A unified data analytics platform that combines data engineering, data science, and machine learning on a lakehouse architecture, built on Apache Spark.
Databricks is one of the two cloud platforms (the other being Snowflake) that most data teams will evaluate in 2026. The marketing pitch is "unified lakehouse platform" — and once you cut through the slogans, the honest positioning is: Databricks is the best place to run Spark, train ML models at scale, and work with data that lives in Iceberg/Delta on S3/ADLS/GCS. If your workload is SQL-first analytics over structured data, Snowflake is usually simpler. If your workload is Python-first with heavy ML or streaming, Databricks is usually stronger.
Three things I've never seen Databricks lose on: (1) multi-language unified workflow — Python, Scala, SQL, and R in one notebook with Unity Catalog governance across all of them; (2) Photon + Delta — the Photon engine plus Delta's liquid clustering and deletion vectors make Delta-native ELT legitimately fast, often on par with Snowflake for similar work; (3) ML platform integration — MLflow + Model Serving + Feature Store + Vector Search in one place means fewer pieces to assemble. Where it struggles: SQL-only workflows feel heavier than Snowflake; setup and governance require more platform work; and costs are harder to predict without DBU modeling.
At one client we inherited a Snowflake-centric platform that added Databricks specifically for two workloads: a 40TB event-data pipeline with complex sessionization (Spark won on both cost and expressiveness) and a model training stack that needed GPU clusters (Databricks ML beat SageMaker for the team's Python ergonomics). Everything else — dashboards, BI, cross-team SQL analytics — stayed on Snowflake. The two platforms coexisted, connected by Delta Sharing for the datasets both sides needed. This "Snowflake + Databricks" pattern is now common enough that Delta Sharing and Snowflake Iceberg tables have both shipped explicit support for it.
Databricks is a unified data analytics platform founded by the creators of Apache Spark. It combines data engineering, data science, and machine learning capabilities on a lakehouse architecture—merging the best of data lakes and data warehouses.
| Feature | Databricks | Snowflake |
|---|---|---|
| Architecture | Lakehouse | Cloud DW |
| ML/AI | Built-in (MLflow, AutoML) | Limited |
| Streaming | Native Spark Streaming | Limited |
| Open Formats | Delta Lake, Parquet | Proprietary |
| SQL Performance | Good | Excellent |
| Data Science | Excellent | Basic |
Field-tested gotchas when running Databricks in production:
autotermination_minutes aggressively on interactive clusters and enforce it via cluster policies. On one client I watched $15K/month leak to forgotten notebooks.Databricks is used as a unified data and AI platform — data engineering (ETL on lakehouse storage), streaming (Structured Streaming with Delta), SQL analytics (Databricks SQL Warehouses with Photon), machine learning (MLflow, Model Serving, Feature Store, Vector Search), and increasingly LLM application development via Mosaic AI. Its sweet spot is Python-centric organizations with mixed workloads — classic ETL plus ML plus streaming — where one platform replaces four specialized tools. For SQL-only analytics over structured data, teams often prefer Snowflake for its simplicity and cost predictability.
Databricks is a lakehouse, not a traditional data warehouse — but Databricks SQL + Photon + Delta Lake gives you warehouse-grade SQL performance and ACID transactions on open-format data in S3/ADLS/GCS. Functionally, you can use Databricks SQL Warehouses as your warehouse layer for BI and analytics, and the experience is similar to a dedicated warehouse. The architectural difference: your data sits in open Delta (or Iceberg) tables on cheap object storage that any engine can read, rather than in a proprietary storage format like a pure warehouse. Whether the distinction matters depends on whether you care about engine portability.
No — Databricks is built on Spark but adds substantial commercial capabilities: Photon (a C++ vectorized execution engine that's often 2-3x faster than open-source Spark for SQL workloads), Delta Lake (open-source but Databricks-authored and Databricks-optimized), Unity Catalog governance, MLflow integration, managed notebooks, Jobs orchestration, Model Serving, and Mosaic AI. Think of it as "Spark + a commercial platform + a lot of engineering." You can run open-source Spark free on EMR, Dataproc, or Kubernetes; Databricks charges a premium for the managed experience and the proprietary pieces (Photon especially).
Not a clean either/or in 2026. Pick Snowflake if your primary workload is SQL analytics over structured data, simplicity and cost predictability matter more than flexibility, your team is SQL-first, and you want governance out of the box. Pick Databricks if your primary workload is Python/Scala-heavy ETL, ML, or streaming, if data lives natively in object storage in open formats (Iceberg/Delta), and if your team is comfortable with more platform engineering. Many enterprises run both — Snowflake for BI and cross-team analytics, Databricks for ML and large-scale Spark — connected by Iceberg, Delta Sharing, or warehouse-to-lakehouse CDC.
Delta Lake is an open-source storage format — Parquet files plus a JSON/Parquet transaction log — that adds ACID transactions, schema evolution, time travel, and fast updates/deletes to data-lake storage. It's the default storage layer on Databricks. You don't strictly have to use it — Databricks also reads Parquet and (via Unity Catalog federation) Iceberg — but most Databricks-native performance features (Photon optimizations, liquid clustering, deletion vectors, Change Data Feed) assume Delta. If you want maximum engine portability, Iceberg is the more neutral choice; if you're Databricks-first, Delta is the path of least resistance.
Unity Catalog is Databricks' centralized governance layer for data and AI assets — tables, views, volumes, ML models, notebooks, and more. It provides a three-level namespace (catalog.schema.object), fine-grained ACLs with SQL-like GRANT statements, column masking and row filters, data lineage, auditing, and cross-workspace sharing. For new Databricks workspaces in 2026, UC is effectively required — most new platform features (Delta Sharing, clean rooms, ML model governance, attribute-based access) assume UC. If you're still on the legacy Hive Metastore, migrating to UC is usually worth the investment.
Databricks is one of the pricier cloud-data platforms when measured simplistically per DBU, but "expensive" is workload-dependent. For Python-heavy ETL, ML training, and streaming, Databricks is often cheaper than the alternatives (a dedicated Spark cluster + SageMaker + a streaming engine + a model registry). For SQL-only warehousing, Snowflake can be cheaper because you don't pay for the ML/streaming pieces you're not using. Practical cost levers: use serverless SQL warehouses (cheaper than classic for bursty SQL), enforce auto-termination and cluster policies, right-size job clusters, and monitor DBU spend via System Tables + dashboards.