When to Use Hudi vs Alternatives

The Decision Framework: Choosing Apache Hudi over alternatives like plain Parquet, Delta Lake, Iceberg, or a cloud data warehouse depends on specific trade offs around scale, cost, control, and operational complexity. Hudi vs Plain Parquet on S3: If your datasets are under a few hundred gigabytes and you can tolerate daily batch windows of 12 to 24 hours, plain Parquet with full table scans is simpler. No indexes to maintain, no compaction to tune, no timeline metadata to manage. Hudi becomes compelling when data volumes reach tens of terabytes and only a small fraction changes daily. The math is clear: processing 5 percent of 10 TB (500 GB) with Hudi is 20x less data than rescanning all 10 TB. At 2 GB per second scan rate, that's 4 minutes versus 83 minutes, and compute cost drops proportionally. Uber Eats provides the concrete example: moving from full scans to Hudi incremental processing cut pipeline time from 12+ hours to under 4 hours while reducing compute cost by 50 percent. Hudi vs Cloud Data Warehouse: Systems like BigQuery, Snowflake, or Redshift natively support updates, deletes, and change tracking with simpler operational models. You pay for convenience with higher storage costs (often 3 to 5x more than S3) and less control over file layout and query engines. Hudi offers lower storage cost and flexibility to use any compute engine (Spark, Presto, Trino, Flink). But you own the operational complexity: you must tune compaction, manage clustering, and operate table services. For teams that already run Spark or Flink at scale, this trade off can be acceptable for the cost savings and control. The decision criteria: if your organization already has mature data lake infrastructure and Spark expertise, Hudi can save 40 to 60 percent on storage plus compute compared to a warehouse. If you're starting fresh or lack those capabilities, a managed warehouse may be simpler despite higher cost. Hudi vs Delta Lake vs Iceberg: These three systems solve similar problems with different philosophies. Delta Lake has strong integration with a specific commercial platform and focuses on simplicity. Iceberg prioritizes multi engine compatibility and hidden partitioning. Hudi emphasizes incremental pull based processing and streaming first design. Choose Hudi when incremental queries are a first class requirement, when you need fine grained control over indexing strategies, or when your workload is write heavy streaming ingestion at tens of thousands of records per second. Hudi's Merge on Read mode and built in streaming patterns excel here. Choose Iceberg if you need strong multi engine guarantees and hidden partitioning to avoid user errors. Choose Delta if you're heavily invested in a particular ecosystem and want tighter integration. Failure Scenario: A team adopts Hudi for a 200 GB dataset with 50 percent daily changes. The overhead of managing indexes, compaction, and timeline metadata outweighs the benefit. Full Parquet scans would be simpler and faster. Hudi shines at large scale with low change rates, not small datasets with high churn.