Data Engineering Podcast

Make Your Business Metrics Reusable With Open Source Headless BI Using Metriql

Oct 8, 2021

43:37

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Highlights

AI Chapters

Episode notes

Data Modelling for Business People

02:11

How to Implement a Metrical Model in a Divity Environment?

02:02

Infer Structure, What Are Some of the Potential Improvements?

02:44

Introduction

2min

Metrical, a Headless B I and Metric Layer for Your Data Stack

4min

Is There a Headless Metric Store?

2min

Open Source

2min

Is There a Difference Between Minerva and Metrical?

4min

Is There Any Impact on the Usage of Metrics?

2min

The Limitations of the Data Consumption Layer

3min

The Hardest Part of the Sequence Parsing Solution

3min

Data Fold Pro Active Approach to Data Quality

4min

10.

Building a Data Warehouse Using DVTS Experience

4min

11.

Data Modelling for Business People

2min

12.

How to Implement a Metrical Model in a Divity Environment?

2min

13.

Infer Structure, What Are Some of the Potential Improvements?

3min

14.

Metricad - What Is the Worst Choice?

2min

15.

The Biggest Gap in Data Management

4min

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.

Announcements

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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?