Video Series

Entropy Explains

Created and narrated by Dr. Simon Harrer

Co-Founder & CEO, Entropy Data

Entropy Explains is a series of short animated explainers on data contracts, data products, and semantics. Each episode explains one concept in under three minutes, with animations instead of slides.

Watch the playlist on YouTube

Coming next

Data Contract Best Practices: answers “What are the best practices for data contracts?” One data contract per output port, schema, and read role, kept in the data product's Git repository, versioned like an API, and treated as the source of truth once it is active.

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Episodes

Episode 10 · 2:24

Data Contracts with dbt

Answers: “How do you use data contracts in a dbt project?”

If you write a data contract by hand next to your dbt model, you keep two copies of the schema, and they drift apart. Import the first contract from the dbt manifest, then let the Data Contract CLI sync it into your project as properties and tests. In CI, a second sync must change nothing, and dbt test reports the results in the words of the contract.

Watch on YouTube Read as annotated slides

Episode 9 · 2:56

Data Contracts and Data Quality

Answers: “How do you define, test, and measure data quality?”

Revenue doubled overnight because of duplicate rows, and nobody had written down that order IDs must be unique. Capture quality rules in plain language with the business and the data consumers, turn them into SQL checks and library metrics with AI, and put them into the data contract together with the SLAs. The Data Contract CLI tests them in CI and on a schedule, and a quality score by dimension shows where quality is weak.

Watch on YouTube Read as annotated slides

Episode 8 · 2:13

Domain Ontologies

Answers: “How do you model an ontology across many domains?”

One ontology for the whole company sounds tidy, but every change needs every domain to agree. Model one ontology per domain, owned by the domain team. Link the domains directly while there are few; once there are many, a small central ontology links only the shared concepts, and AI agents follow it across domains to the linked data contracts.

Watch on YouTube Read as annotated slides

Episode 7 · 2:31

How to Get to Your Ontology

Answers: “How do you build an ontology with AI agents?”

Business people and AI agents both need an ontology, and nobody wants to wait through months of workshops for one. Capture what the business already knows, let an agent draft concepts and relationships on a branch, and let business people review the diff in the diagram, a few rounds, before the merge. Already in use? The blast radius shows which data contracts and owners a change affects.

Watch on YouTube Read as annotated slides

Episode 6 · 2:40

Data Contracts and CI/CD

Answers: “How do data contracts fit into your CI/CD pipeline?”

One renamed column, three broken consumers, and the data contract knew. Keep it in Git next to the pipeline code and check every pull request with the Data Contract CLI: lint, your own rules, breaking changes, and tests in an isolated environment. After the merge, test in staging and production and publish. Every night, promise monitoring tests production.

Watch on YouTube Read as annotated slides

Episode 5 · 1:56

Put Your Data Under Contract

Answers: “How do you put an existing table under a data contract?”

Your table has been in production for years, and there is no data contract. Import it with the Data Contract CLI, test it right away for a green baseline, and add what only you know: a description, a classification, a quality rule, a freshness promise, and an owner. You go data-first once, then the contract leads.

Watch on YouTube Read as annotated slides

Episode 4 · 2:08

Building Data Products with AI

Answers: “How do you build a data product with AI?”

Nobody writes data pipelines by hand anymore. A coding agent builds the data product contract-first, from three inputs: a data contract as the what, defined together in a workshop, skills with your conventions as the how, and a data marketplace as the data to build on.

Watch on YouTube Read as annotated slides

Episode 3 · 2:18

Data Contracts and Semantics

Answers: “How do AI agents know what your data means?”

Model the meaning once: an ontology with entities, properties, and relationships, captured in Apache Ossie. Link data contract columns to its concepts so they inherit description and classification, and let AI agents use that anchoring to know how to join data, and what to leave out.

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Episode 2 · 2:38

Data Contracts and Data Products

Answers: “What is a data product, and how does it relate to data contracts?”

A data product is more than just data: data, code, and metadata as one unit, owned by one team. How it lives in Git as an Open Data Product Standard (ODPS) file, is published to a data marketplace, and moves from v1 to v2 without outages.

Watch on YouTube Read as annotated slides

Episode 1 · 2:02

Data Contracts

Answers: “What is a data contract, and why do you need one?”

Why data consumers break when data changes, what goes into a data contract in the Open Data Contract Standard (ODCS), and how the open-source Data Contract Editor and Data Contract CLI keep the promise.

Watch on YouTube Read as annotated slides

Entropy Explains

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