Databricks AI, Joute's Review

The essentials

• Unified lakehouse platform: data engineering, ML, GenAI, governance
• Usage-based pricing (Databricks Units), available on AWS, Azure, GCP
• Native MLflow, Unity Catalog, LLM and RAG support
• Targets large data teams that want a unified data+AI platform

What is Databricks AI?

Databricks is a cloud platform that unifies data engineering (Spark), data science (collaborative notebooks), ML (MLflow) and governance (Unity Catalog) in one environment. The GenAI layer, progressively added since 2023, includes Mosaic AI for LLM fine-tuning, native RAG features and inference pipelines. Databricks is open source at its core (Apache Spark, MLflow, Delta Lake) and available on all three major clouds. For large enterprises wanting to avoid vendor lock-in while having a robust enterprise platform, Databricks is the reference.

Strengths

Unified lakehouse architecture

One place for raw data, transformations, ML features and production models. No more data copies between data lake and data warehouse.

MLflow: the experimentation tracking standard

MLflow, created by Databricks, has become the industry standard for ML experiment tracking. Native and mature integration with the Databricks ecosystem.

Multi-cloud and open source

No lock-in: Databricks runs on AWS, Azure and GCP. Core components (Spark, MLflow, Delta Lake) are open source. You can leave if you want.

Limits

Complex setup

Databricks requires data engineering expertise to be correctly configured and optimized. Costs can spiral if clusters aren't well managed.

Hard to predict total cost

The Databricks Units model is opaque for teams discovering the platform. In the first months, bills can be surprising.

Pricing

Pay-as-you-go in Databricks Units (DBU). Variable cost by cluster type and duration. A cloud subscription (AWS/Azure/GCP) is also required. Check databricks.com/pricing for cost estimators.

Alternatives

Databricks AI = unified lakehouse. Alternative Snowflake = data warehouse, different ML ecosystem. Alternative BigQuery ML (GCP) = SQL-first, simpler. Alternative SageMaker = AWS-native, less unified.

Verdict

Databricks is recommended for large data teams (10+ people) that want a unified platform for ingestion, transformation, ML and GenAI. For smaller teams or simple projects, managed solutions like BigQuery or Snowflake are less intimidating.

FAQ

Does Databricks support Jupyter notebooks?

Yes, Databricks offers collaborative notebooks compatible with standard Python notebooks. The environment is similar to Jupyter with real-time collaboration features.

Can you use Databricks with open source models (Llama, Mistral)?

Yes, Databricks is particularly well-positioned for deploying and fine-tuning open source models via Mosaic AI.

What's the difference between Databricks and Snowflake?

Databricks is strong on data engineering and ML (Spark, MLflow). Snowflake is strong on SQL data warehousing and data sharing. Both have expanded their scope, but historical strengths remain.

Is Databricks GDPR and SOC 2 compliant?

Yes, Databricks is SOC 2 Type II, ISO 27001, HIPAA certified and GDPR compliant. Check details at databricks.com/trust.

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