Make Your Business Metrics Reusable With Open Source Headless BI Using Metriql
Data Engineering Podcast
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Data Modelling for Business People
Business users are able to identify or define their own set of metrics and then expose that back to metriqua. We also have different reporting types, like sea cool, ncool, segmentation, funnel, et cetera. The data people write these omal files and then commit it into their giepo stury - which is not possible in tableau. That's how we are helping to business people.
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Summary
The key to making data valuable to business users is the ability to calculate meaningful metrics and explore them along useful dimensions. Business intelligence tools have provided this capability for years, but they don’t offer a means of exposing those metrics to other systems. Metriql is an open source project that provides a headless BI system where you can define your metrics and share them with all of your other processes. In this episode Burak Kabakcı shares the story behind the project, how you can use it to create your metrics definitions, and the benefits of treating the semantic layer as a dedicated component of your platform.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Burak Emre Kabakcı about Metriql, a headless BI and metrics layer for your data stack
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Metriql is and the story behind it?
- What are the characteristics and benefits of a "headless BI" system?
- What was your motivation to create and open-source Metriql as an independent project outside of your business?
- How are you approaching governance and sustainability of the project?
- How does Metriql compare to projects such as AirBnB’s Minerva or Transform’s platform?
- How does the industry/vertical of a business impact their ability to benefit from a metrics layer/headless BI?
- What are the limitations to the logical complexity that can be applied to the calculation of a given metric/set of metrics?
- Can you describe how Metriql is implemented?
- How have the design and goals of the project changed or evolved since you began working on it?
- What are the most complex/difficult engineering elements of building a metrics layer?
- Can you describe the workflow of defining metrics?
- What have been your guiding principles in defining the user experience for working with metriql?
- What are the opportunities for including business users in the definition of metrics? (e.g. pushing down/generating definitions from a BI layer)
- What are the biggest challenges and limitations of creating metrics definitions purely in SQL?
- What are the options for exposing metrics back to the warehouse and other operational systems such as reverse ETL vendors?
- What are the missing elements in the data ecosystem for taking full advantage of a headless BI/metrics layer?
- What are the most interesting, innovative, or unexpected ways that you have seen Metriql used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Metriql?
- When is Metriql the wrong choice?
- What do you have planned for the future of Metriql?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Metriql
- Rakam
- Hazelcast
- Headless BI
- Google Data Studio
- Superset
- Trino
- Supergrain
- The Missing Piece Of The Modern Data Stack article by Benn Stancil
- Metabase
- dbt
- dbt-metabase
- re_data
- OpenMetadata
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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