Databricks is the leading data lakehouse platform, combining the flexibility and cost-efficiency of a data lake with the reliability and performance of a data warehouse in a single, unified platform. Built on Apache Spark and Delta Lake, it serves data engineering, data science, and machine learning teams that need to process massive datasets, train AI models, and run analytical workloads from the same governed data foundation.
Product Overview
Databricks invented the data lakehouse concept — the idea that organisations shouldn't have to choose between the cheap object storage of a data lake (S3, ADLS, GCS) and the query performance of a data warehouse. Delta Lake, Databricks' open-source storage layer, adds ACID transactions, schema enforcement, and time-travel versioning on top of Parquet files in object storage — giving warehouse-like reliability without warehouse-like storage costs. Databricks SQL provides a familiar SQL interface for analytics and BI workloads, while Databricks Notebooks (Python, Scala, R, SQL) serve data science and ML use cases. Unity Catalog provides centralised governance — a single place to manage access control, lineage, and auditing across all data assets. Databricks' Mosaic AI platform adds MLflow (model tracking), feature engineering, and LLM fine-tuning capabilities for AI-first data teams.
Key Features
- Delta Lake: Open-source storage layer with ACID transactions, schema enforcement, and time-travel on object storage — the lakehouse foundation.
- Databricks SQL: Serverless SQL analytics on Delta Lake — optimised for BI and dashboarding workloads with sub-second query performance.
- Collaborative Notebooks: Multi-language notebooks (Python, SQL, Scala, R) with real-time collaboration for data engineering, science, and ML workflows.
- Unity Catalog: Centralised data governance — manage access control, data lineage, and auditing across all data and AI assets in one place.
- Mosaic AI: End-to-end ML and LLM platform — model training, MLflow experiment tracking, feature engineering, and LLM fine-tuning.
Best For
Enterprise data engineering and data science teams that need a unified platform for large-scale data processing, machine learning, and analytics — particularly those working with unstructured data, streaming, or AI workloads.
Pricing
Consumption-based pricing by DBU (Databricks Unit) across compute tiers. Jobs: from $0.07/DBU. SQL: from $0.22/DBU. Enterprise: custom.
Key Integrations
Snowflake, dbt, Fivetran, Tableau, Power BI, Looker, Salesforce, Kafka, Airflow, MLflow
Pros
- Lakehouse architecture unifies data engineering, analytics, and ML on one platform
- Open formats (Delta Lake, Parquet) avoid proprietary data lock-in
- Best-in-class for large-scale data processing and ML workloads
- Strong governance with Unity Catalog across data and AI assets
Cons
- Steeper learning curve than SQL-only warehouses like Snowflake or Redshift
- Primarily engineering-oriented — less accessible for business analysts than BI tools
- Cost management requires careful cluster configuration and auto-scaling setup
