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Databricks

Data Warehouse

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★★★★☆ 4.5 / 5 on G2 · 4.6 on Capterra (400+ G2 reviews)
4.5 / 5
G2 rating, 400+ reviews [source]
Lakehouse
+ AI/ML [source]
Spark
+ Delta Lake [source]
Usage
Based pricing [source]

Quick facts

CategoryData lakehouse platform
VendorDatabricks
Starts atUsage based (compute + storage)
Best forData lakehouse and AI
DeploymentCloud (AWS, Azure, GCP)
Free planTrial / community
IntegrationsData, ML, BI ecosystem
G2 rating4.5 / 5
Capterra rating4.6 / 5

What is Databricks?

Databricks is the leading data lakehouse platform, unifying data warehousing, data engineering and AI/ML on one platform built on Spark and Delta Lake, suited to data and AI teams wanting analytics and machine learning together. It pioneered the lakehouse, combining a data lake's ability to store raw and varied data cheaply with a warehouse's analytics and structure, on one platform that also handles data engineering (Spark), SQL analytics, and machine learning and AI, with open formats (Delta Lake, and support for open table formats), governance (Unity Catalog), and runs on AWS, Azure and Google Cloud. Used by data engineering, analytics and AI/ML teams to unify their data and AI work, Databricks is the standard for the lakehouse. For the data lakehouse and AI, Databricks is a leading choice.

Databricks' distinction is unifying data warehousing, engineering and AI/ML in one lakehouse. By combining lake flexibility, warehouse analytics, Spark data engineering and machine learning on one governed platform, it lets data and AI teams work on all their data and workloads together, which separate warehouses and ML tools do not match for unification and AI. That lakehouse unification and AI focus set it apart.

Databricks at a glanceDatabricks is the leading data lakehouse platform unifying analytics, engineering and AI/ML [Databricks]. It holds a 4.5 out of 5 rating across more than 400 reviews on G2 and 4.6 on Capterra, a leading choice for the data lakehouse and AI.

Who Databricks is best for

Databricks is best for data engineering, analytics and AI/ML teams that want to unify warehousing, data engineering, varied data and machine learning on one lakehouse platform, especially for AI and advanced analytics at scale. It suits the data lakehouse and AI. It is a weaker fit for teams wanting the simplest classic cloud warehouse, where Snowflake or BigQuery fit, a single cloud native warehouse only, where Redshift or Synapse fit, or lightweight real time analytics, where ClickHouse fits.

Key features

The core strengths are the lakehouse architecture (lake flexibility plus warehouse analytics), data engineering on Spark, SQL analytics (Databricks SQL), machine learning and AI (MLflow, model training and serving, generative AI), open formats (Delta Lake), governance (Unity Catalog), and multi cloud. Databricks offers unified data and AI. Its lakehouse unification, Spark and AI/ML are its defining advantages.

Databricks pricing

Databricks is usage based: you pay for compute (Databricks Units, DBUs, by workload type and size) plus your cloud storage, with no upfront licence, and a trial and community edition. So costs scale with compute and workload, and optimisation matters. Confirm details on the Databricks pricing page.

Integrations and ecosystem

Databricks integrates with data sources, ETL/ELT, BI tools, and the ML and AI ecosystem, supports open formats (Delta Lake, Iceberg), and runs on AWS, Azure and Google Cloud, with Unity Catalog governance. Its open formats, ML and AI ecosystem and multi cloud reach, which unify data and AI work, support its lakehouse positioning.

Databricks and the lakehouse

Unifying data warehousing, engineering and AI/ML in one lakehouse is the signature strength: Databricks combines lake flexibility, warehouse analytics, Spark engineering and machine learning on one governed platform, so data and AI teams work on all their data and workloads together, which separate tools do not match for unification and AI. For the lakehouse and AI, this is the main reason to choose Databricks.

Ease of use and support

Databricks is highly rated for unifying data and AI, Spark, ML, the lakehouse and scale, with strong support and ecosystem, popular with data engineering and AI teams, though reviewers note it is more technical and engineering oriented than a simple warehouse, with cost and complexity considerations. Its G2 reviews praise unification and AI, with technical complexity and cost the main considerations.

Is Databricks worth it?

For data engineering, analytics and AI/ML teams that want to unify warehousing, engineering, varied data and machine learning on one lakehouse, Databricks is an excellent choice. The main caution is that it is more technical and engineering oriented with cost considerations, so simple classic warehousing suits Snowflake or BigQuery, single cloud native Redshift or Synapse, and real time ClickHouse, but for the data lakehouse and AI Databricks leads.

Pricing

Standard / Premium

Usage
compute (DBUs) + storage
  • Lakehouse
  • Engineering and SQL
  • Teams

Enterprise

Usage
compute + storage
  • Governance (Unity Catalog)
  • Security
  • Scale

AI / ML

Usage
compute
  • ML and generative AI
  • Model serving
  • AI teams

Pros and cons

Pros

  • Unifies warehouse, engineering and AI/ML
  • Lakehouse (lake + warehouse)
  • Built on Spark and Delta Lake
  • Strong ML and AI capabilities
  • Multi cloud, open formats

Cons

  • More technical and engineering oriented
  • Usage costs and complexity
  • Steeper than a simple warehouse
  • Needs data engineering skill

Databricks alternatives

These data warehouse tools come up most often against Databricks.

Snowflake

Cloud data platform with separated storage and compute

Google BigQuery

Serverless cloud data warehouse on Google Cloud

Amazon Redshift

AWS petabyte-scale cloud data warehouse

Databricks FAQ

What is Databricks used for?
Databricks is used as a data lakehouse: unifying data warehousing, data engineering (Spark), SQL analytics and machine learning and AI on one platform, so data and AI teams store and process varied data, run analytics, and build and serve ML and AI models together, at scale.
How much does Databricks cost?
Databricks is usage based: you pay for compute (Databricks Units, DBUs, by workload and size) plus your cloud storage, with no upfront licence and a trial and community edition. Costs scale with compute. See the Databricks pricing page.
What is a data lakehouse?
A data lakehouse, pioneered by Databricks, combines a data lake's ability to store raw and varied data cheaply with a data warehouse's analytics and structure, on one platform, adding data engineering and AI/ML, so organisations unify their analytics and AI stack instead of separate lakes and warehouses.
Databricks vs Snowflake, which is better?
Databricks is a lakehouse unifying analytics, data engineering and AI/ML, while Snowflake is an all round cloud data warehouse known for ease and sharing. Databricks suits unified data and AI workloads and engineering; Snowflake suits warehousing, analytics and data sharing.
Is Databricks good for AI and machine learning?
Yes. Databricks is strong for AI and machine learning, with built in ML (MLflow, model training and serving), generative AI, and data engineering on one lakehouse platform, which is a key reason data and AI teams choose it to unify analytics and AI.
Is Databricks multi cloud?
Yes. Databricks runs on AWS, Azure and Google Cloud, so organisations can use its lakehouse on their chosen cloud, vendor neutrally, with open formats (Delta Lake) and governance (Unity Catalog), which supports unifying data and AI across clouds.

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