Databricks vs BigQuery

Quick verdict

Databricks describes its product as a Data Intelligence Platform built on the lakehouse model: data lives as files in cloud object storage, organised into tables by an open table format. Delta Lake is the default format for all operations on Databricks; Databricks originally developed the Delta Lake protocol and contributes to the open source project. Unity Catalog is the governance layer for tables, volumes, models and functions. Compute comes as Apache Spark clusters, serverless compute for notebooks, jobs and Lakeflow pipelines, and SQL warehouses for BI and SQL analytics, with Photon, Databricks' native vectorised query engine, enabled on SQL warehouses and serverless compute. Databricks runs on AWS, Microsoft Azure (as Azure Databricks) and Google Cloud.

BigQuery is Google Cloud's fully managed data warehouse. Google documents a storage layer that ingests, stores and optimises data in a columnar format with ACID transaction semantics, and a separate distributed compute layer, connected by Google's network so that each scales independently. There are no clusters or warehouses to create: you are billed either per TiB of data your queries process (on-demand) or for slot capacity through the Standard, Enterprise and Enterprise Plus editions. BigQuery ML lets you train and run models with SQL, and Gemini features assist with SQL and Python. BigQuery is a Google Cloud service.

Side by side

Key differences

Lakehouse platform versus serverless warehouse

Databricks starts from files in object storage. A Delta Lake table is a set of Parquet files plus a transaction log, which gives ACID transactions, versioned history and time travel to earlier versions, and lets batch and Structured Streaming jobs work on the same copy of the data. Unity Catalog sits over those tables and adds access control, lineage, auditing through system tables and data sharing; it is enabled automatically for workspaces created after 8 November 2023, and Databricks also publishes an open source implementation of Unity Catalog. Databricks SQL supports ANSI SQL with Delta Lake extensions, and the same tables are used from notebooks and jobs in Python or Scala.

BigQuery starts from a managed warehouse. You load or stream data into BigQuery tables, and Google handles storage layout, replication and compute allocation. In our view, the practical difference is who does the platform work: on Databricks your team chooses compute types, cluster policies and storage locations; on BigQuery most of that is done for you, at the cost of being tied to Google Cloud.

Compute: SQL warehouses and clusters versus slots

Databricks documents three SQL warehouse types. Serverless warehouses run in Databricks' own serverless compute plane, typically start in 2 to 6 seconds, and include Photon, Predictive IO and Intelligent Workload Management. Pro warehouses run in your own cloud account, typically take about 4 minutes to start, and include Photon and Predictive IO. Classic warehouses also run in your account and include Photon only. Outside SQL, you run Spark clusters (classic compute in your cloud account) or serverless compute for notebooks, jobs and Lakeflow pipelines. Databricks documents that Photon does not support UDFs, RDD APIs or Dataset APIs, and that Photon compute consumes DBUs at a different rate from the same instance type without Photon.

BigQuery's unit of compute is the slot. On on-demand pricing Google allocates slots for each query and bills for bytes processed. On capacity pricing you buy slots through an edition: Standard supports autoscaling only, while Enterprise and Enterprise Plus add a baseline of reserved slots plus autoscaling and optional one-year or three-year commitments. Slot-hours are billed with a one-minute minimum by default. Google documents a 99.9% SLO for Standard and 99.99% for Enterprise and Enterprise Plus.

Open table formats: Delta Lake and Iceberg on both sides

On Databricks, CREATE TABLE without a USING clause creates a Delta table. Tables created with USING ICEBERG in Unity Catalog are managed Iceberg tables (generally available; they need Databricks Runtime 16.4 LTS or later and serverless compute). Iceberg tables managed by other catalogs such as AWS Glue or Snowflake can be attached as foreign Iceberg tables, which are read-only in Databricks. External engines can read and write Iceberg tables in Unity Catalog through the Iceberg REST Catalog API. Databricks announced in June 2024 that it had agreed to acquire Tabular, the company founded by Apache Iceberg's original creators.

