Leading The Charge For The ELT Data Integration Pattern For Cloud Data Warehouses At Matillion

Summary The predominant pattern for data integration in the cloud has become extract, load, and then transform or ELT. Matillion was an early innovator of that approach and in this episode CTO Ed Thompson explains how they have evolved the platform to keep pace with the rapidly changing ecosystem. He describes how the platform is architected, the challenges related to selling cloud technologies into enterprise organizations, and how you can adopt Matillion for your own workflows to reduce the maintenance burden of data integration workflows. 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! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. 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Your host is Tobias Macey and today I’m interviewing Ed Thompson about Matillion, a cloud-native data integration platform for accelerating your time to analytics Interview Introduction How did you get involved in the area of data management? Can you describe what Matillion is and the story behind it? What are the use cases and user personas that you are focused on supporting? How does that influence the focus and pace of your feature development and priorities? How is Matillion architected? How have the design and goals of the system changed since you started working on it? The ecosystems of both cloud technologies and data processing have been rapidly growing and evolving, with new patterns and paradigms being introduced. What are the elements of your product focus and messaging that you have had to update and what are the core principles that have stayed the same? What have been the most challenging integrations to build and support? What is a typical workflow for integrating Matillion into an organization and building a set of pipelines? What are some of the patterns that have been useful for managing incidental complexity as usage scales? What are the most interesting, innovative, or unexpected ways that you have seen Matillion used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Matillion? When is Matillion the wrong choice? What do you have planned for the future of Matillion? Contact Info LinkedIn Matillion Contact 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 [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Matillion Twitter IBM DB2 Cognos Talend Redshift AWS Marketplace AWS Re:Invent Azure GCP == Google Cloud Platform Informatica SSIS == SQL Server Integration Services PCRE == Perl Compatible Regular Expressions Teradata Tomcat Collibra Alation The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast