AWS Data Engineering Optimization Playbook: Athena, Glue & S3 Compaction

[!NOTE] Querying unoptimized S3 buckets wastes thousands of dollars in scanned byte fees and cloud compute. This playbook provides concrete optimization rules across Athena, Glue, and S3.

1. Athena Cost & Speed Optimization Matrix

1. Convert to Columnar Format (Parquet/ORC): Cuts scanned bytes by 80-90% because queries read only referenced columns.
2. Apply Snappy or ZSTD Compression: Further shrinks storage and network transfer.
3. Partition Projection: Completely eliminates MSCK REPAIR TABLE scans.
4. Bucket Pruning: Combine date partitioning with hash bucketing on high-cardinality keys.

Staff Architecture: End-to-End AWS FinOps — Graviton, Spot Fleets & Small-File Compaction

• Compute Savings (20–40% Immediate Win): Migrate Glue workers (Glue 4.0/5.0) and EMR clusters to AWS Graviton (m7g / r7g / c7g) instances—JVM/Spark workloads routinely deliver 15–25% better price-performance with zero PySpark code changes (verify native wheel compatibility for any C-extension UDFs).
• Spot Allocation + Decommission Gracefully: Enable Spark Dynamic Allocation combined with spark.decommission.enabled=true and spark.storage.decommission.shuffleBlocks.enabled=true on EMR Spot task nodes so EC2 2-minute Spot interruption notices migrate shuffle blocks before termination instead of triggering catastrophic FetchFailedException stage retries.
• S3 Compaction & Zero-Waste Partitioning: Schedule weekly Iceberg rewrite_data_files / Delta OPTIMIZE + VACUUM jobs to merge sub-10MB streaming files into 128–256 MB Parquet files, slashing S3 LIST/GET request bills and speeding up Athena/EMR scans by 5x–10x.