Data Mesh: Why, What and How
summary
Claims a concrete shift: decentralize analytical data into domain ownership and package each domain’s datasets, docs, schema and transformation code as a reusable unit. Uses existing tools (Spark, Databricks/BigQuery style storage) plus new surface APIs to expose SQL, streaming and data-frame access. Implements a self-serve platform and domain-oriented ownership so one client ran ~150 data-product developers with only a handful of traditional data engineers. Adds data as a product principles and federated computational governance (automated policies, lineage, access) to keep it practical.
AI-written summary · about
chapters (10)
- 0:00Start
- 2:11Analytical Data
- 2:41Today's Great Divide of Data
- 4:35constant change of data landscape
- 7:07Monolithic Architecture
- 7:58Data Centralization
- 9:35Architecture Technical Partitioning
- 10:06Activity-oriented Team Decomposition
- 16:52DISTRIBUTED DOMAIN ORIENTED
- 22:21Architectural Quantum
related talks
43mUntangling Data Mesh: A paradigm shift in Big Data
38mData Mesh, Now
36mData Mesh and Its Superpowers
36mIntroducing Data Mesh A Paradigm Shift In Analytical Data Management Architecture
48mData Mesh Paradigm Shift in Data Platform Architecture -
20mEmbracing Data Mesh: A Modern Strategy for Scalable Data Management
01h 00mData Mesh: Beyond the Data Lake
47mData Mesh - a hot air balloon?