arrow-datafusion VS Apache Spark

Python's Substrait seems like the biggest/most-used competitor-ish out there. I'd love some compare & contrast; my sense is that Substrait has a smaller ambition, and more wants to be a language for talking about execution rather than a full on execution engine. https://github.com/substrait-io/substrait

We can also see from the DataFusion discussion that they too see themselves as a bit of a Velox competitor. https://github.com/apache/arrow-datafusion/discussions/6441

Agree, substrait is a really cool project! Related: if you like substrait you might want to check out datafusion too. The project is a query execution engine built on top of Apache Arrow (with SQL parser, query planner & optimizer, execution engine, extensible user defined functions, among others) and it implements a substrait provider and consumer: https://github.com/apache/arrow-datafusion/tree/main/datafus...

The draft contains some preliminary benchmark results, comparing it to DuckDB.

https://github.com/apache/arrow-datafusion/issues/6782

Apache Arrow is a pretty common memory structure these days. Datafusion is an open query engine built in Rust started by Andy Grove.

DuckDB is a great piece of software if you are

If you are looking for a query engine implemented in a safe language (Rust) I definitely suggest checking out DataFusion. It is comparable to DuckDB in performance, has all the standard built in SQL functionality, and is extensible in pretty much all areas (query language, data formats, catalogs, user defined functions, etc)

https://arrow.apache.org/datafusion/

Disclaimer I am a maintainer of DataFusion

https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb

Problem comes when you want to do this inside an async context since we couldn't block an async task. https://users.rust-lang.org/t/sync-function-invoking-async/43364/6 You might need to do it in another runtime/thread. It is not recommended to do this, but sometimes it is unavoidable while implementing a third-party trait. https://github.com/apache/arrow-datafusion/issues/3777 However, I believe this isn't a problem particular to tokio, or any specific runtime.

Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.

Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system

also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.

Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.

Apache Spark - https://spark.apache.org/

A JVM based framework named "Spark", when https://spark.apache.org exists?

I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.