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Pricing sourced from Databricks · Last verified March 2026
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View Full Features →Databricks uses a lakehouse architecture that stores data in open formats (Delta Lake/Parquet) on your cloud object storage, combining data lake flexibility with warehouse-like performance and governance. Snowflake is a purpose-built cloud data warehouse optimized for SQL analytics. Databricks excels at unified workloads spanning data engineering, data science, and ML on a single platform, while Snowflake is generally stronger for pure SQL analytics and ease of use for analysts. Many organizations use both, though Databricks is positioning its SQL capabilities as a warehouse replacement.
Databricks uses a consumption-based pricing model measured in Databricks Units (DBUs). Standard tier starts at $0.07/DBU, Premium at $0.22/DBU, and Enterprise at $0.33/DBU. Serverless SQL compute runs at $0.55/DBU, while Jobs compute ranges from $0.10–$0.30/DBU depending on tier and cloud provider. Cloud infrastructure costs (VMs, storage, networking) are billed separately by your cloud provider, typically adding 30–50% on top of DBU charges. Premium and Enterprise tiers add features like Unity Catalog, audit logging, and role-based access control. There is no free tier for production use, though a 14-day free trial is available. Most production customers spend $5,000–$50,000+/month depending on workload scale.
Yes, Databricks supports structured streaming through Apache Spark's streaming capabilities. You can ingest data from sources like Apache Kafka, Amazon Kinesis, and Azure Event Hubs, and process it with the same DataFrame API used for batch workloads. Delta Live Tables simplifies building reliable streaming and batch ETL pipelines with declarative syntax and automatic data quality enforcement.
Databricks notebooks support Python, SQL, Scala, and R. You can mix languages within a single notebook using magic commands. Python is the most widely used language on the platform, and Databricks SQL provides a dedicated SQL-first experience for analysts. The platform also supports Java for Spark jobs submitted via JAR files.
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