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Datafusion-ballista Alternatives
Similar projects and alternatives to datafusion-ballista
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prql
PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
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fluvio
Lean and mean distributed stream processing system written in rust and web assembly. Alternative to Kafka + Flink in one.
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explorer
Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
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blaze
Blazing-fast query execution engine speaks Apache Spark language and has Arrow-DataFusion at its core. (by kwai)
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encoding
Go package containing implementations of efficient encoding, decoding, and validation APIs.
datafusion-ballista discussion
datafusion-ballista reviews and mentions
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DataFusion Comet: Apache Spark Accelerator
But why. Just ditch Spark and use https://github.com/apache/datafusion-ballista directly.
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Polars
Not super on topic because this is all immature and not integrated with one another yet, but there is a scaled-out rust data-frames-on-arrow implementation called ballista that could maybe? form the backend of a polars scale out approach: https://github.com/apache/arrow-ballista
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Rust vs. Go in 2023
> Is Rust's compile-time GC about something other than performance somehow?
AFAIK, memory safety and language features as RAII is also available in C++, for instance. About the reasons for slow compilation, take a look at https://www.reddit.com/r/rust/comments/xna9mb/why_are_rust_p...
Not having a GC is also about not having a runtime as you mention (e.g. nice for creating Python extensions and embedded systems programming) and also more runtime deterministic performance: on that, if I'm not mistaken that was the reason for Discourse switching to Rust and also, e.g.: "the choice of Rust as the main execution language avoids the overhead of GC pauses and results in deterministic processing times" https://github.com/apache/arrow-ballista/blob/main/README.md
- Ballista (Rust) vs Apache Spark. A Tale of Woe.
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Evolution and Trends of Data Engineering 2022/23
Ballista (Arrow-Rust), which is largely inspired by Apache Spark, there are some interesting differences.
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Data Engineering with Rust
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
- Any job processing framework like Spark but in Rust?
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Is Apache Arrow DataFusion and Ballista the future of big data engineering/science?
Source: https://github.com/apache/arrow-ballista
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Pure Python Distributed SQL Engine
Can you explain how this might differ from something like https://github.com/apache/arrow-ballista
I've seen several variants of "next-gen" spark, but nowhere have I really seen the different tradeoffs/advantages/disadvantages between them.
- Scala or Rust? which one will rule in future?
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Stats
apache/datafusion-ballista is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of datafusion-ballista is Rust.
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