Choosing Between Delta, Iceberg, and Hudi

The Decision Framework: All three formats solve the lakehouse problem, but each optimizes for different workloads. Your choice depends on three factors: your primary use case (batch analytics, streaming, or high frequency updates), your engine ecosystem (are you locked into one vendor or need multi engine support?), and your operational maturity (how much tuning and maintenance can you handle?). Delta Lake: Databricks First, Streaming Native: Delta excels when you are heavily invested in Databricks or Apache Spark. It provides tight integration with Databricks SQL and makes unifying batch and streaming simple through Structured Streaming. You write streaming pipelines that incrementally update tables with exactly once semantics. For companies running primarily on Databricks, this is the path of least resistance. The trade off is engine lock in. While Delta is open source and other engines (Trino, Flink) are adding support, the best experience remains in the Databricks ecosystem. If you anticipate needing strong multi engine support (using Spark for ETL, Trino for ad hoc queries, Flink for real time, and custom services for specific workloads), Delta may force you into suboptimal engine choices or version compatibility issues. Delta's transaction log is simple and performant for moderate scale. At very large scale (millions of commits, complex partition evolution), the log can become unwieldy. Checkpoint files help, but operational complexity increases. For teams with limited data engineering resources, Delta offers a simpler operational model than Iceberg or Hudi. Iceberg: Multi Engine, Enterprise Scale: Iceberg is preferred when you need strong multi engine compatibility and plan to operate at very large scale. Companies like Netflix, Apple, and Adobe use Iceberg because they run heterogeneous engine environments: Spark for batch ETL, Flink for streaming, Trino and Presto for interactive queries, and custom query services for specific products. Iceberg's metadata tree architecture scales better than Delta's log for complex scenarios. With millions of data files, Iceberg's manifest structure keeps query planning fast through aggressive metadata caching and pruning. Partition evolution (changing partition schemes without data rewrites) and hidden partitioning (partition transforms in metadata, not file paths) are first class features, making schema evolution and query optimization easier over time. The trade off is operational complexity. Iceberg requires a robust catalog implementation (Hive Metastore, AWS Glue, Nessie, or custom). Catalog availability becomes a single point of failure: if the catalog is down, your lakehouse is unavailable even though data files are intact. You also need to carefully manage snapshot expiration and metadata cleanup to prevent metadata bloat. At Netflix scale, this means investing in catalog infrastructure and monitoring. Hudi: Upsert Heavy, CDC Focused: Hudi is the best choice for workloads dominated by high frequency upserts and change data capture. If you are ingesting 100k+ events per second from transactional databases via Debezium or similar tools, Hudi's indexing and Merge On Read tables minimize write amplification. Hudi also excels at incremental query semantics: downstream jobs can read only new commits since a checkpoint, enabling efficient incremental processing. Hudi offers two table types: Copy On Write rewrites full data files on update (simpler reads, higher write cost), while Merge On Read appends updates to log files (faster writes, more complex reads requiring log merge at query time). This flexibility lets you tune for your read/write ratio. For a table with 90% writes and 10% reads (like event logs), MOR is ideal. For 90% reads (like dimension tables), COW is simpler. The trade off is a steeper learning curve. Hudi has more knobs to tune: index types (bloom filter, HBase, simple), compaction strategies, clustering, and cleaning policies. Engine support is also less mature than Iceberg. Spark and Flink have good support, but other engines may lag. For teams without deep Hudi expertise, the operational burden can be significant.