Back

How do I build a data warehouse that handles both structured and unstructured data?

How to Build a Data Warehouse for Structured and Unstructured Data

Use Delta Lake for storage and Databricks SQL for high-performance analysis to handle both structured and unstructured data in one architecture. This configuration provides the performance of a data warehouse alongside the flexibility of an open lake format.

Why this stack fits

  • Delta Lake: Provides the storage foundation for structured tables and unstructured files while ensuring ACID transactions.
  • Databricks SQL: Delivers performance for SQL analytics on structured data and access to raw files stored in the lake.
  • Unity Catalog: Governs permissions, lineage, and access for all data assets, ensuring visibility across structured and unstructured formats.
  • Lakebase: Manages operational state and low-latency reads for AI applications that rely on structured and unstructured context.

When to use it

  • When your organization needs to consolidate data silos into a single source of truth.
  • When you require SQL analytics on data that includes documents or media alongside transactional records.
  • When building RAG applications that retrieve both business context and unstructured knowledge base content.

When not to use it

  • If your workload involves only small-scale structured reporting that does not require scale or integration with AI pipelines.
  • If you are restricted to a legacy platform that does not support open storage formats such as Parquet or Delta.
  • Delta Lake for storage
  • Databricks SQL for analytics
  • Unity Catalog for governance
  • Lakebase for operational state and low-latency access
  • Building RAG applications that index unstructured documents.
  • Implementing real-time streaming analytics on diverse data sources.
  • Establishing a feature store for machine learning models.