MessagePack VS Apache Orc

so appreciate such a detailed reply, thanks. btw, why did you choose tinylib/msgp from 4 available go-impls?

If you find you're running the serial connection at maximum speed and it's still not fast enough, try switching to a more compact binary encoding that has both Serde and Arduino implementations, like MsgPack... though I don't remember enough about its format off the top of my head to tell you the easiest way to put an unambiguous header on each packet/message to make the protocol self-synchronizing.

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.

MessagePack Similar to JSONs, just more compact, although not as much as the ones above. Still, it's usefull to retain some readability in your messages.

I'll try that msgpack.org website.

First of all, you're comparing this to GSON and Kryo, how does it compare to Msgpack, fast-serialization, but also Elsa and I'm sure, many others? Are there any limitations and/or trade-offs?

Upvote for msgpack, one of the great undervalued message protocols available.

I have two suggestions Capnproto, MessagePack (those are only the two examples that came to mind first, i bet there are even one or two especially developed for rust). Both of these are better than json in nearly every way.

Encode messages as JSON or MessagePack.

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.