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A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
In Perspective (https://github.com/finos/perspective), we use Apache Arrow as a fast, cross-language/cross-network data encoding that is extremely useful for in-browser data visualization and analytics.
- super fast read/write compared to CSV & JSON (Perspective and Arrow share an extremely similar column encoding scheme, so we can memcpy Arrow columns into Perspective wholesale instead of reading a dataset iteratively).
- the ability to send Arrow binaries as an ArrayBuffer between a Python server and a WASM client, which guarantees compatibility and removes the overhead of JSON serialization/deserialization.
- because Arrow columns are strictly typed, there's no need to infer data types - this helps with speed and correctness.
- Compared to JSON/CSV, Arrow binaries have a super compact encoding that reduces network transport time.
For us, building on top of Apache Arrow (and using it wherever we can) reduces the friction of passing around data between clients, servers, and runtimes in different languages, and allows larger datasets to be efficiently visualized and analyzed in the browser context.
Query processing and transformation of array-backed data tables.
Take a look at the arquero library from a research group at University of Washington (the same group that D3 came out of). https://github.com/uwdata/arquero
Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
Official Julia implementation of Apache Arrow
Excited to see this release's official inclusion of the pure Julia Arrow implementation !
It's so cool to be able mmap Arrow memory and natively manipulate it from within Julia with virtually no performance overhead. Since the Julia compiler can specialize on the layout of Arrow-backed types at runtime (just as it can with any other type), the notion of needing to build/work with a separate "compiler for fast UDFs" is rendered obsolete.
It feels pretty magical when two tools like this compose so well without either being designed with the other in mind - a testament to the thoughtful design of both :) mad props to Jacob Quinn for spearheading the effort to revive/restart Arrow.jl and get the package into this release.
Bridge between Go and Python to facilitate zero-copy using Apache Arrow
Not only in between processes, but also in between languages in a single process. In this POC I spun up a Python interpreter in Go and can pass the Arrow data buffer between processes in constant time. https://github.com/nickpoorman/go-py-arrow-bridge
Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Data loader for the Apache Arrow format.
Not sure I follow, that page indicates that JS support is pretty good for all but the more obscure features (e.g. decimals) and doesn't mention data visualization at all? Anyhow, I've successfully used https://github.com/vega/vega-loader-arrow for in-browser plots before, and Observable has a fair few notebooks showing how to use the JS API (e.g. https://observablehq.com/@theneuralbit/introduction-to-apach...)
Cylon is a fast, scalable, distributed memory, parallel runtime with a Pandas like DataFrame. (by cylondata)
Cudf and Cylon are two execution engines natively supporting Arrow format https://github.com/rapidsai/cudf https://github.com/cylondata/cylon
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ClickHouse® is a free analytics DBMS for big data
On a side note, Clickhouse had some Arrow support
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