talk-data.com

Abstract Metrics are an emerging type of BI artifact that packages data into a format optimized for consumption by non-technical business users. Over the last few years two types of technologies, the semantic layer and metric layer, have arisen independently. They both support the creation of metrics.

In this talk, you will learn: 👉 Which needs do metric and semantic layers address - for data teams and business teams? 👉 What differentiates these technologies and why do they have such similar capabilities? 👉How can these technologies be used together in BI tools to take advantage of the strengths of both? Speaker: Graham Watts, Senior Software Architect @Klipfolio

Panel Discussion Thought leaders will discuss the opportunities for data teams and how these technologies will shape the future of BI. Panelists:

• Nick Handel, Director of Product @dbt Labs
• Previously Co-Founder and CEO of Transform. In his early days as a Data Scientist at Airbnb, Nick learned that most businesses were continuing to track the same North Star metrics from the very early days. This observation inspired him to work on a foundation for the revamped dbt Semantic Layer in his days at Transform.
• Magnus Dahlbäck, Senior Director Data and Platform @Voi
• Former Head of Data Engineering at Telia. Magnus is advocating for a new approach to making data-driven decisions: “Metrics are the core of running a data-driven business. However, most data teams continue to overlook this. Instead, there's too much emphasis on data and dashboards rather than on metrics.”
• Graham Watts, Senior Software Architect @Klipfolio
• As one of the first thought leaders and metric experts in the space, Graham has worked on and helped design the Klipfolio PowerMetrics metrics layer. He’s been a driving force behind building the first hybrid solution that combines the metric and semantic layer.

Abstract Metrics are an emerging type of BI artifact that packages data into a format optimized for consumption by non-technical business users. Over the last few years two types of technologies, the semantic layer and metric layer, have arisen independently. They both support the creation of metrics.

In this talk, you will learn: 👉 Which needs do metric and semantic layers address - for data teams and business teams? 👉 What differentiates these technologies and why do they have such similar capabilities? 👉How can these technologies be used together in BI tools to take advantage of the strengths of both? Speaker: Graham Watts, Senior Software Architect @Klipfolio

Panel Discussion Thought leaders will discuss the opportunities for data teams and how these technologies will shape the future of BI. Panelists:

• Nick Handel, Director of Product @dbt Labs
• Previously Co-Founder and CEO of Transform. In his early days as a Data Scientist at Airbnb, Nick learned that most businesses were continuing to track the same North Star metrics from the very early days. This observation inspired him to work on a foundation for the revamped dbt Semantic Layer in his days at Transform.
• Magnus Dahlbäck, Senior Director Data and Platform @Voi
• Former Head of Data Engineering at Telia. Magnus is advocating for a new approach to making data-driven decisions: “Metrics are the core of running a data-driven business. However, most data teams continue to overlook this. Instead, there's too much emphasis on data and dashboards rather than on metrics.”
• Graham Watts, Senior Software Architect @Klipfolio
• As one of the first thought leaders and metric experts in the space, Graham has worked on and helped design the Klipfolio PowerMetrics metrics layer. He’s been a driving force behind building the first hybrid solution that combines the metric and semantic layer.

With the dbt Semantic Layer, you can define metrics alongside your dbt models, and access them from any integrated analytics tool.

The Semantic Layer has been totally revamped. It’s now powered by MetricFlow, allowing for more complex metric definition and a significantly improved querying experience. Join the dbt Labs product team as they dive into what’s new, what’s different, and what it means for you.

Speakers: Nick Handel, Director, Product Management, dbt Labs; Roxi Pourzand, Product Manager, dbt Labs

Register for Coalesce at https://coalesce.getdbt.com

Nick Handel, as co-founder at Transform, helped develop the popular open source metrics framework MetricFlow. Drew Banin, a co-founder at dbt Labs, helped build the initial version of the dbt Semantic Layer, which launched last year.   Transform was acquired in February by dbt Labs, and in this conversation with Tristan, they talk through their collective plans for the future of the dbt Semantic Layer. For full show notes and to read 7+ 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.

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. 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 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 tha