-- Databricks SQL
CREATE TABLE main.sales.orders (order_id BIGINT, amount DECIMAL(10,2));  -- Delta by default
CREATE TABLE main.sales.orders_ice (order_id BIGINT, amount DECIMAL(10,2)) USING ICEBERG;

BigQuery offers Apache Iceberg managed tables (formerly called BigLake tables for Apache Iceberg in BigQuery), which are generally available: BigQuery manages them like its own tables, with DML, streaming, schema evolution and time travel, but stores the data in Iceberg format in Cloud Storage buckets you own, so engines such as Spark can read them. External Iceberg tables are read-only. For Delta Lake, BigQuery offers BigLake external tables; Google's documentation lists limits, including that the schema is autodetected and cannot be modified from BigQuery, that Delta Lake V2 checkpoints are not supported, and that change data capture, materialized views and the Read API are not supported on them. Databricks can also query BigQuery tables in place, read-only, through Lakehouse Federation.

Machine learning and AI

Databricks documents MLflow for experiment tracking and the model lifecycle, Model Serving for deploying custom models and LLMs as REST endpoints, a Feature Store with features governed in Unity Catalog, AI Gateway for governing and monitoring access to served models, Ray on Databricks, and serverless GPU compute for training and inference. Models and functions are Unity Catalog objects, so the same permissions model covers data and models.

BigQuery ML lets you create and run models such as classification, regression, clustering, recommendation, forecasting and anomaly detection with SQL statements. Google's editions page lists BigQuery ML as available in Enterprise and Enterprise Plus but not in the Standard edition. In our view, Databricks suits teams that build and serve models as code; BigQuery ML suits analysts who want predictions without leaving SQL.

Pricing and licensing

Databricks is billed in Databricks Units (DBUs), a normalised unit of processing power, charged per second at a rate that depends on the compute type (for example jobs compute, all-purpose compute, SQL Classic, SQL Pro, SQL Serverless), your plan and your cloud and region. On classic compute, which runs in your own cloud account, your cloud provider also bills you separately for the virtual machines, storage and networking. For serverless compute and serverless SQL warehouses, Databricks documents that the DBU charge covers the underlying compute. Azure Databricks prices are set by Microsoft and billed through your Azure subscription. As one example, Microsoft's Azure Retail Prices API listed the Azure Databricks Premium Serverless SQL DBU in East US at USD 0.70 per DBU-hour in October 2026; Databricks' own price tables load dynamically, so use its pricing calculator for AWS and Google Cloud. Committed-use contracts give discounts. Free Edition is free for non-commercial use only, with serverless compute and one 2X-Small SQL warehouse under a fair-usage policy; it replaced Community Edition, which was retired in 2025. A 14-day free trial is also offered.

BigQuery charges separately for compute and storage. On-demand compute is billed per TiB of data processed, with the first 1 TiB per month free; the per-TiB rate varies by location, and Google's pricing page did not render for us to read in October 2026, so check it or the Google Cloud calculator for your region. Capacity compute is billed per slot-hour through the Standard, Enterprise and Enterprise Plus editions, with one-year (20%) and three-year (40%) commitment discounts on Enterprise and Enterprise Plus; slot-hour rates vary by edition and region, so use Google's calculator. Storage is billed per GiB-month, with the first 10 GiB per month free. New Google Cloud customers get USD 300 of welcome credit to use within 90 days.

In our view, the two models reward different habits: on BigQuery on-demand, cost follows bytes scanned, so partitioning, clustering and selecting only needed columns matter; on Databricks, cost follows how long compute runs and at what size, so auto-stop settings and right-sizing matter.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• You want a managed SQL warehouse that runs on any of the three big clouds: compare Snowflake vs Databricks and Snowflake vs BigQuery.
• Your main need is sub-second dashboards or APIs over event data: a real-time OLAP database may fit better; see ClickHouse vs BigQuery and ClickHouse vs Databricks.
• You are on AWS: see Databricks vs Redshift and BigQuery vs Redshift. On Microsoft's stack, see Databricks vs Microsoft Fabric.
• Your data fits on one machine or in a PostgreSQL database: you may not need a warehouse at all; see data warehouse vs database and DuckDB vs BigQuery.

Final recommendation

Frequently asked questions

Sources

• Databricks pricing
• Databricks: SQL warehouse types
• Databricks: Serverless compute
• Databricks: What is Delta Lake
• Databricks: Unity Catalog
• Databricks: Apache Iceberg support
• Databricks: Photon
• Databricks: AI and machine learning
• Databricks: Lakehouse Federation
• Databricks Free Edition limitations
• Azure Databricks pricing (Microsoft)
• BigQuery overview
• BigQuery pricing
• BigQuery editions
• BigQuery: Apache Iceberg tables
• BigQuery: BigLake external tables for Delta Lake
• Google Cloud free tier

Checked October 2026.

How we research comparisons: our editorial method.