Persistent Collection VS Apache Orc

So does Java! Also, kotlinx.collections is still not stable and I don't think they are intending to make it so any time soon.

> If you are using containers, always, always, always use immutable containers from Google Guava unless you have an exceptionally good reason.

I actually prefer pcollections: https://github.com/hrldcpr/pcollections

AtomicReference + immutable data types is a really nice way to program in Java, and is basically the way most Clojure programs are written.

You might consider persistent collections instead of immutable collections, I believe it is more optimized https://github.com/hrldcpr/pcollections

The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto.

Data formatting is another place to make gains. When dealing with huge amounts of data, finding the data you need can take up a significant amount of your compute time. Apache Parquet and Apache ORC are columnar data formats optimized for analytics that pre-aggregate metadata about columns. If your EMR queries column intensive data like sum, max, or count, you can see significant speed improvements by reformatting data like CSVs into one of these columnar formats.

The following stack captures layers of software components that make up Hudi, with each layer depending on and drawing strength from the layer below. Typically, data lake users write data out once using an open file format like Apache Parquet/ORC stored on top of extremely scalable cloud storage or distributed file systems. Hudi provides a self-managing data plane to ingest, transform and manage this data, in a way that unlocks incremental data processing on them.