Data Mesh Implementation
The Challenge
Centralized data teams become bottlenecks as organizations scale. Domain experts lack ownership of their data, leading to slow delivery and misaligned priorities.
Root Cause Analysis
- Central bottleneck: One data team serving dozens of business domains
- Lack of domain context: Central teams misinterpret domain-specific data semantics
- Poor data quality: No accountability at the source
- Slow time-to-insight: Weeks-long request queues for new datasets
How We Solve This with Cloud Technologies
Domain-Driven Data Products
We implement Data Mesh principles using cloud-native tooling:
- Self-serve data infrastructure with Databricks Unity Catalog or AWS Lake Formation
- Domain-owned data pipelines using dbt and Apache Airflow per domain
- Federated governance with centralized policies and decentralized execution
- Data product APIs exposing curated datasets as discoverable, documented products
Key Patterns
- Data Product specification: Schema, SLA, quality metrics, lineage
- Self-serve platform: Terraform modules for domain teams to provision pipelines
- Federated compute: Each domain runs transformations in isolated Spark clusters
- Discovery catalog: Atlan or Collibra for searchable, documented data products
Business Impact
- 3x faster data delivery with domain teams owning their pipelines
- Higher data quality through source-level accountability
- Scalable architecture that grows with organizational complexity