Migrate Spark SQL-driven ETL pipelines to BigQuery with preserved incremental semantics, deterministic dedupe, and late-arrival corrections—validated with idempotency simulations and proof-backed cutover gates.

Optimize Spark SQL→BigQuery workloads for predictable scan cost and fast SLAs: prune-first rewrites, partitioning/clustering, materializations, semi-structured typing strategy, and regression gates.

Convert Spark SQL to BigQuery Standard SQL with preserved semantics for window logic, NULL/type coercion, arrays/structs, and time handling—validated with golden-query parity and pruning/cost gates.

Migrate Spark SQL UDFs, notebook macro utilities, and script-driven procedural logic to BigQuery UDFs and stored procedures with preserved typing and behavior—validated with replayable harnesses and integrity gates.

Prove Spark SQL→BigQuery parity with repeatable gates: golden queries, KPI diffs, checksum aggregates, pruning/cost baselines, rerun/backfill simulations, and rollback-ready cutover criteria.