Migration Services
Databricks to BigQuery Migration Services
Move Databricks data, SQL, pipelines, and analytical workloads to BigQuery through a structured migration designed to improve scalability and governance.
Migration Services
Move Databricks data, SQL, pipelines, and analytical workloads to BigQuery through a structured migration designed to improve scalability and governance.
Why BigQuery?
Run large-scale analytical workloads without managing SQL warehouses, clusters, or underlying infrastructure.
Consolidate suitable lakehouse tables and analytical data into a managed BigQuery environment.
Use BigLake to govern and query supported open-format data, including Delta Lake tables stored in Cloud Storage.
Connect enterprise data with Looker, Dataform, Dataflow, Dataproc, Vertex AI, Gemini, and other Google Cloud services.
How We Migrate
Review Databricks catalogs, schemas, Delta tables, SQL warehouses, notebooks, jobs, pipelines, dashboards, permissions, and dependencies.
Define the BigQuery data model, target datasets, migration waves, storage approach, security controls, pipelines, and validation plan.
Move or expose lakehouse data, recreate tables and views, and adapt Databricks SQL, transformations, and analytical logic for BigQuery.
Transition ingestion, transformation, scheduling, and orchestration to BigQuery, Dataform, Dataflow, Dataproc, or Cloud Composer.
Compare data and query results before cutover, then optimize partitioning, clustering, capacity, performance, and cost controls.
Why Codimite?
Codimite combines Google Cloud architecture, data engineering, SQL modernization, and structured validation to support the complete migration.
Start Your MigrationWorkload-Based Architecture. We determine which workloads belong in BigQuery and which require Dataproc, Dataflow, BigLake, or another supporting service.
Delta Lake Migration Planning. We assess managed and external Delta tables and select native BigQuery, BigLake, or phased coexistence approaches.
SQL and Pipeline Modernization. We adapt Databricks SQL, notebooks, transformations, jobs, and dependencies for Google Cloud.
Controlled Cutover. We reduce migration risk through prioritized workloads, parallel validation, phased releases, and rollback planning.
Platform Comparison
Both platforms support enterprise analytics, but BigQuery provides a serverless, SQL-centered data platform with deep Google Cloud integration.
| Area | Databricks | BigQuery |
|---|---|---|
| Platform approach | Lakehouse platform for engineering, analytics, governance, and AI | Fully managed, serverless data platform for analytics and AI-ready data |
| Data architecture | Delta Lake tables stored in cloud object storage | Managed BigQuery tables with BigLake access to supported open formats |
| Analytics engine | Databricks SQL and Spark-based processing | Serverless GoogleSQL engine designed for large-scale analytics |
| Infrastructure | Uses SQL warehouses, jobs compute, clusters, or serverless compute | Google manages analytical infrastructure, scaling, maintenance, and availability |
| Scaling | Depends on warehouse, cluster, job, and serverless configuration | Storage and analytical compute scale independently according to workload demand |
| SQL | ANSI SQL with Databricks and Delta Lake extensions | GoogleSQL with analytical, geospatial, search, and machine-learning capabilities |
| Data storage | Data generally remains in lakehouse object storage | Managed columnar storage with native tables and external data access options |
| Open formats | Delta Lake with support for selected additional formats | BigLake supports governed access to Delta Lake, Iceberg, and other supported formats |
| Optimization | Partitioning, clustering, liquid clustering, Photon, and table maintenance | Partitioning, clustering, materialized views, caching, and managed query optimization |
| Data engineering | Spark notebooks, jobs, Lakeflow pipelines, and SQL | BigQuery SQL, Dataform, Dataflow, Dataproc, and Cloud Composer integrations |
| Streaming | Spark Structured Streaming and Databricks pipelines | BigQuery streaming, Pub/Sub, Dataflow, and Datastream integrations |
| Business intelligence | Databricks dashboards and third-party BI tools | Native alignment with Looker and support for third-party BI tools |
| Machine learning | Databricks ML, MLflow, feature engineering, and model serving | BigQuery ML with direct integration into Vertex AI and Gemini |
| Governance | Unity Catalog governs data and AI assets | IAM, BigQuery controls, Dataplex capabilities, policy tags, lineage, and audit logging |
| Workload management | SQL warehouse configuration, serverless settings, and compute policies | Reservations, assignments, autoscaling, quotas, priorities, and workload isolation |
| Pricing | Platform units plus underlying cloud infrastructure and storage | On-demand or capacity-based analytical pricing with managed infrastructure |
| Operations | Teams manage workspaces, compute policies, assets, and platform configuration | Google manages infrastructure while teams focus on data products and analytics |
| Best suited for | Unified Spark-oriented lakehouse workloads | Serverless enterprise SQL analytics and Google Cloud-aligned data workloads |
FAQs
Codimite can migrate Delta tables, schemas, views, SQL workloads, dashboards, notebooks, jobs, pipelines, permissions, and connected analytical processes.
Not always. BigQuery is well suited to serverless SQL analytics and managed data workloads, while Spark-based processing or specialized engineering jobs may move to Dataproc, Dataflow, or another Google Cloud service.
Delta tables may be migrated into native BigQuery tables, accessed through BigLake external tables, converted to another supported format, or retained temporarily during a phased migration.
Some SQL can be translated or adapted, but Databricks-specific functions, Delta operations, Spark logic, notebook code, schedules, dependencies, and retries often require manual redesign.
Catalogs, schemas, ownership, permissions, classifications, and lineage are mapped to Google Cloud IAM, BigQuery access controls, policy tags, metadata, and lineage capabilities.
We compare schemas, row counts, aggregates, transformations, SQL outputs, permissions, refresh behavior, performance, and downstream reports before production cutover.
Assess your Databricks data, SQL, pipelines, and governance requirements to build a practical BigQuery migration roadmap.
Start Your Migration
