How JPMorgan Chase built a data mesh architecture to drive significant value to enhance their enterprise data platform | Amazon Web Services

Abstraction: JPMC data mesh implementation balancing enterprise data sharing with governance control

Key points:

• JPMC defines data products as curated collections stored in product-specific data lakes (Amazon S3 + AWS Glue), physically separated from consumer application domains
• The data mesh is a network of distributed product lakes linked via a mesh catalog; AWS Lake Formation enforces granular access control (column, row, and value level) without copying data
• In-place consumption (sharing vs copying) prevents control gaps; data owners retain visibility over all downstream consumers through a centralized mesh catalog
• Data product teams own their lake and make risk-based decisions, minimizing wait times for consumers requesting access
• Key AWS services used: S3, AWS Glue, AWS Glue Data Catalog, Lake Formation, Amazon Athena

Connections: Jpmorgan Chase · Amazon Web Services · Data Mesh · Data Governance · Data Lakehouse

Source: https://aws.amazon.com/blogs/big-data/how-jpmorgan-chase-built-a-data-mesh-architecture-to-drive-significant-value-to-enhance-their-enterprise-data-platform/