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 managementYou listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy!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!Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch.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. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscriptionYour host is Tobias Macey and today I’m interviewing Nick Handel about Transform, a platform providing a dedicated metrics layer for your data stackInterview
IntroductionHow 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 TwitterParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Links
TransformTransform’s Metrics FrameworkTransform’s Metrics CatalogTransform’s Metrics APINick’s experiences using Airbnb’s Metrics StoreGet TransformBlackRockAirBnBAirflowSupersetPodcast EpisodeAirBnB Knowledge RepoAirBnB Minerva Metric StoreOLAP CubeSemantic LayerMaster Data ManagementPodcast EpisodeData NormalizationOpenLineageThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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