So, will Databricks deprioritize Delta Lake in favor or Iceberg, or will they try to derail Iceberg development now they they got the team that originally built it at Netflix?
Edit: from the Tabular CEO announcement
Databricks reached out to me and proposed a collaboration that could bring Iceberg and Delta closer together [...] I’m excited to have the opportunity to work with Databricks and the broader Delta community to build better table formats together.
Doesn't it sound like they'll try and move the two formats closer together so that there isn't such a format war? IDK how it would benefit Databricks to ruin either format if they're now such huge stakeholders in them both.
Either way, I just want to know which format to pick. I've been chief data engineer at my current company for about a year and would like to be able to move off of plain parquet files in my lake but I'm not sure what table format to choose.
Hi, in case you did not find the answer yet. In my hamble opinion:
- choose Iceberg: If you have several computing/query engines other than Spark, like Presto, Flink. Iceberg has a great extraction and design for a engine-independent table format. But its learning cost is relative high
- choose Delta: If you only have Spark and would like to be deeply binded with Databricks
- choose Hudi: If you would like to use data lake out-of-the-box and it is quite easy to use.
- If your data is updated frequently, like streaming, check https://paimon.apache.org/ if you would like to be deeply binded with Flink
Thank you! Sounds like iceberg is the best then. I'm very allergic to lock-in. Currently we're very Spark heavy and our query engine is AWS Redshift Serverless. The recent AWS Glue Catalog support for Iceberg seems to make this promising.
At the Databricks Summit keynote this morning they pitched it as a way of trying to standardize and bridge across the two more easily, with neither going away.
Edit: from the Tabular CEO announcement
So it seems they are going for the latter.