Github user alpinegizmo commented on a diff in the pull request:
https://github.com/apache/flink/pull/4013#discussion_r119360012
--- Diff: docs/dev/tableApi.md ---
@@ -25,32 +25,16 @@ specific language governing permissions and limitations
under the License.
-->
-**Table API and SQL are experimental features**
+Apache Flink features two relational APIs - the Table API and SQL - for
unified stream and batch processing. The Table API is a language-integrated
query API for Scala and Java that allows to compose queries from relational
operators such as selection, filter, and join in a very intuitive way. Flink's
SQL support is based on [Apache Calcite](https://calcite.apache.org) which
implements the SQL standard. Queries specified in either interface have the
same semantics and specify the same result regardless whether the input is a
batch input (DataSet) or a stream input (DataStream).
-The Table API is a SQL-like expression language for relational stream and
batch processing that can be easily embedded in Flink's DataSet and DataStream
APIs (Java and Scala).
-The Table API and SQL interface operate on a relational `Table`
abstraction, which can be created from external data sources, or existing
DataSets and DataStreams. With the Table API, you can apply relational
operators such as selection, aggregation, and joins on `Table`s.
+The Table API and the SQL interfaces are tightly integrated with each
other as well as Flink's DataStream and DataSet APIs. You can easily switch
between all APIs and libraries which build upon the APIs. For instance, you can
extract patterns from a DataStream using the [CEP library]({{ site.baseurl
}}/dev/libs/cep.html) and later use the Table API to analyze the patterns or
you scan, filter, and aggregate a batch table using a SQL query before running
a [Gelly graph algorithm]({{ site.baseurl }}/dev/libs/gelly) on the
preprocessed data.
--- End diff --
... and later use the Table API to analyze the patterns, or you can scan,
filter, and aggregate ...
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