Metrics are the most important primitive in the data world and driving the use of powerful and reliable metrics is the best way data teams can add value to their enterprises. In this talk, we'll walk through how data teams can best support the metric lifecycle, end-to-end from:

1. Designing useful metrics as part of metric trees
2. Developing these metrics off stable and standard data contracts
3. Operationalizing metrics to drive value

ABOUT THE SPEAKER: Abhi Sivasailam is a Growth and Analytics leader who most recently led Product-Led Growth, Product Analytics, and Analytics Engineering at Flexport, where he helped to lead these and other functions through 10x growth over the past 3 years. Previously, Abhi led growth and data teams at Keap, Hustle, and Honeybook.

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Abhi is a growth and data leader, and an excellent Twitter follow. Most recently, he was Head of Growth and Analytics at Flexport, where he helped the company to grow 10x over the past 3 years. Previously, Abhi led growth and data teams at Keap, Hustle, and Honeybook. In this conversation with Tristan and Julia, Abhi explains his methodology for setting up a new growth data organization, and how you might be falling victim to the dreaded "arbitrary uniqueness" bug. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs

The "socio" is inseparable from the "technical". In fact, technological change often begets social and organizational change.

And in the data space, the technical changes that some now refer to as the "modern data stack" call for changes in how teams work with data, and in turn how data specialists work within those teams. Enter the Modern Data Team.

In this talk, Abhi Sivasailam will unpack the changing landscape of data roles and teams and what this looks like in action at Flexport. Come learn how Flexport approaches data contracts, management, and governance, and the central role that Analytics Engineers and Product Analysts play in these processes.

Check the slides here: https://docs.google.com/presentation/d/1Sgm3J6EkeKQf5D1MKopsLLAMOhAZ05CxDlei2mbDE90/edit#slide=id.g16424dcc8d3_0_1145

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Summary Data platforms are exemplified by a complex set of connections that are subject to a set of constantly evolving requirements. In order to make this a tractable problem it is necessary to define boundaries for communication between concerns, which brings with it the need to establish interface contracts for communicating across those boundaries. The recent move toward the data mesh as a formalized architecture that builds on this design provides the language that data teams need to make this a more organized effort. In this episode Abhi Sivasailam shares his experience designing and implementing a data mesh solution with his team at Flexport, and the importance of defining and enforcing data contracts that are implemented at those domain boundaries.

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. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m interviewing Abhi Sivasailam about the different social and technical interfaces available for defining and enforcing data contracts

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what your working definition of a "data contract" is?

What are the goals and purpose of these contracts?

What are the locations and methods of defining a data contract?

What kind of information needs to be encoded in a contract definition?

How do you manage enforcement of contracts? manifestations of contracts in data mesh implementation ergonomics (technical and social) of data contracts and how to prevent them from prohibiting productivity What are the most interesting, innovative