April 07, 2024

Establish A Single Source Of Truth For Your Data Consumers With A Semantic Layer

56 minutes

Summary

Maintaining a single source of truth for your data is the biggest challenge in data engineering. Different roles and tasks in the business need their own ways to access and analyze the data in the organization. In order to enable this use case, while maintaining a single point of access, the semantic layer has evolved as a technological solution to the problem. In this episode Artyom Keydunov, creator of Cube, discusses the evolution and applications of the semantic layer as a component of your data platform, and how Cube provides speed and cost optimization for your data consumers.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management

This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your data workflow, from migration to dbt deployment. Datafold has recently launched data replication testing, providing ongoing validation for source-to-target replication. Leverage Datafold's fast cross-database data diffing and Monitoring to test your replication pipelines automatically and continuously. Validate consistency between source and target at any scale, and receive alerts about any discrepancies. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold.

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Your host is Tobias Macey and today I'm interviewing Artyom Keydunov about the role of the semantic layer in your data platform

Interview

Introduction

How did you get involved in the area of data management?

Can you start by outlining the technical elements of what it means to have a "semantic layer"?

In the past couple of years there was a rapid hype cycle around the "metrics layer" and "headless BI", which has largely faded. Can you give your assessment of the current state of the industry around the adoption/implementation of these concepts?

What are the benefits of having a discrete service that offers the business metrics/semantic mappings as opposed to implementing those concepts as part of a more general system? (e.g. dbt, BI, warehouse marts, etc.)

At what point does it become necessary/beneficial for a team to adopt such a service?

What are the challenges involved in retrofitting a semantic layer into a production data system?

evolution of requirements/usage patterns

technical complexities/performance and cost optimization

What are the most interesting, innovative, or unexpected ways that you have seen Cube used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cube?

When is Cube/a semantic layer the wrong choice?

What do you have planned for the future of Cube?

Contact Info

keydunov on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.

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Links

Cube

Semantic Layer

Business Objects

Tableau

Looker

Podcast Episode

Mode

Thoughtspot

LightDash

Podcast Episode

Embedded Analytics

Dimensional Modeling

Clickhouse

Podcast Episode

Druid

BigQuery

Starburst

Pinot

Snowflake

Podcast Episode

Arrow Datafusion

Metabase

Podcast Episode

Superset

Alation

Collibra

Podcast Episode

Atlan

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Starburst: ![Starburst Logo](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/UpvN7wDT.png)

This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics.

Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)

Datafold: ![Datafold](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/zm6x2tFu.png)

Dagster: ![Dagster Logo](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/jz4xfquZ.png)

Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage.

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