Which DataFrame?

---

Avro

Row-oriented, schema-based serialization optimized for streaming, RPC, and data exchange. Its compact binary format and schema embedding make it highly suited for ingestion pipelines and streaming use cases.

Parquet

A columnar storage format designed for analytical workloads, offering exceptional compression, encoding (e.g., dictionary, RLE, bit-packing), and efficient scans through predicate pushdown and column pruning. Avro excels in row-level mutability and schema evolution during ingestion.

Iceberg

A table abstraction that supports ACID transactions, snapshot-based versioning, and efficient metadata via manifest files and metadata hierarchies. Supports multiple engines (Spark, Trino, Flink) and robust schema/partition evolution.

Why Iceberg

Delta Lake

Built by Databricks, providing ACID compliance, unified batch/stream support, time travel, and optimized reads/writes via a transaction log (Delta Log), auto-compaction, and indexing.

• File format compatibility: Iceberg supports multiple file types; Delta Lake relies primarily on Parquet
• Schema handling: Iceberg enables full evolution; Delta Lake mandates schema compliance

Delta Lake often leads in table loading and query speed through its optimized engine and compaction strategies. Iceberg shines in scalability, multi-engine access, and flexible partitioning. Both deliver ACID transactions, time travel, and open-source flexibility.

Why Delta Lake

XTable

Often, enterprises use differing table formats across domains (e.g., Iceberg for analytics, Delta for streaming). Apache XTable (incubating under ASF) solves this by enabling metadata-level, lossless translation between formats, without rewriting data so systems can interoperate seamlessly.

It maps metadata between Hudi, Iceberg, and Delta (and potentially others), facilitating cross-platform access (e.g., make a Hudi dataset queryable as Iceberg on Snowflake or as Delta on Databricks).

Adoption includes major players like Microsoft, Google, Databricks, Snowflake, Adobe.

Supports both incremental and full metadata sync, enabling live interoperability without data duplication.

Conclusion

Use Avro for ingestion, persist to Parquet for analytics, and layer Iceberg or Delta for mutability and governance. Platform alignment matters; choose Iceberg if you operate across engines; Delta if your stack centers on Spark/Databricks and real-time workloads. Avoid format lock-in: Implement XTable to future-proof your architecture, enabling multi-engine queries and smoother migrations.

There is not one perfect solution to use across all solution spaces, as is the case with everything. But understanding the pros and cons of each approach will help guide you to make the right decision.