# Databricks vs BigQuery

> If your data and teams live in Google Cloud and the work is mostly SQL analytics and BI, BigQuery is the simpler choice. There are no clusters to run, you pay per query or per slot, and Gemini is built into the console. Choose Databricks when heavy Spark or Python engineering, custom ML and AI agents, or a platform that also runs on AWS and Azure matter more.

Canonical: https://www.techfabric.com/compare/databricks-vs-bigquery
Written by TechFabric, a Databricks partner. Checked against the vendors' own pages on 2026-10-10.

## Databricks

A data and AI platform that runs on AWS, Azure and Google Cloud, keeps tables in open Delta Lake or Apache Iceberg format in cloud object storage, and governs them with Unity Catalog.

## BigQuery

Google Cloud's serverless data warehouse, where you run SQL without managing servers and pay either per TiB scanned or per slot-hour, slots being Google's unit of query compute.

## Choose Databricks when

- You run data on more than one cloud, or expect to, and want one platform and one catalog across AWS, Azure and Google Cloud.
- Your pipelines are large Python or Spark jobs, batch and streaming, and you want ingestion, declarative pipelines and orchestration in one place with Lakeflow.
- You're building custom ML models or AI agents on governed data and want tables, models and functions under one catalog.
- You want to keep data in open table formats that other engines can read and write through an Iceberg REST catalog, rather than read from exported metadata.

## Choose BigQuery when

- Your company is standardized on Google Cloud and most of the work is SQL analytics feeding dashboards and spreadsheets.
- You don't want to size anything. On-demand pricing bills per TiB scanned, and the first 1 TiB each month is free.
- Your analysts want to train models and call Gemini from SQL (BigQuery ML and AI functions) without adopting a separate platform.
- Your data team is small and would rather not manage clusters, runtimes or Spark versions at all.

## Differences

| | Databricks | BigQuery |
| --- | --- | --- |
| Architecture | A lakehouse: compute (clusters, serverless jobs and SQL warehouses) is separate from tables stored in cloud object storage, with Unity Catalog as the governance layer across them. | A serverless warehouse: Google runs the compute, measured in slots, and the storage. It can also manage Iceberg tables whose files sit in a Cloud Storage bucket you own. [21, 22, 2, 3] |
| Storage and open table formats | Delta Lake by default. Unity Catalog managed Iceberg tables, foreign Iceberg tables and Iceberg v3 features became generally available on 21 May 2026, and external engines can read and write managed Iceberg tables through the Iceberg REST Catalog API. | Native BigQuery storage by default. Apache Iceberg managed tables keep data in your Cloud Storage bucket; other engines read them from exported metadata, and Google says to add data only through BigQuery. BigQuery writes to tables in the Lakehouse runtime catalog are in Preview. [13, 14, 3, 4, 5] |
| Compute model | Choice of classic clusters in your cloud account, serverless compute, and SQL warehouses; serverless SQL warehouses typically start in 2 to 6 seconds. | Queries run on slots. You use on-demand capacity, or reservations in the Standard, Enterprise or Enterprise Plus edition that autoscale; Standard reservations are capped at 1,600 slots. [22, 24, 2] |
| Data engineering and pipelines | Lakeflow covers ingestion connectors (Lakeflow Connect), Spark Declarative Pipelines for batch and streaming in SQL or Python, Lakeflow Jobs for orchestration, and Lakeflow Designer for visual data preparation. | SQL first, with BigQuery pipelines, the Data Transfer Service, Python UDFs (generally available since May 2026) and a generally available Data Engineering Agent. Spark work runs in Managed Service for Apache Spark, the service formerly called Dataproc. [20, 4] |
| Machine learning and AI | Python-first ML next to the data. MLflow tracks experiments and manages the model lifecycle, Model Serving deploys custom models and LLMs as REST endpoints, and Agent Bricks builds governed agents; its Knowledge Assistant became generally available in January 2026. Models and functions are governed in Unity Catalog alongside tables. | BigQuery ML trains and runs models with SQL and can call models on Gemini Enterprise Agent Platform (formerly Vertex AI). It works with on-demand pricing and with Enterprise or Enterprise Plus reservations, but not Standard edition. Gemini in BigQuery adds data agents, data canvas and SQL generation, with some features still in Preview. [26, 25, 21, 8, 2, 6, 10] |
| Governance and catalog | Unity Catalog governs tables, views, volumes, functions and models with grants, attribute-based policies, row and column filters, and automatic lineage. An open-source implementation also exists. | Google Cloud IAM plus Knowledge Catalog, the name Google gave Dataplex Universal Catalog in April 2026. Open-format tables use the Lakehouse runtime catalog, formerly BigLake metastore. [21, 4, 5] |
| Streaming | Structured Streaming and Spark Declarative Pipelines handle streaming in the same code as batch. Real-time mode in Structured Streaming has been generally available since March 2026, and real-time mode in Spark Declarative Pipelines entered Public Preview in May 2026. | Continuous queries process rows as they arrive and write to BigQuery tables, Iceberg managed tables, Pub/Sub, Bigtable or Spanner. They need an Enterprise or Enterprise Plus reservation and don't run on on-demand pricing. [20, 27, 13, 7] |
| Operational databases (OLTP) | Lakebase is a managed Postgres database inside the platform, with autoscaling, scale to zero, branching and tables synced from Unity Catalog. It is generally available on AWS and Azure but in Beta on Google Cloud. | BigQuery has no transactional database of its own; Google's operational databases, such as Spanner, are separate products. Continuous queries can export results to Spanner in real time, which Google made generally available in March 2026, and they need an Enterprise or Enterprise Plus reservation. [15, 16, 18, 19, 17, 4, 7] |
| Cloud availability | Runs on AWS, Azure and Google Cloud. On Google Cloud, classic compute runs in your own Google Cloud account. | Runs in Google Cloud regions. It can query data held in AWS and Azure through BigQuery Omni, and through newer cross-cloud connections that are in Preview. [24, 2, 4] |
| Lock-in and portability | Tables stay in Delta or Iceberg in your own cloud storage on any of the three clouds, and any Iceberg client can read and write managed Iceberg tables through Unity Catalog's REST endpoint. Lakeflow pipelines, jobs and SQL warehouse features are Databricks-specific. | Native tables live in BigQuery's own storage. Iceberg managed tables and Delta Lake external tables give you open-format options, each with documented limitations. [14, 3, 9] |

## Pricing

Databricks: Databricks bills in Databricks Units (DBUs), a normalized unit of processing, metered per second and priced per product and cloud on its price list. You pay as you go or commit to usage for discounts. Storage, networking and, for classic compute, the virtual machines are billed by your cloud provider.

BigQuery: On-demand queries cost $6.25 per TiB processed in us-central1 (Iowa), and the first 1 TiB per month is free. Editions bill slot-hours per second with a one-minute minimum: $0.04 (Standard), $0.06 (Enterprise) and $0.10 (Enterprise Plus) pay as you go in us-central1, with one- and three-year commitments cheaper. Storage is billed per GiB with the first 10 GiB a month free, and tables unchanged for 90 days drop to long-term pricing, about 50% lower. [11, 1, 2]

## Using both

Yes. Databricks can query BigQuery in place through Lakehouse Federation, which reads through the BigQuery Storage API and pushes filters down, plus aggregates, joins and limits when materialization is on (that adds BigQuery compute charges). In the other direction, BigQuery can read Delta Lake tables in Cloud Storage as external tables, and Spark engines can read BigQuery's Apache Iceberg managed tables from exported Iceberg metadata. [12, 9, 3]

## Moving from BigQuery to Databricks

1. **Inventory the estate.** Inventory datasets, scheduled queries, BigQuery pipelines, BigQuery ML models and BI connections, and note which workloads run on on-demand pricing and which on reservations.
2. **Map projects and IAM.** Map BigQuery project.dataset.table to Unity Catalog catalog.schema.table, and IAM roles on datasets to Unity Catalog grants.
3. **Federate during the move.** Connect BigQuery through Lakehouse Federation so teams can query it from Databricks during the move; tables with INTERVAL columns fail to load through the connector, so plan those separately.
4. **Copy tables, convert SQL.** Copy tables into Delta or managed Iceberg tables, then convert GoogleSQL with Databricks' agentic code converter (Beta, lists BigQuery as a source dialect) and review scripts, UDFs and ML models by hand.
5. **Rebuild ingestion and streaming.** Rebuild ingestion and streaming: Data Transfer Service jobs and continuous queries become Lakeflow Connect ingestion and Spark Declarative Pipelines.
6. **Reconcile and switch off.** Run both systems in parallel, reconcile row counts and aggregates table by table, then repoint dashboards and switch off the BigQuery jobs.

## Questions

### Is Databricks cheaper than BigQuery?

They meter different things, so there's no general answer. On BigQuery, the decision to get right is on-demand against slots. On-demand bills $6.25 per TiB scanned in us-central1 after a free 1 TiB a month, so the cost follows how much data each query reads. Editions bill slot-hours instead, whatever the bytes scanned. Databricks bills DBUs per second plus your cloud provider's charges. Run a representative week of your own workload on both and compare the bills.

### Can Databricks run on Google Cloud?

Yes. Databricks runs on Google Cloud as well as AWS and Azure, with classic compute running in your own Google Cloud account. One gap to know about: Lakebase, its managed Postgres, is still in Beta on Google Cloud.

### Can we keep BigQuery and add Databricks?

You can. Databricks can query BigQuery tables in place through Lakehouse Federation, and BigQuery can read Delta Lake tables stored in Cloud Storage. You pay for compute on both sides when you do.

### Does BigQuery support Apache Iceberg?

Yes. Apache Iceberg managed tables, formerly called BigLake tables for Apache Iceberg in BigQuery, store data in your Cloud Storage bucket. Other engines read them from exported metadata, Google says writes should go through BigQuery, and features such as row-level security and table clones aren't supported on them.

### Which is better for AI work?

It depends on who does the work. BigQuery suits analysts who want ML and Gemini functions from SQL; BigQuery ML runs on on-demand pricing and on Enterprise or Enterprise Plus reservations, but not on Standard edition. Databricks suits engineering teams building custom models and agents that need to be governed alongside the data.

## Sources

1. [BigQuery pricing](https://cloud.google.com/bigquery/pricing), Google, read 2026-10-10
2. [Introduction to BigQuery editions](https://docs.cloud.google.com/bigquery/docs/editions-intro), Google, read 2026-10-10
3. [Apache Iceberg managed tables](https://docs.cloud.google.com/bigquery/docs/iceberg-tables), Google, read 2026-10-10
4. [BigQuery release notes](https://docs.cloud.google.com/bigquery/docs/release-notes), Google, read 2026-10-10
5. [About the Lakehouse runtime catalog](https://docs.cloud.google.com/bigquery/docs/about-blms), Google, read 2026-10-10
6. [Gemini in BigQuery overview](https://docs.cloud.google.com/bigquery/docs/gemini-overview), Google, read 2026-10-10
7. [Introduction to continuous queries](https://docs.cloud.google.com/bigquery/docs/continuous-queries-introduction), Google, read 2026-10-10
8. [BigQuery ML overview](https://docs.cloud.google.com/bigquery/docs/bqml-introduction), Google, read 2026-10-10
9. [Create BigLake external tables for Delta Lake](https://docs.cloud.google.com/bigquery/docs/create-delta-lake-table), Google, read 2026-10-10
10. [Gemini Enterprise Agent Platform release notes](https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes), Google, read 2026-10-10
11. [Databricks pricing](https://www.databricks.com/product/pricing), Databricks, read 2026-10-10
12. [Run federated queries on Google BigQuery](https://docs.databricks.com/aws/en/query-federation/bigquery), Databricks, read 2026-10-10
13. [Databricks platform release notes, May 2026](https://docs.databricks.com/aws/en/release-notes/product/2026/may), Databricks, read 2026-10-10
14. [Access Databricks tables from Apache Iceberg clients](https://docs.databricks.com/aws/en/external-access/iceberg), Databricks, read 2026-10-10
15. [Lakebase release notes](https://docs.databricks.com/aws/en/release-notes/lakebase/), Databricks, read 2026-10-10
16. [Databricks Lakebase is now Generally Available](https://www.databricks.com/blog/databricks-lakebase-generally-available), Databricks, read 2026-10-10
17. [Azure Databricks Lakebase is Generally Available](https://www.databricks.com/blog/azure-databricks-lakebase-generally-available), Databricks, read 2026-10-10
18. [Lakebase Postgres (Google Cloud)](https://docs.databricks.com/gcp/en/oltp/projects/), Databricks, read 2026-10-10
19. [Lakebase Postgres (AWS)](https://docs.databricks.com/aws/en/oltp/projects/), Databricks, read 2026-10-10
20. [Data engineering with Databricks (Lakeflow)](https://docs.databricks.com/aws/en/data-engineering/), Databricks, read 2026-10-10
21. [What is Unity Catalog?](https://docs.databricks.com/aws/en/data-governance/unity-catalog/), Databricks, read 2026-10-10
22. [SQL warehouse types](https://docs.databricks.com/aws/en/compute/sql-warehouse/warehouse-types), Databricks, read 2026-10-10
23. [Convert SQL with the agentic code converter](https://docs.databricks.com/aws/en/migration/lakebridge-agentic-converter), Databricks, read 2026-10-10
24. [Databricks architecture overview (Google Cloud)](https://docs.databricks.com/gcp/en/getting-started/overview), Databricks, read 2026-10-10
25. [Agent Bricks Knowledge Assistant is now generally available](https://www.databricks.com/blog/agent-bricks-knowledge-assistant-now-generally-available-turning-enterprise-knowledge-answers), Databricks, read 2026-10-10
26. [AI and machine learning on Databricks](https://docs.databricks.com/aws/en/machine-learning/), Databricks, read 2026-10-10
27. [Announcing General Availability of Real-Time Mode for Apache Spark Structured Streaming on Databricks](https://www.databricks.com/blog/announcing-general-availability-real-time-mode-apache-spark-structured-streaming-databricks), Databricks, read 2026-10-10