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Hadoop (legacy clusters) 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 Hadoop-era ETL (Hive/Impala/Spark/Oozie) to BigQuery with preserved partition semantics, late-data behavior, and restartability—validated with replayable integrity gates and cutover evidence.
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02
Workload
Performance tuning & optimization
Optimize Hadoop→BigQuery workloads for predictable scan cost and fast SLAs: prune-first rewrites, partitioning/clustering consolidation, typed extraction boundaries, materializations, and regression gates post-cutover.
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03
Workload
SQL / query migration
Convert Hadoop-era SQL (Hive/Impala/Spark SQL) to BigQuery Standard SQL with preserved partition semantics, NULL/type behavior, and time logic—validated with golden-query parity and pruning/cost gates.
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04
Workload
Stored procedure / UDF migration
Migrate Hadoop-era UDFs (Hive/Impala/Spark), script-driven macros, and procedural utilities to BigQuery routines with preserved typing and behavior—validated with replayable harnesses and operational integrity gates.
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05
Workload
Validation & reconciliation
Prove Hadoop→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