Our team is hitting performance bottlenecks with standard Parquet files on Azure Data Lake Storage. We need ACID transactions and the ability to do "Time Travel" for auditing our machine learning models. Between Delta Lake and Apache Iceberg, which one provides better open-source support and performance for large-scale updates and deletes?
3 answers
Choosing between Delta and Iceberg often depends on your compute engine. If you are heavily invested in Databricks, Delta Lake is the native choice and offers incredible performance through features like Z-Ordering. However, if you use a mix of Snowflake, Trino, and Spark, Apache Iceberg’s "hidden partitioning" is a lifesaver. It allows you to change partition logic without rewriting the entire table. For ML auditing, both offer great time-traveling capabilities, but Iceberg’s snapshot management felt slightly more robust when we were handling multi-petabyte datasets last year.
Does your organization have a preference for vendor-neutral formats, or are you okay with a format that is more tightly integrated with a specific platform's optimization engine?
Delta Lake 3.0 now has Universal Format (UniForm) which allows you to read Delta tables as if they were Iceberg. It might be the "best of both worlds" solution for your team.
I agree with Karen; UniForm is a game changer. It effectively eliminates the format war by allowing interoperability across different query engines.
Gregory, we definitely prefer staying vendor-neutral. We don't want to get locked into one ecosystem if we decide to move parts of our stack to AWS or GCP later. That's why Iceberg is currently leading the race for us, even if it requires a bit more manual tuning compared to Delta's "auto-optimize" features. We value the flexibility of the open-source community support.