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Posted to commits@datafusion.apache.org by gi...@apache.org on 2025/04/30 14:43:43 UTC
(datafusion) branch asf-site updated: Publish built docs triggered by 2f454aefd5e216b0667ae8aecf357dcf18f2ea4f
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github-bot pushed a commit to branch asf-site
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The following commit(s) were added to refs/heads/asf-site by this push:
new 165d9a64fd Publish built docs triggered by 2f454aefd5e216b0667ae8aecf357dcf18f2ea4f
165d9a64fd is described below
commit 165d9a64fd7480bed0ae5633007860379b466b8b
Author: github-actions[bot] <gi...@users.noreply.github.com>
AuthorDate: Wed Apr 30 14:43:38 2025 +0000
Publish built docs triggered by 2f454aefd5e216b0667ae8aecf357dcf18f2ea4f
---
_sources/user-guide/introduction.md.txt | 16 ++++++++--------
searchindex.js | 2 +-
user-guide/introduction.html | 16 ++++++++--------
3 files changed, 17 insertions(+), 17 deletions(-)
diff --git a/_sources/user-guide/introduction.md.txt b/_sources/user-guide/introduction.md.txt
index 1879221aa4..57bf9c2fa4 100644
--- a/_sources/user-guide/introduction.md.txt
+++ b/_sources/user-guide/introduction.md.txt
@@ -40,9 +40,9 @@ Arrow](https://arrow.apache.org/).
## Features
- Feature-rich [SQL support](https://datafusion.apache.org/user-guide/sql/index.html) and [DataFrame API](https://datafusion.apache.org/user-guide/dataframe.html)
-- Blazingly fast, vectorized, multi-threaded, streaming execution engine.
+- Blazingly fast, vectorized, multithreaded, streaming execution engine.
- Native support for Parquet, CSV, JSON, and Avro file formats. Support
- for custom file formats and non file datasources via the `TableProvider` trait.
+ for custom file formats and non-file datasources via the `TableProvider` trait.
- Many extension points: user defined scalar/aggregate/window functions, DataSources, SQL,
other query languages, custom plan and execution nodes, optimizer passes, and more.
- Streaming, asynchronous IO directly from popular object stores, including AWS S3,
@@ -68,7 +68,7 @@ DataFusion can be used without modification as an embedded SQL
engine or can be customized and used as a foundation for
building new systems.
-While most current usecases are "analytic" or (throughput) some
+While most current use cases are "analytic" or (throughput) some
components of DataFusion such as the plan representations, are
suitable for "streaming" and "transaction" style systems (low
latency).
@@ -100,7 +100,7 @@ Here are some active projects using DataFusion:
- [Blaze](https://github.com/kwai/blaze) The Blaze accelerator for Apache Spark leverages native vectorized execution to accelerate query processing
- [CnosDB](https://github.com/cnosdb/cnosdb) Open Source Distributed Time Series Database
- [Comet](https://github.com/apache/datafusion-comet) Apache Spark native query execution plugin
-- [Cube Store](https://github.com/cube-js/cube.js/tree/master/rust)
+- [Cube Store](https://github.com/cube-js/cube.js/tree/master/rust) Cube’s universal semantic layer platform is the next evolution of OLAP technology for AI, BI, spreadsheets, and embedded analytics
- [Dask SQL](https://github.com/dask-contrib/dask-sql) Distributed SQL query engine in Python
- [datafusion-dft](https://github.com/datafusion-contrib/datafusion-dft) Batteries included CLI, TUI, and server implementations for DataFusion.
- [delta-rs](https://github.com/delta-io/delta-rs) Native Rust implementation of Delta Lake
@@ -120,11 +120,11 @@ Here are some active projects using DataFusion:
- [Polygon.io](https://polygon.io/) Stock Market API
- [qv](https://github.com/timvw/qv) Quickly view your data
- [Restate](https://github.com/restatedev) Easily build resilient applications using distributed durable async/await
-- [ROAPI](https://github.com/roapi/roapi)
-- [Sail](https://github.com/lakehq/sail) Unifying stream, batch, and AI workloads with Apache Spark compatibility
+- [ROAPI](https://github.com/roapi/roapi) Create full-fledged APIs for slowly moving datasets without writing a single line of code
+- [Sail](https://github.com/lakehq/sail) Unifying stream, batch and AI workloads with Apache Spark compatibility
- [Seafowl](https://github.com/splitgraph/seafowl) CDN-friendly analytical database
- [Sleeper](https://github.com/gchq/sleeper) Serverless, cloud-native, log-structured merge tree based, scalable key-value store
-- [Spice.ai](https://github.com/spiceai/spiceai) Unified SQL query interface & materialization engine
+- [Spice.ai](https://github.com/spiceai/spiceai) Building blocks for data-driven AI applications
- [Synnada](https://synnada.ai/) Streaming-first framework for data products
- [VegaFusion](https://vegafusion.io/) Server-side acceleration for the [Vega](https://vega.github.io/) visualization grammar
- [Telemetry](https://telemetry.sh/) Structured logging made easy
@@ -181,6 +181,6 @@ provide integrations with other systems, some of which are described below:
## Why DataFusion?
- _High Performance_: Leveraging Rust and Arrow's memory model, DataFusion is very fast.
-- _Easy to Connect_: Being part of the Apache Arrow ecosystem (Arrow, Parquet and Flight), DataFusion works well with the rest of the big data ecosystem
+- _Easy to Connect_: Being part of the Apache Arrow ecosystem (Arrow, Parquet, and Flight), DataFusion works well with the rest of the big data ecosystem
- _Easy to Embed_: Allowing extension at almost any point in its design, and published regularly as a crate on [crates.io](http://crates.io), DataFusion can be integrated and tailored for your specific usecase.
- _High Quality_: Extensively tested, both by itself and with the rest of the Arrow ecosystem, DataFusion can and is used as the foundation for production systems.
diff --git a/searchindex.js b/searchindex.js
index 39fd58b3af..f3c26093ea 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles":{"!=":[[54,"op-neq"]],"!~":[[54,"op-re-not-match"]],"!~*":[[54,"op-re-not-match-i"]],"!~~":[[54,"id19"]],"!~~*":[[54,"id20"]],"#":[[54,"op-bit-xor"]],"%":[[54,"op-modulo"]],"&":[[54,"op-bit-and"]],"(relation, name) tuples in logical fields and logical columns are unique":[[12,"relation-name-tuples-in-logical-fields-and-logical-columns-are-unique"]],"*":[[54,"op-multiply"]],"+":[[54,"op-plus"]],"-":[[54,"op-minus"]],"/":[[54,"op-divide"]],"2022 Q2":[[10,"q2"]] [...]
\ No newline at end of file
+Search.setIndex({"alltitles":{"!=":[[54,"op-neq"]],"!~":[[54,"op-re-not-match"]],"!~*":[[54,"op-re-not-match-i"]],"!~~":[[54,"id19"]],"!~~*":[[54,"id20"]],"#":[[54,"op-bit-xor"]],"%":[[54,"op-modulo"]],"&":[[54,"op-bit-and"]],"(relation, name) tuples in logical fields and logical columns are unique":[[12,"relation-name-tuples-in-logical-fields-and-logical-columns-are-unique"]],"*":[[54,"op-multiply"]],"+":[[54,"op-plus"]],"-":[[54,"op-minus"]],"/":[[54,"op-divide"]],"2022 Q2":[[10,"q2"]] [...]
\ No newline at end of file
diff --git a/user-guide/introduction.html b/user-guide/introduction.html
index 15efe69652..4e33f5ca73 100644
--- a/user-guide/introduction.html
+++ b/user-guide/introduction.html
@@ -635,9 +635,9 @@ Arrow</a>.</p>
<h2>Features<a class="headerlink" href="#features" title="Link to this heading">¶</a></h2>
<ul class="simple">
<li><p>Feature-rich <a class="reference external" href="https://datafusion.apache.org/user-guide/sql/index.html">SQL support</a> and <a class="reference external" href="https://datafusion.apache.org/user-guide/dataframe.html">DataFrame API</a></p></li>
-<li><p>Blazingly fast, vectorized, multi-threaded, streaming execution engine.</p></li>
+<li><p>Blazingly fast, vectorized, multithreaded, streaming execution engine.</p></li>
<li><p>Native support for Parquet, CSV, JSON, and Avro file formats. Support
-for custom file formats and non file datasources via the <code class="docutils literal notranslate"><span class="pre">TableProvider</span></code> trait.</p></li>
+for custom file formats and non-file datasources via the <code class="docutils literal notranslate"><span class="pre">TableProvider</span></code> trait.</p></li>
<li><p>Many extension points: user defined scalar/aggregate/window functions, DataSources, SQL,
other query languages, custom plan and execution nodes, optimizer passes, and more.</p></li>
<li><p>Streaming, asynchronous IO directly from popular object stores, including AWS S3,
@@ -663,7 +663,7 @@ easily pass plans across language and system boundaries.</p></li>
<p>DataFusion can be used without modification as an embedded SQL
engine or can be customized and used as a foundation for
building new systems.</p>
-<p>While most current usecases are “analytic” or (throughput) some
+<p>While most current use cases are “analytic” or (throughput) some
components of DataFusion such as the plan representations, are
suitable for “streaming” and “transaction” style systems (low
latency).</p>
@@ -693,7 +693,7 @@ parellelized streaming execution plans, file format support, etc.</p>
<li><p><a class="reference external" href="https://github.com/kwai/blaze">Blaze</a> The Blaze accelerator for Apache Spark leverages native vectorized execution to accelerate query processing</p></li>
<li><p><a class="reference external" href="https://github.com/cnosdb/cnosdb">CnosDB</a> Open Source Distributed Time Series Database</p></li>
<li><p><a class="reference external" href="https://github.com/apache/datafusion-comet">Comet</a> Apache Spark native query execution plugin</p></li>
