Update known users (apache#15895)

Author: comphead

docs/source/user-guide/introduction.md

@@ -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.