On-Prem Warehouse to BigQuery Blueprint
A blueprint to migrate an on-prem warehouse to Google BigQuery using the Migration Service for SQL translation, Cloud Storage staging with bq load/Datastream, ELT via dbt/Dataform, and cost-aware partitioning.
Overview
Google BigQuery is a serverless, fully managed data warehouse that scales to petabytes with no infrastructure to manage. Storage and compute are decoupled, and queries run on demand. Migrating an on-prem warehouse to BigQuery removes capacity planning and unlocks elastic analytics. This blueprint covers schema, data, and pipeline migration.
Phases
Assessment. Inventory schemas, views, procedures, and ETL jobs. Use the BigQuery Migration Service to assess SQL dialect translation and estimate effort. Profile query patterns and data volume.
Schema conversion. Recreate datasets and tables, mapping types to BigQuery (STRING, NUMERIC, TIMESTAMP, STRUCT, ARRAY). Replace distribution/index strategies with partitioning (by date) and clustering columns to control scan cost.
Data load. Stage exports as Parquet/Avro in Cloud Storage and load with bq load or the BigQuery Data Transfer Service. Use the Storage Write API or Datastream for ongoing/CDC loads.
Pipeline migration. Translate SQL with the BigQuery Migration Service SQL translator. Move transforms to ELT (dbt or Dataform). Re-point orchestration (Cloud Composer/Airflow).
Validation. Reconcile counts and checksums; compare query outputs and BI dashboards against the source.
Cutover. Switch consumers to BigQuery, run in parallel briefly, then retire the on-prem warehouse.
Key Risks and Mitigations
- Data consistency: Reconcile counts/checksums per table and validate dashboards before cutover.
- Cost overrun: BigQuery on-demand pricing bills by bytes scanned. Partition and cluster tables, avoid
SELECT *, and consider capacity (slot) reservations for predictable spend. - Skills gap: Train teams on partitioning, clustering, and slot management.
Recommended Tooling
BigQuery Migration Service (assessment and SQL translation); Cloud Storage plus bq load/Data Transfer Service; Datastream or Storage Write API for CDC; dbt or Dataform for ELT; BigQuery's query plan and bytes-billed metrics for cost tuning.
Success Metrics
Lower query latency, reduced bytes scanned per query (scan cost), and overall cost reduction versus fixed on-prem hardware.
Prerequisites
A GCP project with IAM, Cloud Storage staging, SQL dialect mapping, cost guardrails (partitioning, reservations), and a parallel-run validation plan.