-<li><p><a class="reference external" href="https://github.com/cube-js/cube.js/tree/master/rust">Cube Store</a></p></li>
+<li><p><a class="reference external" href="https://github.com/cube-js/cube.js/tree/master/rust">Cube Store</a> Cube’s universal semantic layer platform is the next evolution of OLAP technology for AI, BI, spreadsheets, and embedded analytics</p></li>
<li><p><a class="reference external" href="https://github.com/dask-contrib/dask-sql">Dask SQL</a> Distributed SQL query engine in Python</p></li>
<li><p><a class="reference external" href="https://github.com/datafusion-contrib/datafusion-dft">datafusion-dft</a> Batteries included CLI, TUI, and server implementations for DataFusion.</p></li>
<li><p><a class="reference external" href="https://github.com/delta-io/delta-rs">delta-rs</a> Native Rust implementation of Delta Lake</p></li>
@@ -713,11 +713,11 @@ parellelized streaming execution plans, file format support, etc.</p>
<li><p><a class="reference external" href="https://polygon.io/">Polygon.io</a> Stock Market API</p></li>
<li><p><a class="reference external" href="https://github.com/timvw/qv">qv</a> Quickly view your data</p></li>
<li><p><a class="reference external" href="https://github.com/restatedev">Restate</a> Easily build resilient applications using distributed durable async/await</p></li>
-<li><p><a class="reference external" href="https://github.com/roapi/roapi">ROAPI</a></p></li>
-<li><p><a class="reference external" href="https://github.com/lakehq/sail">Sail</a> Unifying stream, batch, and AI workloads with Apache Spark compatibility</p></li>
+<li><p><a class="reference external" href="https://github.com/roapi/roapi">ROAPI</a> Create full-fledged APIs for slowly moving datasets without writing a single line of code</p></li>
+<li><p><a class="reference external" href="https://github.com/lakehq/sail">Sail</a> Unifying stream, batch and AI workloads with Apache Spark compatibility</p></li>
<li><p><a class="reference external" href="https://github.com/splitgraph/seafowl">Seafowl</a> CDN-friendly analytical database</p></li>
<li><p><a class="reference external" href="https://github.com/gchq/sleeper">Sleeper</a> Serverless, cloud-native, log-structured merge tree based, scalable key-value store</p></li>
-<li><p><a class="reference external" href="https://github.com/spiceai/spiceai">Spice.ai</a> Unified SQL query interface & materialization engine</p></li>
+<li><p><a class="reference external" href="https://github.com/spiceai/spiceai">Spice.ai</a> Building blocks for data-driven AI applications</p></li>
<li><p><a class="reference external" href="https://synnada.ai/">Synnada</a> Streaming-first framework for data products</p></li>
<li><p><a class="reference external" href="https://vegafusion.io/">VegaFusion</a> Server-side acceleration for the <a class="reference external" href="https://vega.github.io/">Vega</a> visualization grammar</p></li>
<li><p><a class="reference external" href="https://telemetry.sh/">Telemetry</a> Structured logging made easy</p></li>
@@ -756,7 +756,7 @@ provide integrations with other systems, some of which are described below:</p>
<h2>Why DataFusion?<a class="headerlink" href="#why-datafusion" title="Link to this heading">¶</a></h2>
<ul class="simple">
<li><p><em>High Performance</em>: Leveraging Rust and Arrow’s memory model, DataFusion is very fast.</p></li>
-<li><p><em>Easy to Connect</em>: Being part of the Apache Arrow ecosystem (Arrow, Parquet and Flight), DataFusion works well with the rest of the big data ecosystem</p></li>
+<li><p><em>Easy to Connect</em>: Being part of the Apache Arrow ecosystem (Arrow, Parquet, and Flight), DataFusion works well with the rest of the big data ecosystem</p></li>
<li><p><em>Easy to Embed</em>: Allowing extension at almost any point in its design, and published regularly as a crate on <a class="reference external" href="http://crates.io">crates.io</a>, DataFusion can be integrated and tailored for your specific usecase.</p></li>
<li><p><em>High Quality</em>: Extensively tested, both by itself and with the rest of the Arrow ecosystem, DataFusion can and is used as the foundation for production systems.</p></li>
</ul>
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