of usage for real-time data changed in that time? What are some of the differences in usage for real-time CDC data vs. event streams that have been the driving force for a large amount of real-time data? What are some of the architectural shifts that are necessary in an organizations data platform to take advantage of CDC data streams? What are some of the shifts in e.g. cloud data warehouses that have happened/are happening to allow for ingestion and timely processing of these data feeds? What are some of the different ways that CDC is implemented in different source systems? What are some of the ways that CDC principles might start to bleed into e.g. APIs/SaaS systems to allow for more unified processing patterns across data sources? What are some of the architectural/design changes that you have had to make to provide CDC for your customers at Fivetran? What are the most interesting, innovative, or unexpected ways that you have seen CDC used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDC at Fivetran? When is CDC the wrong choice? What do you have planned for the future of CDC at Fivetran? Contact Info LinkedIn 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. 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 [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Fivetran Podcast Episode HVR Software Change Data Capture Debezium Podcast Episode LogMiner Materialize Podcast Episode Kafka Kinesis dbt Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast