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Hive to BigQuery
Book Assessment
At a glance
Scope
Query and schema conversion
Semantic and type alignment
Validation and cutover readiness
Risk areas
Deliverables
Prioritized execution plan
Parity evidence and variance log
Rollback-ready cutover criteria
Next reads
Related links
01
Workload
ETL / pipeline migration
Migrate Hive-era ETL (partition overwrite, staging zones, Oozie/Airflow chains) to BigQuery with preserved window semantics, late-arrival corrections, and restartability—validated with integrity and pruning/cost gates.
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02
Workload
Performance tuning & optimization
Optimize Hive→BigQuery workloads for predictable scan cost and fast SLAs: prune-first rewrites, partitioning/clustering consolidation, typed extraction boundaries, and regression gates for post-cutover stability.
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03
Workload
SQL / query migration
Convert HiveQL to BigQuery Standard SQL with preserved semantics for partition filters, window logic, NULL/type coercion, and time handling—validated with golden-query parity and pruning/cost gates.
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04
Workload
Stored procedure / UDF migration
Migrate Hive UDFs, SerDe-era parsing helpers, and macro-style ETL utilities to BigQuery UDFs and stored procedures with preserved typing and behavior—validated with replayable harnesses and integrity gates.
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05
Workload
Validation & reconciliation
Prove Hive→BigQuery parity with repeatable gates: golden queries, KPI diffs, checksum aggregates, pruning/cost baselines, rerun/backfill simulations, and rollback-ready cutover criteria.
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Book Assessment