Make data central feature
summary
Argues that modern tools shift from managing tasks to modeling data sets and shows concrete demos: DBT for SQL models, a Versatile Data Kit notebook that ingests GitHub PRs by declaring sources, destinations, flows, and Dagster (duxter) that defines assets and materialization to produce dimension and fact tables (PRs merged). Claims the system infers orchestration, parallelizes work, and keeps dataset-to-job links; demo runs end-to-end on GitHub data and materializes the tables successfully.
AI-written summary · about
chapters (11)
- 0:00Welcome
- 1:29Why data-centric tools matter
- 3:42Data engineering cycle
- 5:04Task-centric tools limits
- 7:53Data-centric interfaces benefits
- 9:20Example: DBT for transformations
- 11:30Tools continuum: task to data
- 12:53Demo: Versatile Data Kit
- 15:45Demo: Dexter for modeling
- 19:43Summary
- 21:09Q&A
related talks
39mData Mesh: Why, What and How
39mEnabling Data centric Teams
26mData, It’s a People Problem: Understanding the Sociotechnical Elements of a Data-Driven Organization
20mData-as-a-product: A framework for collaborative data wrangling
28mHow Open Source Enables Innovation A Case Study with dbt and Lightdash
31mBuilding 3D Trusted Data Pipelines With Dagster, Dbt, and Duckdb
36mIntroducing Data Mesh A Paradigm Shift In Analytical Data Management Architecture
34mOPERATIONALIZE THE DATA CATALOG