Activity Schema (Event-centric modeling)

A global standard for modeling and querying data

The Activity Schema™ data model is an open standard for data modeling and transformation. It enables simpler, faster, and more reliable data preparation. GitHub, activityschema.com

From Github: 2.0.md.

An Activity Schema data model is a new paradigm designed for modern data warehouses. It was created and implemented by  Ahmed Elsamadisi at  Narrator.

This new standard for data enables:

1. Standardized data modeling accross industry, sector and use-case
2. A simpler structure that is easily understandable
3. A warehouse-native solution for modeling data.

The Activity Schema model aims for these design goals

• only one definition for each concept - know exactly where to find the right data
• single model layer - no layers of dependencies
• simple definitions - no thousand-line SQL queries to model data
• no relationships in the modeling layer - all concepts are defined independently of each other
• no foreign key joins - all concepts can be joined together without having to identify how they relate in advance
• analyses can be run anywhere — analyses can be shared and reused across companies with vastly different data.
• high performance - reduced joins and fewer columns leverages the power of modern column-oriented data warehouses
• incremental updates - no more rebuilding data models on every update
• dynamically queried at analysis time - create any table at the moment you need it

At its core an activity schema approach consists of transforming raw tables into a time series table called an Activity Stream. All downstream plots, tables, materialized views, etc used for BI are built directly from that table, with no other dependencies.

The diagram above is the entire dependency graph: only three layers and a single data model. The modeling layer is able to create any kind of aggregation or table needed, and the consistent table structure allows data analyses to be written once and reused elsewhere.

• Activity Schema

Origin: Matthew Mullins on LinkedIn: The sample size is small, but it’s drawn from professionals in enterprise.… | 18 comments
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Created 2023-09-13