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new a3b7608e9f Publish built docs triggered by
74204cd49b04fd039771a8b5363b7623fdb35fa3
a3b7608e9f is described below
commit a3b7608e9f0420cce71b69fb5d243af1ce6cb19f
Author: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
AuthorDate: Fri Sep 12 11:26:13 2025 +0000
Publish built docs triggered by 74204cd49b04fd039771a8b5363b7623fdb35fa3
---
_sources/user-guide/introduction.md.txt | 6 +++---
searchindex.js | 2 +-
user-guide/introduction.html | 4 ++--
3 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/_sources/user-guide/introduction.md.txt
b/_sources/user-guide/introduction.md.txt
index 040405f8f6..9bb98a19ee 100644
--- a/_sources/user-guide/introduction.md.txt
+++ b/_sources/user-guide/introduction.md.txt
@@ -81,7 +81,7 @@ Here are some example systems built using DataFusion:
- SQL support to another library, such as [dask sql]
- Streaming data platforms such as [Synnada]
- Tools for reading / sorting / transcoding Parquet, CSV, AVRO, and JSON files
such as [qv]
-- Native Spark runtime replacement such as [Blaze]
+- Native Spark runtime replacement such as [Auron]
By using DataFusion, projects are freed to focus on their specific
features, and avoid reimplementing general (but still necessary)
@@ -96,8 +96,8 @@ Here are some active projects using DataFusion:
- [Arroyo](https://github.com/ArroyoSystems/arroyo) Distributed stream
processing engine in Rust
- [ArkFlow](https://github.com/arkflow-rs/arkflow) High-performance Rust
stream processing engine
+- [Auron](https://github.com/apache/auron) The Auron accelerator for big data
engine (e.g., Spark, Flink) leverages native vectorized execution to accelerate
query processing
- [Ballista](https://github.com/apache/datafusion-ballista) Distributed SQL
Query Engine
-- [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’s
universal semantic layer platform is the next evolution of OLAP technology for
AI, BI, spreadsheets, and embedded analytics
@@ -138,7 +138,7 @@ Here are some less active projects that used DataFusion:
- [Tensorbase](https://github.com/tensorbase/tensorbase)
[ballista]: https://github.com/apache/datafusion-ballista
-[blaze]: https://github.com/blaze-init/blaze
+[auron]: https://github.com/apache/auron
[cloudfuse buzz]: https://github.com/cloudfuse-io/buzz-rust
[cnosdb]: https://github.com/cnosdb/cnosdb
[cube store]: https://github.com/cube-js/cube.js/tree/master/rust
diff --git a/searchindex.js b/searchindex.js
index e0ac9f0117..cb0d2e1671 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles":{"!=":[[57,"op-neq"]],"!~":[[57,"op-re-not-match"]],"!~*":[[57,"op-re-not-match-i"]],"!~~":[[57,"id19"]],"!~~*":[[57,"id20"]],"#":[[57,"op-bit-xor"]],"%":[[57,"op-modulo"]],"&":[[57,"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"]],"*":[[57,"op-multiply"]],"+":[[57,"op-plus"]],"-":[[57,"op-minus"]],"/":[[57,"op-divide"]],"<":[[57,"op-lt"]],"<
[...]
\ No newline at end of file
+Search.setIndex({"alltitles":{"!=":[[57,"op-neq"]],"!~":[[57,"op-re-not-match"]],"!~*":[[57,"op-re-not-match-i"]],"!~~":[[57,"id19"]],"!~~*":[[57,"id20"]],"#":[[57,"op-bit-xor"]],"%":[[57,"op-modulo"]],"&":[[57,"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"]],"*":[[57,"op-multiply"]],"+":[[57,"op-plus"]],"-":[[57,"op-minus"]],"/":[[57,"op-divide"]],"<":[[57,"op-lt"]],"<
[...]
\ No newline at end of file
diff --git a/user-guide/introduction.html b/user-guide/introduction.html
index 554819d22e..93fab34a70 100644
--- a/user-guide/introduction.html
+++ b/user-guide/introduction.html
@@ -702,7 +702,7 @@ latency).</p>
<li><p>SQL support to another library, such as <a class="reference external"
href="https://github.com/dask-contrib/dask-sql";>dask sql</a></p></li>
<li><p>Streaming data platforms such as <a class="reference external"
href="https://synnada.ai/";>Synnada</a></p></li>
<li><p>Tools for reading / sorting / transcoding Parquet, CSV, AVRO, and JSON
files such as <a class="reference external"
href="https://github.com/timvw/qv";>qv</a></p></li>
-<li><p>Native Spark runtime replacement such as <a class="reference external"
href="https://github.com/blaze-init/blaze";>Blaze</a></p></li>
+<li><p>Native Spark runtime replacement such as <a class="reference external"
href="https://github.com/apache/auron";>Auron</a></p></li>
</ul>
<p>By using DataFusion, projects are freed to focus on their specific
features, and avoid reimplementing general (but still necessary)
@@ -716,8 +716,8 @@ parellelized streaming execution plans, file format
support, etc.</p>
<ul class="simple">
<li><p><a class="reference external"
href="https://github.com/ArroyoSystems/arroyo";>Arroyo</a> Distributed stream
processing engine in Rust</p></li>
<li><p><a class="reference external"
href="https://github.com/arkflow-rs/arkflow";>ArkFlow</a> High-performance Rust
stream processing engine</p></li>
+<li><p><a class="reference external"
href="https://github.com/apache/auron";>Auron</a> The Auron accelerator for big
data engine (e.g., Spark, Flink) leverages native vectorized execution to
accelerate query processing</p></li>
<li><p><a class="reference external"
href="https://github.com/apache/datafusion-ballista";>Ballista</a> Distributed
SQL Query Engine</p></li>
-<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>
Cube’s universal semantic layer platform is the next evolution of OLAP
technology for AI, BI, spreadsheets, and embedded analytics</p></li>
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