This project intends to document requirements and referential material about the semantic layer (e.g., metrics, business rules) in the perspective of data engineering on a modern data stack (MDS). The semantic layer is also sometimes referred to as ontology.
Even though the members of the GitHub organization may be employed by some companies, they speak on their personal behalf and do not represent these companies.
- Data Engineering Helpers - Knowledge Sharing - Data products
- Data Engineering Helpers - Knowledge Sharing - Data contracts
- Data Engineering Helpers - Knowledge Sharing - Data quality
- Data Engineering Helpers - Knowledge Sharing - Architecture principles
- Data Engineering Helpers - Knowledge Sharing - Data life cycle
- Data Engineering Helpers - Knowledge Sharing - Data management
- Data Engineering Helpers - Knowledge Sharing - Data lakehouse
- Data Engineering Helpers - Knowledge Sharing - Metadata
- Data Engineering Helpers - Knowledge Sharing - Data pipeline deployment
More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area.
- Title: Unlocking the Power of Data in Biopharma: Why a Semantic Layer (Finally) Makes It Click
- Date: Aug. 2025
- Author: Nnaemezue (Mezue) Obi-Eyisi (Nnaemezue Obi-Eyisi on LinkedIn, Nnaemezue Obi-Eyisi on Medium)
- Blog post on Medium: https://afroinfotech.medium.com/unlocking-the-power-of-data-in-biopharma-why-a-semantic-layer-finally-makes-it-click-b179d4e88208
- Date: Aug. 2025
- Author: Matt Martin (Matt Martin on LinkedIn, Matt Martin's blog on Substack)
- Link to the post on LinkedIn: https://www.linkedin.com/posts/mattmartin14_duckdb-dataengineering-copilot-activity-7362431315020771328-OcDk/
- Verbatim:
I am finding that I'm starting to do a lot more charting in python notebooks now than I used to; reason being is its brain-dead easy to just put a quick prompt on copilot to build you something. As an example, if I have a duckdb query working for me and I want to chart the results, I can with this simple copilot prompt in the cell: "create a bar chart with matplotlib that shows order counts by month; add a trend line showing the average order count by month". In the screenshot below, you will notice that copilot is smart enough to interpret my natural language prompt and examine the SQL statement above and match my request to the actual fields (I did not have to provide a mapping whatsoever). This is literally done in less than a couple seconds and I did not have to go crack open another BI tool like Tableau.
This is been accelerating my Q/A of datasets much faster now; I'm finding less and less a need for traditional BI tools going forward.
- Title: Can You Build a Semantic Layer on Databricks Without a Third-Party Tool?
- Date: Jul. 2025
- Author: Nnaemezue (Mezue) Obi-Eyisi (Nnaemezue Obi-Eyisi on LinkedIn, Nnaemezue Obi-Eyisi on Medium)
- Blog post on Medium: https://afroinfotech.medium.com/unlocking-the-power-of-data-in-biopharma-why-a-semantic-layer-finally-makes-it-click-b179d4e88208
- Post on LinkedIn: https://www.linkedin.com/posts/nobieyisi_can-you-build-a-semantic-layer-on-databricks-activity-7349061083174772770-nt17/
- Title: Knowledge mesh and data products
- Date: Mar. 2025
- Author: Andrea Gioia (Andrea Gioia on LinkedIn)
- Link to the post on LinkedIn: https://www.linkedin.com/posts/andreagioia_thedatajoy-dataproducts-ontology-activity-7303666970078449664-shYT
- Title: What Syntax for the Semantic Layer
- Date: Mar. 2025
- Author: Benoit Pimpaud (Benoit Pimpaud on LinkedIn, Benoit Pimpaud on Medium)
- Link to the article on Medium: https://medium.pimpaudben.fr/what-syntax-for-the-semantic-layer-9cbc30976609
- Title: Analytics Heroes by Modern Data 101 with Matthew Weingarten
- Date: Jan. 2025
- Author: Matt Weingarten (Matt Weingarten on LinkedIn, Matt Weingarten on Medium)
- Hosted by: Abhyudai Verma (Abhyudai Verma on LinkedIn)
- Link to the article on LinkedIn: https://www.linkedin.com/posts/modern-data-101_matt-analytics-heroes-slider-activity-7291045398675181568-RGye
- Link to the introduction on Modern Data 101:
https://www.moderndata101.com/analytics-heroes/matthew-weingarten
- Link to the video on Youtube: https://youtu.be/ZYWMGMgoyYw
- Title: Metrics-Focused Data Strategy with Model-First Data Products
- Authors: Animesh Kumar, Shubhanshu Jain, Samadrita Ghosh, and Nikhil Singh
- Date: 28 March 2024
- Article on Substack: https://moderndata101.substack.com/p/model-first-data-products
- Title: Semantic Layers: A Buyers Guide
- Authors: David Jayatillake and Rohan Thakur
- Date: 25 March 2024
- Link to the article on Substack: https://davidsj.substack.com/p/semantic-layers-a-buyers-guide
- Title: The Semantic Layer Movement: The Rise & Current State
- Date: Oct. 2024
- Author: Animesh Kumar
- Link to the article on Medium: https://medium.com/@community_md101/the-semantic-layer-movement-the-rise-current-state-f8dbbb989b2e
- That is one of the seminal articles starting the concept of Semantic layer
- Title: Design Tip #158 Making Sense of the Semantic Layer
- Date: Aug. 2013
- Author: Joy Mundi (she has retired long ago by now; Joy Mundi on the Kimball Group site, Joy Mundi on LinkedIn)
- Link to the Kimball Group post: https://www.kimballgroup.com/2013/08/design-tip-158-making-sense-of-the-semantic-layer/
- GitHub repository:
- Main contributors:
- Julien Hurault (Julien Hurault on LinkedIn, Julien Hurault on Substack)
- David Krevitt (David Krevitt on LinkedIn, David Krevitt on Substack)
- Article on Substack: https://juhache.substack.com/p/the-boring-semantic-layer
- Home page: https://cube.dev
- GitHub repository: https://github.com/cube-js/cube
- Statement: Cube — Universal semantic layer platform for AI, BI, spreadsheets, and embedded analytics
Cube is the semantic layer for building data applications. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application. Cube was designed to work with all SQL-enabled data sources, including cloud data warehouses like Snowflake or Google BigQuery, query engines like Presto or Amazon Athena, and application databases like Postgres. Cube has a built-in relational caching engine to provide sub-second latency and high concurrency for API requests.
- Home page: https://www.denodo.com/en
- Statement: "One Logical Platform for All Your Data One Modern Solution for Your Business"
- GitHub repository: https://github.com/dbt-labs/metricflow
- Statement: "MetricFlow is a semantic layer that makes it easy to organize metric definitions"