Bringing The Metrics Layer To The Masses With Transform

Summary Collecting and cleaning data is only useful if someone can make sense of it afterward. The latest evolution in the data ecosystem is the introduction of a dedicated metrics layer to help address the challenge of adding context and semantics to raw information. In this episode Nick Handel shares the story behind Transform, a new platform that provides a managed metrics layer for your data platform. He explains the challenges that occur when metrics are maintained across a variety of systems, the benefits of unifying them in a common access layer, and the potential that it unlocks for everyone in the business to confidently answer questions with data. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. 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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. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Nick Handel about Transform, a platform providing a dedicated metrics layer for your data stack Interview Introduction How did you get involved in the area of data management? Can you describe what Transform is and the story behind it? How do you define the concept of a "metric" in the context of the data platform? What are the general strategies in the industry for creating, managing, and consuming metrics? How has that been changing in the past couple of years? What is driving that shift? What are the main goals that you have for the Transform platform? Who are the target users? How does that focus influence your approach to the design of the platform? How is the Transform platform architected? What are the core capabilities that are required for a metrics service? What are the integration points for a metrics service? Can you talk through the workflow of defining and consuming metrics with Transform? What are the challenges that teams face in establishing consensus or a shared understanding around a given metric definition? What are the lifecycle stages that need to be factored into the long-term maintenance of a metric definition? What are some of the capabilities or projects that are made possible by having a metrics layer in the data platform? What are the capabilities in downstream tools that are currently missing or underdeveloped to support the metrics store as a core layer of the platform? What are the most interesting, innovative, or unexpected ways that you have seen Transform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Transform? When is Transform the wrong choice? What do you have planned for the future of Transform? Contact Info LinkedIn @nick_handel on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Transform Transform’s Metrics Framework Transform’s Metrics Catalog Transform’s Metrics API Nick’s experiences using Airbnb’s Metrics Store Get Transform BlackRock AirBnB Airflow Superset Podcast Episode AirBnB Knowledge Repo AirBnB Minerva Metric Store OLAP Cube Semantic Layer Master Data Management Podcast Episode Data Normalization OpenLineage The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast