Databricks vs BigQuery
An independent, sourced comparison. Every row links to the vendor's own docs and carries a verified date.
By brickster.ai · feature data verified Sep 2, 2026 · live signals refreshed Sep 5, 2026
The short answer
Pick Databricks if data engineering, machine learning, or AI agents are your center of gravity and you want one open platform you control across clouds. Pick BigQuery if you want a serverless, SQL-first warehouse that needs almost no operation and sits inside Google Cloud next to Pub/Sub, Looker, and Sheets. Since April 2026 the two interoperate over Iceberg, so on Google Cloud a split is a real option.
Databricks is the Data Intelligence Platform: Spark and Photon compute running in your cloud account over open table formats (Delta Lake natively, managed Apache Iceberg read and write GA), with Unity Catalog governing data and AI assets. It runs the same way on AWS, Azure, and Google Cloud, where classic compute launches on Google Compute Engine in your project and billing flows through Google Cloud Marketplace.
BigQuery is Google's serverless data warehouse, and in 2026 it is trying to be a lakehouse too. You write GoogleSQL, Google runs it: no clusters, no runtimes, capacity either paid per TiB scanned or per slot-hour. Around it sit Apache Iceberg managed tables (formerly BigLake tables for Apache Iceberg), the Knowledge Catalog (formerly Dataplex Universal Catalog), free Gemini assistance in the console, and in-SQL machine learning. The platforms meet in the middle now: BigQuery can read Unity Catalog tables and Databricks can query BigQuery, both over open interfaces, so the choice is increasingly about where each workload runs best rather than which vendor wins outright.
Choose Databricks if
- Data engineering is heavy, code-first, or genuinely streaming. Lakeflow ingestion and declarative pipelines, Auto Loader, and Structured Streaming with sub-second latency go further than SQL-plus-services composition, and BigQuery's own continuous queries require Enterprise edition with a dedicated reservation and keep stateful operations in preview.
- Machine learning and AI agents are central. Databricks covers Model Serving, AI Search, the Unity AI Gateway, and an Agent Framework with managed MCP servers (public preview), next to MLflow, a feature store, AutoML, and GPU compute. BigQuery ML trains a fixed menu of model types in SQL and hands deep learning to the Gemini Enterprise Agent Platform (formerly Vertex AI), a second product with its own pricing.
- Multicloud or portability matters. BigQuery is Google Cloud only; Databricks runs the same platform, catalog, and pipelines on all three major clouds.
- You want one governance model for tables, files, models, and agents. Unity Catalog spans them with ABAC, lineage, and an open-source core; BigQuery splits the job between IAM, policy tags, and the Knowledge Catalog.
- Spark is your team's language. BigQuery's engine is SQL; its Spark story runs through separate serverless Spark sessions, while Databricks is Spark-native end to end.
Choose BigQuery if
- You want zero-ops SQL above all. No clusters, no warehouses to size, no runtime versions: BigQuery's serverless model is the lowest-operations warehouse of the platforms we compare, and the sandbox tier works without a credit card.
- Your stack is Google. Pub/Sub streams into BigQuery natively with no pipeline, Looker and Connected Sheets query it directly, and Gemini in BigQuery's core assistance (SQL and Python generation, data canvas, data insights) is free across all compute options.
- Entry economics and simplicity: $6.25 per TiB scanned on demand with the first 1 TiB per month free, 10 GiB of free storage, an automatic 50% long-term storage discount for untouched tables, and free batch loading and export.
- Analytics on arriving data with SQL alone. Pub/Sub subscriptions write straight into tables, the Storage Write API gives exactly-once streaming with the first 2 TiB per month free, and continuous queries run always-on SQL if you are on Enterprise edition.
- Your ML needs fit in SQL. BigQuery ML trains regression, classification, clustering, PCA, and ARIMA_PLUS forecasting where the data already lives (matrix factorization needs Enterprise reservations), with vector search GA for embeddings workloads.
Databricks vs BigQuery, measure by measure
Every cell links to the vendor's own product, pricing, or docs page and shows when it was last verified. It quotes them, it doesn't score a winner.
| Measure | Databricks Lakehouse (Spark + Photon) | BigQuery Serverless data warehouse |
|---|---|---|
| Architecture & openness | ||
| ArchitecturePlatform shape | Data + AI Platform (lakehouse) source · verified 2026-09-02 | Serverless warehouse; Lakehouse (was BigLake) source · verified 2026-09-02 |
| Compute engineUnderlying query engine | Apache Spark + Photon source · verified 2026-09-02 | Dremel (proprietary engine) source · verified 2026-09-02 |
| Storage / compute separationIndependent scaling | Decoupled storage and compute source · verified 2026-09-02 | Fully decoupled (Colossus + Dremel) source · verified 2026-09-02 |
| Native table formatDelta / Iceberg / proprietary | Delta Lake (and managed Iceberg) source · verified 2026-09-02 | Proprietary (Capacitor columnar) source · verified 2026-09-02 |
| Apache IcebergRead + write support | Native managed Iceberg, read+write GA source · verified 2026-09-02 | Managed Iceberg tables, read+write source · verified 2026-09-02 |
| Delta LakeRead / write Delta tables | Native Delta read/write source · verified 2026-09-02 | Read-only external tables for Delta Lake source · verified 2026-09-02 |
| Open / REST catalogIceberg REST / open catalog | Unity Catalog Iceberg REST catalog source · verified 2026-09-02 | Iceberg REST catalog (Lakehouse runtime catalog) source · verified 2026-09-02 |
| Open-source coreEngine / format open source | Spark, Delta, Unity Catalog open source source · verified 2026-09-02 | Engine proprietary; Iceberg format open source · verified 2026-09-02 |
| Multi-cloudAWS / Azure / GCP | AWS, Azure, GCP source · verified 2026-09-02 | GCP native; AWS/Azure via BigQuery Omni source · verified 2026-09-02 |
| Deployment modelSaaS vs your cloud account | Runs in your cloud account source · verified 2026-09-02 | SaaS-only (Google Cloud managed) source · verified 2026-09-02 |
| Cost & pricing | ||
| Billing unit | Per-DBU source · verified 2026-09-02 | On-demand per-TB or slot capacity source · verified 2026-09-02 |
| Billing granularityPer-second / minute / hour | Per-second source · verified 2026-09-02 | Slots per-sec; 1-min minimum unless fluid scaling source · verified 2026-09-02 |
| Scale-to-zero serverlessAuto-suspend | Serverless SQL/compute, auto-suspend source · verified 2026-09-02 | Serverless; editions autoscale to zero source · verified 2026-09-02 |
| Separate infra billCompute billed apart from VM / storage | Classic: separate VM bill; serverless bundled source · verified 2026-09-02 | Compute/storage billed by BigQuery directly source · verified 2026-09-02 |
| Storage pricing$ / TB-month | Cloud bills own storage; default storage in DSUs source · verified 2026-09-02 | ~$0.02/GB-mo active; $0.01 long-term source · verified 2026-09-02 |
| Free tier / trial | Free Edition + 14-day trial source · verified 2026-09-02 | 1 TB queries + 10 GB storage/month source · verified 2026-09-02 |
| Committed-use discounts | Committed-use contracts source · verified 2026-09-02 | 1yr/3yr capacity commitments discounted source · verified 2026-09-02 |
| Cost observabilityUsage / cost monitoring | System tables, usage dashboards, budgets source · verified 2026-09-02 | Cost dashboards, slot/admin monitoring source · verified 2026-09-02 |
| Pricing transparencyPublished vs custom-quote | List DBU prices published source · verified 2026-09-02 | List prices published publicly source · verified 2026-09-02 |
| SQL & query | ||
| ANSI SQL coverageWindow, recursive CTE | ANSI SQL incl. window, recursive CTE source · verified 2026-09-02 | GoogleSQL; window + recursive CTE source · verified 2026-09-02 |
| Semi-structured dataJSON / VARIANT | Native VARIANT and JSON support source · verified 2026-09-02 | Native JSON data type source · verified 2026-09-02 |
| GeospatialGeo types + functions | Spatial SQL GA, GEOMETRY/GEOGRAPHY, H3 source · verified 2026-09-02 | GEOGRAPHY type + GIS functions source · verified 2026-09-02 |
| User-defined functionsSQL / Python / Java | SQL, Python, Scala, Java UDFs source · verified 2026-09-02 | SQL, JavaScript, Python UDFs source · verified 2026-09-02 |
| Materialized views | Native materialized views source · verified 2026-09-02 | Native materialized views source · verified 2026-09-02 |
| Query result caching | Query result caching source · verified 2026-09-02 | Automatic query result cache source · verified 2026-09-02 |
| Query federationQuery external sources in place | Lakehouse Federation source · verified 2026-09-02 | Cloud SQL, Spanner, external sources source · verified 2026-09-02 |
| Data engineering | ||
| Batch ETL / ELT toolingNative pipeline tooling | Lakeflow pipelines, Lakeflow Jobs source · verified 2026-09-02 | Dataform, Dataflow, Data Transfer Service source · verified 2026-09-02 |
| Streaming ingestion | Structured Streaming, Real-Time Mode source · verified 2026-09-02 | Storage Write API streaming ingest source · verified 2026-09-02 |
| Change data capture | CDC via AUTO CDC APIs, Lakeflow Connect source · verified 2026-09-02 | Native CDC ingest; Datastream source · verified 2026-09-02 |
| Auto file ingestionAuto Loader / Snowpipe class | Auto Loader source · verified 2026-09-02 | DTS event-driven Cloud Storage transfers source · verified 2026-09-02 |
| Native orchestrationJobs / scheduler | Lakeflow Jobs source · verified 2026-09-02 | Scheduled queries, Dataform workflows source · verified 2026-09-02 |
| dbt support | First-class dbt adapter and task source · verified 2026-09-02 | First-class dbt adapter source · verified 2026-09-02 |
| Declarative pipelinesDLT / Lakeflow-style | Lakeflow pipelines (Spark Declarative Pipelines) source · verified 2026-09-02 | Dataform SQL declarative pipelines source · verified 2026-09-02 |
| ML & AI | ||
| Model trainingNative, on-platform | Native training on Spark/GPU clusters source · verified 2026-09-02 | BigQuery ML trains models in SQL source · verified 2026-09-02 |
| Feature store | Native feature store in Unity Catalog source · verified 2026-09-02 | Agent Platform Feature Store on BigQuery source · verified 2026-09-02 |
| Experiment trackingMLflow or equivalent | Managed MLflow source · verified 2026-09-02 | Agent Platform Experiments (separate product) source · verified 2026-09-02 |
| Model servingHost / inference | Databricks Model Serving (real-time, batch) source · verified 2026-09-02 | Serving via Agent Platform endpoints source · verified 2026-09-02 |
| AutoML | AutoML models in BigQuery ML source · verified 2026-09-02 | |
| Vector searchEmbeddings index | Databricks AI Search (formerly Vector Search) source · verified 2026-09-02 | Native vector index + VECTOR_SEARCH source · verified 2026-09-02 |
| Foundation-model gatewayGoverned multi-model access | Unity AI Gateway (multi-provider routing) source · verified 2026-09-02 | Gemini via ML.GENERATE_TEXT/AI functions source · verified 2026-09-02 |
| Text-to-SQLNL-to-SQL assistant | Genie Agents (formerly Genie Spaces) source · verified 2026-09-02 | Gemini SQL generation in editor source · verified 2026-09-02 |
| Agents / MCPAgent framework + MCP server | Agent Bricks, Agent Framework, MCP (preview) source · verified 2026-09-02 | Data agents + BigQuery MCP server source · verified 2026-09-02 |
| GPU compute | GPU instances for ML source · verified 2026-09-02 | GPUs via Agent Platform, not native BigQuery source · verified 2026-09-02 |
| BI & consumption | ||
| Native dashboards / BI | AI/BI Dashboards source · verified 2026-09-02 | BI Engine; Looker Studio dashboards source · verified 2026-09-02 |
| Semantic / metrics layer | Unity Catalog Metric Views source · verified 2026-09-02 | Looker; BigQuery Graph measures (preview) source · verified 2026-09-02 |
| Notebooks | Native notebooks source · verified 2026-09-02 | Native notebooks in BigQuery Studio source · verified 2026-09-02 |
| Natural-language BIAsk-your-data | Genie One and Genie Agents natural language source · verified 2026-09-02 | Data Canvas natural-language analytics source · verified 2026-09-02 |
| BI tool integrationsTableau / Power BI / Looker | Tableau, Power BI, Looker connectors source · verified 2026-09-02 | Tableau, Power BI, Looker connectors source · verified 2026-09-02 |
| Governance & security | ||
| Unified governance catalogOne catalog across data + AI | Unity Catalog across data and AI source · verified 2026-09-02 | Knowledge Catalog (formerly Dataplex) source · verified 2026-09-02 |
| Fine-grained RBAC | Fine-grained RBAC in Unity Catalog source · verified 2026-09-02 | IAM roles, fine-grained permissions source · verified 2026-09-02 |
| Attribute-based access controlTag-based policies | ABAC with governed tags, GA source · verified 2026-09-02 | Policy tags / taxonomy-based access source · verified 2026-09-02 |
| Column masking | Dynamic column masks source · verified 2026-09-02 | Dynamic data masking via policy tags source · verified 2026-09-02 |
| Row-level security | Row filters source · verified 2026-09-02 | Row-level access policies source · verified 2026-09-02 |
| Data lineageAutomatic | Automatic lineage in Unity Catalog source · verified 2026-09-02 | Automatic lineage via Knowledge Catalog source · verified 2026-09-02 |
| Data classificationAuto PII discovery | Automated data classification GA source · verified 2026-09-02 | Automatic PII discovery via Knowledge Catalog source · verified 2026-09-02 |
| Audit logging | Audit logs / system tables source · verified 2026-09-02 | Cloud Audit Logs for access source · verified 2026-09-02 |
| Customer-managed keysCMK / BYOK | Customer-managed keys source · verified 2026-09-02 | CMEK via Cloud KMS source · verified 2026-09-02 |
| Private networkingPrivateLink / VPC | PrivateLink, VNet/VPC injection source · verified 2026-09-02 | VPC Service Controls, Private Service Connect source · verified 2026-09-02 |
| Sharing & collaboration | ||
| Data sharingCross-account / cross-cloud | OpenSharing (formerly Delta Sharing), any cloud source · verified 2026-09-02 | BigQuery sharing / Analytics Hub source · verified 2026-09-02 |
| Clean rooms | Clean Rooms GA source · verified 2026-09-02 | Native data clean rooms source · verified 2026-09-02 |
| Marketplace | Databricks Marketplace source · verified 2026-09-02 | Google Cloud Marketplace / data exchanges source · verified 2026-09-02 |
| Operations & reliability | ||
| Public status APIMachine-readable uptime | Public Status API; email, webhook, Slack alerts source · verified 2026-09-02 | Google Cloud status dashboard published source · verified 2026-09-02 |
| Published SLA | 99.9% control plane SLA; Lakebase SLA published source · verified 2026-09-02 | 99.99% uptime SLA published source · verified 2026-09-02 |
| Auto-scaling | Cluster autoscaling source · verified 2026-09-02 | Autoscaling slots in editions source · verified 2026-09-02 |
| Multi-region / DR | Managed DR (gated preview) via Mission Critical source · verified 2026-09-02 | Cross-region dataset replication, multi-region source · verified 2026-09-02 |
| Workload isolationIsolate ETL vs BI | Separate warehouses/clusters per workload source · verified 2026-09-02 | Reservations isolate ETL vs BI source · verified 2026-09-02 |
| Ecosystem & support | ||
| Partner connectors | Lakeflow Connect 100+ sources source · verified 2026-09-02 | Data Transfer Service + partner connectors source · verified 2026-09-02 |
| Compliance certificationsSOC 2 / HIPAA / FedRAMP / ISO | SOC 2, HIPAA, PCI-DSS, FedRAMP, ISO source · verified 2026-09-02 | SOC 2, HIPAA, ISO, PCI, FedRAMP source · verified 2026-09-02 |
| Global regions | Dozens of regions across AWS/Azure/GCP source · verified 2026-09-02 | 40+ Google Cloud regions source · verified 2026-09-02 |
| Support tiers | Tiered support plans source · verified 2026-09-02 | Standard, Enhanced, Premium support source · verified 2026-09-02 |
Architecture and openness
Databricks runs open formats in your own storage: Delta Lake natively, managed Iceberg read and write GA, and a Unity Catalog Iceberg REST endpoint that external engines read. BigQuery inverts the control: Google manages storage and compute, you bring SQL. Its opening move is real, though. Apache Iceberg managed tables (GA, with partitioning and multi-statement transactions GA since July 2026) keep data as Iceberg in your own Cloud Storage bucket, and the Lakehouse runtime catalog (formerly BigLake metastore) exposes an Iceberg REST catalog that Spark, Flink, and Trino can use. Delta is second-class in BigQuery: readable via Delta Lake BigLake external tables (GA since 2024) but read-only. The exit stories differ in kind: leaving Databricks means pointing another engine at files you already own; leaving BigQuery is genuinely cheap (free batch export, 300 TiB per month of free Storage Read API) but native-table data lives in Google's managed storage until you move it. Both are far more open than they were two years ago; Databricks is open by default, BigQuery is open by option.
Pricing and cost model
Three meters, not two. BigQuery on-demand bills $6.25 per TiB scanned (US, first 1 TiB per month free): zero commitment, and a query that scans little costs little, but an accidental full-table scan is real money. BigQuery editions bill slot-hours (Standard $0.04, Enterprise $0.06, Enterprise Plus $0.10 in the US multi-region), autoscaling in 50-slot steps, billed per second with a one-minute minimum by default, with commitment discounts from 10% to 40% depending on track and term. Databricks meters DBUs per workload: on Google Cloud Premium in US (Virginia), jobs compute is $0.15 per DBU plus the GCE infrastructure, all-purpose interactive $0.55 plus infrastructure, and SQL runs $0.22 classic, $0.55 pro, or $0.70 serverless with compute bundled. Rules of thumb: intermittent, scan-light SQL is hard to beat on BigQuery on-demand; steady heavy SQL becomes a slots-versus-DBU calculation; and non-SQL work (pipelines, ML, agents) never touches BigQuery's meters at all, it runs on other Google products with their own bills. Model your own workload before believing anyone's math, including ours.
Data engineering and streaming
Databricks does its engineering in one product: Lakeflow Connect for managed ingestion, declarative pipelines for ETL, Jobs for orchestration, Auto Loader for incremental files, and Structured Streaming with Real-Time Mode for true streaming, all in Spark with Photon underneath. BigQuery composes Google Cloud services: batch loads are free, Pub/Sub subscriptions write directly into tables with no pipeline code, the Storage Write API streams with exactly-once semantics ($0.025 per GiB after a free 2 TiB per month), Dataflow handles heavy transformation, and Google's Data Engineering Agent (GA since April 2026) generates and maintains pipelines from natural language. BigQuery's always-on SQL answer, continuous queries, is gated: Enterprise or Enterprise Plus only, a dedicated reservation, and stateful operations like joins still in preview. The split that holds: SQL-shaped ELT with Google-native sources favors BigQuery; complex, custom, or latency-sensitive engineering favors Databricks.
Machine learning and AI
Databricks treats ML and agents as the core product: Model Serving, AI Search, the Unity AI Gateway, an Agent Framework with managed MCP servers (public preview), MLflow, feature store, AutoML, GPU compute. BigQuery treats ML as a SQL feature and ships its agents as Google-run services around the warehouse. BigQuery ML is genuinely useful where it fits: CREATE MODEL trains regression, classification, k-means, PCA, and ARIMA_PLUS forecasting in place (on-demand training of the built-in model types bills $312.50 per TiB processed, matrix factorization requires Enterprise reservations, and BigQuery ML is unavailable on the Standard edition), with TimesFM forecasting built in and vector search GA. Deep learning and custom serving hand off to the Gemini Enterprise Agent Platform (formerly Vertex AI), a separate product. Where Google leads is assistance: Gemini in BigQuery's core features (SQL and Python generation, data canvas, data insights, data preparation) are GA and free on every compute option, and conversational analytics data agents answer business questions over curated sources. If you are building and operating models and agents, Databricks goes much further in one place. If you are augmenting analysts with AI, BigQuery's free Gemini layer is a genuine strength.
BI and consumption
BigQuery's consumption story is the Google ecosystem: Looker and Looker Studio query it as their native backend, Connected Sheets puts billions of rows behind a spreadsheet interface (with built-in TimesFM forecasting), BI Engine accelerates dashboards in memory at $0.0416 per GiB-hour with free capacity bundled into editions commitments, and conversational analytics agents give business users a governed natural-language surface. Databricks consumption is younger but converging fast: AI/BI Dashboards, Genie for natural-language questions grounded in Unity Catalog, and Genie One as the business-user front door, plus first-class connectors to Power BI, Tableau, and Looker over SQL warehouses. Both platforms can sit behind any BI tool; the question is which native surface your users adopt. Sheets-and-Looker shops will feel at home on BigQuery; teams standardizing on one governed data-and-AI platform get further with Databricks.
Governance, and the case for both
Unity Catalog governs tables, files, models, functions, and agents in one hierarchy: ABAC GA, row filters and column masks, automated lineage across data and AI, an open-source core, and an Iceberg REST endpoint that extends governance to outside engines. BigQuery splits governance across Google Cloud: IAM for access, policy tags with dynamic masking for column-level security (GA), auto-captured lineage, and the Knowledge Catalog (formerly Dataplex Universal Catalog, renamed April 2026) for search, profiling, and quality across the estate. Both work; Unity Catalog is the more unified model, Knowledge Catalog reaches wider across non-BigQuery Google services. The 2026 fact most comparisons miss: the two now federate. Google announced catalog federation in preview on April 29, 2026, letting BigQuery read Unity Catalog managed tables without copying data, and Databricks reading Iceberg tables managed by Google's Cloud Lakehouse is in private preview, both over the Iceberg REST protocol. Add Lakehouse Federation (Databricks querying BigQuery directly, no preview badge in the docs) and Delta Lake BigLake external tables (BigQuery reading Delta, GA, read-only), and running Databricks for engineering and ML beside BigQuery for Google-ecosystem analytics is a supported architecture, not a hack.
Databricks vs BigQuery pricing
The units are different shapes: BigQuery bills per TiB scanned (on-demand) or per slot-hour (editions), Databricks per DBU per workload with GCE infrastructure billed separately on classic compute. Non-SQL work never hits BigQuery's meters at all; it runs on separate Google products. Model your own workload, and watch the scan meter on demand.
Databricks
Databricks on Google Cloud is subscribed through Google Cloud Marketplace and billed to your Google account. Premium plan list rates in US (Virginia), read from the pricing pages in early September 2026: jobs compute $0.15 per DBU (classic; the GCE VMs bill separately) or $0.35 per DBU serverless all-in, all-purpose interactive $0.55 per DBU plus infrastructure or $0.75 serverless, and Databricks SQL at $0.22 classic, $0.55 pro, and $0.70 serverless with compute included. Photon on classic all-purpose compute emits DBUs at 2x. Pre-purchase commit discounts exist via marketplace private offers. A 14-day trial runs serverless on Databricks-managed infrastructure, and Free Edition covers personal, non-commercial use.
BigQuery
BigQuery offers two compute models. On-demand: $6.25 per TiB scanned (US multi-region), first 1 TiB per month free, minimum 10 MB billed per table referenced. Editions: Standard $0.04, Enterprise $0.06, Enterprise Plus $0.10 per slot-hour (US multi-region), autoscaling in 50-slot steps, per-second billing with a one-minute minimum by default; spend-based commitments cut 10% (1-year) or 20% (3-year), and resource commitments on Enterprise tiers cut 20% or 40%. Storage in US multi-region: $0.02 per GiB-month active logical, halving to $0.01 for tables untouched 90 days, with compressed physical billing at $0.04/$0.02 as a dataset-level option and 10 GiB free monthly. Streaming, at US regional rates such as Iowa: inserts over the older REST path $0.01 per 200 MiB, Storage Write API $0.025 per GiB with 2 TiB free monthly. Batch loading and export are free; BigQuery ML on-demand model training bills $312.50 per TiB.
Sources: BigQuery pricing, BigQuery editions and autoscaling, Databricks SQL pricing (set GCP), Lakeflow Jobs pricing (set GCP), UC and BigQuery interop announcement.
Frequently asked questions
Is BigQuery cheaper than Databricks?
For intermittent, scan-light SQL, usually yes: $6.25 per TiB with a free first TiB each month and zero idle cost is a very low floor. For sustained heavy SQL it becomes a slots-versus-DBU calculation that depends on utilization. And for pipelines, ML, and agents the comparison breaks down, because that work never touches BigQuery's meters; it runs on other Google products with their own bills, while Databricks prices it as DBUs on one platform.
What is the main difference between Databricks and BigQuery?
Databricks is an open, multicloud data and AI platform: Spark-native compute in your cloud account, open table formats you control, one governance model across data, models, and agents. BigQuery is a serverless, SQL-first warehouse inside Google Cloud: Google runs everything, you write GoogleSQL, and the surrounding ecosystem (Pub/Sub, Looker, Sheets, Gemini) does the rest.
Can Databricks and BigQuery work together?
Yes, and since April 2026 by design. Lakehouse Federation lets Databricks query BigQuery in place. In the other direction, Google's catalog federation (announced in preview April 29, 2026) lets BigQuery read Unity Catalog managed tables over the Iceberg REST protocol without copying data, and BigQuery has read Delta tables through BigLake external tables since 2024, read-only. Engineering and ML on Databricks with Google-ecosystem analytics on BigQuery is a supported split.
Is Databricks or BigQuery better for machine learning?
Databricks, for building and operating models: MLflow, a feature store, GPU compute, model serving, vector search, and an agent framework live in one platform. BigQuery ML is excellent for SQL-shaped ML (forecasting, classification, clustering in place) but hands deep learning and custom serving to the separate Gemini Enterprise Agent Platform, and it is unavailable on the Standard edition. For AI assistance for analysts, BigQuery's free Gemini features are ahead.
Is BigQuery a lakehouse now?
It is closer than most people think. Apache Iceberg managed tables are GA and keep data as Iceberg in your own Cloud Storage bucket, the Lakehouse runtime catalog exposes an Iceberg REST endpoint for Spark, Flink, and Trino, and Google literally renamed BigLake to Lakehouse in April 2026. The caveats: Delta support is read-only, and the deepest engine features still assume BigQuery-managed native tables.
How hard is it to leave each platform?
Leaving Databricks means pointing another engine at open-format files already in your own storage; Unity Catalog's Iceberg REST endpoint even lets outside engines read managed tables in place. Leaving BigQuery is operationally easy and cheap (batch export is free, the Storage Read API gives 300 TiB per month free) but it is a data move: native tables live in Google-managed storage until you export them, unless you adopted Iceberg managed tables in your own buckets from the start.
Does Databricks run well on Google Cloud?
Yes. It is subscribed through Google Cloud Marketplace, billed through your Google account, and classic compute launches Google Compute Engine VMs in your own project (the early GKE-based architecture is history). Serverless SQL, jobs, and notebooks are available, and the BigQuery integrations (Lakehouse Federation, the catalog interop, the Spark connector) are first-class, so mixed estates are normal.
How this comparison works
- Every cell in the table links to the vendor's own documentation and shows when it was last verified. We quote them, we don't run our own benchmarks.
- The feature data is a slow-moving snapshot, re-checked periodically. The open-source momentum and refresh date update daily via our pipeline.
- brickster.ai is independent and not affiliated with Databricks, BigQuery, or any vendor. If something looks wrong, tell us.