Modern Data Stack (MDS)
The Modern Data Stack (MDS) is a design framework for cloud-native analytical platforms. Rather than relying on monolithic database suites or complex Hadoop clusters, the Modern Data Stack emphasizes a modular ecosystem of best-of-breed tools. These tools connect via APIs, utilizing SQL as the primary language for data modeling, transformation, and analysis.
Standard Components of the MDS
A typical Modern Data Stack is divided into modular layers:
- Ingestion Layer: Services (like Fivetran, Airbyte, or Meltano) that copy data from applications and databases to cloud storage.
- Storage and Table Format Layer: Cloud object storage (like S3 or GCS) combined with open table formats (like Apache Iceberg or Delta Lake) acting as the single source of truth.
- Metadata Catalog Layer: Centralized catalog providers (like Apache Polaris or AWS Glue) managing access controls and pointers.
- Transformation Layer: Modeling frameworks (specifically dbt) where analysts write SQL to clean, partition, and aggregate tables.
- Compute Engine Layer: Fast, vectorized query engines (such as Dremio or Snowflake) executing analytical queries.
- BI and Presentation Layer: Visualization consoles (like Tableau, Power BI, or Apache Superset) rendering dashboards.
Evolution Toward the Lakehouse
In its early iterations, the MDS was built around proprietary cloud data warehouses. Over time, the ecosystem evolved toward open data lakehouses. By substituting proprietary storage formats with open table formats like Apache Iceberg, organizations achieve decoupled compute and storage, preventing vendor lock-in while leveraging the modularity of the Modern Data Stack.