Data Warehouse to BigQuery Program Playbook

BigQuery is a serverless, fully managed analytics warehouse that scales compute automatically and bills by data scanned or by reserved slots. Migrating to it removes capacity planning for the warehouse engine but introduces new disciplines around partitioning, clustering, and query cost. This playbook treats the move as a governed program with explicit cost controls, because uncontrolled scanning is the most common way BigQuery migrations go wrong.

Dialect differences also matter. Legacy warehouses use proprietary SQL extensions, and BigQuery's GoogleSQL has its own functions and semantics. Catalog these gaps early so conversion is planned rather than discovered during testing.

Phase-by-Phase

Assessment and Mapping. Map source tables, jobs, and reports to BigQuery equivalents, assess SQL compatibility, and plan waves so high-value workloads move with confidence first.

Platform Foundation. Design dataset and project layout, choose partitioning and clustering keys to bound scan cost, and define an IAM model with least privilege so access is governed from the start.

Migration and Transformation. Load datasets in efficient columnar formats, convert SQL dialects, and rebuild transformation pipelines as version-controlled models with quality checks.

Optimization and Handover. Tune slot usage or on-demand patterns, enforce per-query and per-project cost limits, and hand over to operations with runbooks and dashboards.

Team and Roles

A data architect owns dataset design and partitioning strategy. Data engineers handle loading, conversion, and pipelines. DevOps provisions infrastructure and CI. Security defines the IAM and governance model. A product owner sequences waves and confirms consumer readiness.

Risks and Mitigations

SQL dialect gaps cause subtle result differences, so validate outputs rigorously. Cost overrun from full-table scans is mitigated by partitioning, clustering, and query cost limits. Governance gaps are closed with a least-privilege IAM model and a data catalog.

Success Criteria

Target strong query latency, predictable cost per query, verified data parity, and complete governance coverage across datasets.

Tooling

Provision with Terraform, manage transformations as version-controlled models, and monitor cost and performance through native logging plus Grafana or Datadog dashboards. Run pipeline tests in CI.