Databricks vs data warehouse for AI

Summary

• Traditional data warehouses excel at SQL queries and BI reporting but require external tools for ML workloads, creating friction between analytics and AI teams.
• A lakehouse combines data warehouse capabilities (ACID, SQL, structured data) with lake flexibility (open formats, Spark for ML, Python/Scala support), eliminating system silos.
• Data warehouses optimize for predictable, repeatable reporting queries. Lakehouses optimize for exploratory analysis, ML feature engineering, and AI model development.
• Lakehouse architectures like Databricks provide unified governance (Unity Catalog), single data copy, and native MLflow integration for end-to-end ML workflows.
• Choose a data warehouse for BI analytics. Choose a lakehouse if you need to build AI applications, run feature engineering, and train models alongside analytics.

Databricks vs data warehouse for AI

The choice between a traditional data warehouse and a lakehouse platform hinges on your workload mix: if you're primarily doing business intelligence and reporting, a data warehouse is optimized for that. If you need to build AI applications, train models, and collaborate across analytics and data science, a lakehouse eliminates silos and reduces complexity.

What is the difference between a data warehouse and a lakehouse?

Data Warehouse: A relational database optimized for structured queries, reporting, and BI. Data is typically organized into star schemas or normalized tables. Traditional data warehouses enforce schema-on-write, meaning data is validated and organized before it enters the system. Query performance is highly predictable.
Lakehouse: A unified platform combining warehouse and lake traits. Data is stored in open formats (Delta Lake, Parquet, Iceberg) and organized using a catalog (Unity Catalog). The lakehouse supports SQL queries like a warehouse but also enables Python, Scala, and distributed computing via Spark for ML workloads. Metadata governance is centralized, and multiple data types (structured, semi-structured) are supported natively.

Evaluation criteria for data platform choice

Capability	Data Warehouse	Lakehouse
Primary use case	SQL queries, BI dashboards, reporting	Analytics + AI, exploratory analysis, feature engineering
Data format	Relational (rows/columns)	Open (Parquet, Delta, Iceberg, JSON, images)
ACID transactions	Yes	Yes (Delta Lake)
SQL support	Full ANSI SQL	Full ANSI SQL + Spark SQL
ML capabilities	None native (use external tools)	MLflow, Spark ML, Python/Scala libraries
Feature engineering	Manual ETL external to warehouse	In-platform using Spark
Governance	Role-based access, object-level	Unity Catalog (table, column, row-level)
Query latency	Sub-second for BI queries	Variable (warehouse queries similar; Spark jobs depend on scale)
Compute model	Provisioned clusters	Autoscaling clusters
Team collaboration	DBAs, analysts	Data engineers, scientists, analysts in one platform
Data copies	Multiple (warehouse, feature store, training)	Single copy (Unity Catalog governs all)

Databricks lakehouse architecture

Databricks implements a lakehouse on Delta Lake (open-source ACID format). Data engineers ingest raw data and build transformation pipelines using Lakeflow Pipelines. Data scientists build features using Spark DataFrames and Pandas, train models with MLflow, and register them in Model Serving for production. Analysts run SQL queries from SQL Warehouses and build dashboards with AI/BI Dashboards.
Key advantages:

• Single source of truth: All teams query the same Delta tables governed by Unity Catalog.
• Native ML integration: MLflow is built in; no external feature store or model registry to manage.
• Open data format: Delta Lake tables are readable by any Spark engine, other query engines, and Python libraries.
• Unified governance: One governance layer (Unity Catalog) for SQL analytics, Python notebooks, and ML pipelines.

When to choose a data warehouse

• Predictable reporting: Your use case is primarily SQL queries, dashboards, and scheduled reports.
• Limited AI needs: You don't train ML models or do complex feature engineering.
• Separate teams: Analytics and data science teams work independently and don't need shared infrastructure.
• Legacy SQL expertise: Your organization has deep SQL and BI tool investments.
• High-concurrency BI: You need many simultaneous users running light-weight queries.

When to choose a lakehouse

• AI workloads: You train models, build features, or use Genie Agents for AI applications.
• Exploratory analysis: Data scientists need flexibility to write Python, SQL, and Spark code interactively.
• Unified teams: You want data engineers, scientists, and analysts working in one platform.
• Complex transformations: You use machine learning or graph processing in ETL pipelines.
• Cost efficiency: You want to avoid managing a separate feature store, model registry, and compute infrastructure.

Common scenarios

Scenario 1: Finance reporting: Your finance team runs monthly close reports in SQL and builds dashboards. A data warehouse is sufficient.
Scenario 2: Churn prediction: Your data science team needs to engineer features from customer behavior, train a model, score production data weekly, and update a dashboard. A lakehouse eliminates friction between the feature engineering and BI teams.
Scenario 3: Hybrid: Your analytics team uses a data warehouse for reporting. Your ML team uses a lakehouse for model development. Data syncs from warehouse to lakehouse nightly or via streaming.

FAQs

Can I use Databricks as a data warehouse?

Yes. Databricks SQL Warehouse supports ANSI SQL and integrates with BI tools. The lakehouse's true value emerges when you combine analytics with ML workloads.

Can a data warehouse do ML?

Traditionally, no. ML feature engineering and model training are not native. You extract data and use external tools (Python, scikit-learn, etc.).

How do I migrate from a data warehouse to a lakehouse?

Export tables as Parquet or CSV, ingest into Databricks Unity Catalog, and convert to Delta Lake format. Rewrite SQL queries (mostly compatible with Spark SQL). Python/Spark code remains the same.