diff --git a/docs/dev/python/datastream-api-users-guide/intro_to_datastream_api.md b/docs/dev/python/datastream-api-users-guide/intro_to_datastream_api.md
new file mode 100644
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--- /dev/null
+++ b/docs/dev/python/datastream-api-users-guide/intro_to_datastream_api.md
@@ -0,0 +1,333 @@
+---
+title: "Intro to the Python DataStream API"
+nav-parent_id: python_datastream_api
+nav-pos: 5
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+DataStream programs in Flink are regular programs that implement transformations on data streams
+(e.g., filtering, updating state, defining windows, aggregating). The data streams are initially
+created from various sources (e.g., message queues, socket streams, files). Results are returned via
+sinks, which may for example write the data to files, or to standard output (for example the command
+line terminal).
+
+Python DataStream API is a Python version of DataStream API which allows Python users could write
+Python DatStream API jobs.
+
+* This will be replaced by the TOC
+{:toc}
+
+Common Structure of Python DataStream API Programs
+--------------------------------------------
+
+The following code example shows the common structure of Python DataStream API programs.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.common.serialization import JsonRowDeserializationSchema, JsonRowSerializationSchema
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.connectors import FlinkKafkaConsumer, FlinkKafkaProducer
+
+
+def datastream_api_demo():
+    # 1. create a StreamExecutionEnvironment
+    env = StreamExecutionEnvironment.get_execution_environment()
+    # the sql connector for kafka is used here as it's a fat jar and could avoid dependency issues
+    env.add_jars("file:///path/to/flink-sql-connector-kafka.jar")
+
+    # 2. create source DataStream
+    deserialization_schema = JsonRowDeserializationSchema.builder() \
+        .type_info(type_info=Types.ROW([Types.LONG(), Types.LONG()])).build()
+
+    kafka_source = FlinkKafkaConsumer(
+        topics='test_source_topic',
+        deserialization_schema=deserialization_schema,
+        properties={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+
+    ds = env.add_source(kafka_source)
+
+    # 3. define the execution logic
+    ds = ds.map(lambda a: Row(a % 4, 1), output_type=Types.ROW([Types.LONG(), Types.LONG()])) \
+           .key_by(lambda a: a[0]) \
+           .reduce(lambda a, b: Row(a[0], a[1] + b[1]))
+
+    # 4. create sink and emit result to sink
+    serialization_schema = JsonRowSerializationSchema.builder().with_type_info(
+        type_info=Types.ROW([Types.LONG(), Types.LONG()])).build()
+    kafka_sink = FlinkKafkaProducer(
+        topic='test_sink_topic',
+        serialization_schema=serialization_schema,
+        producer_config={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+    ds.add_sink(kafka_sink)
+
+    # 5. execute the job
+    env.execute('datastream_api_demo')
+
+
+if __name__ == '__main__':
+    datastream_api_demo()
+{% endhighlight %}
+
+{% top %}
+
+Create a StreamExecutionEnvironment
+---------------------------
+
+The `StreamExecutionEnvironment` is a central concept of the DataStream API program.
+The following code example shows how to create a `StreamExecutionEnvironment`:
+
+{% highlight python %}
+from pyflink.datastream import StreamExecutionEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+{% endhighlight %}
+
+{% top %}
+
+Create a DataStream
+---------------
+
+The DataStream API gets its name from the special `DataStream` class that is
+used to represent a collection of data in a Flink program. You can think of
+them as immutable collections of data that can contain duplicates. This data
+can either be finite or unbounded, the API that you use to work on them is the
+same.
+
+A `DataStream` is similar to a regular Python `Collection` in terms of usage but
+is quite different in some key ways. They are immutable, meaning that once they
+are created you cannot add or remove elements. You can also not simply inspect
+the elements inside but only work on them using the `DataStream` API
+operations, which are also called transformations.
+
+You can create an initial `DataStream` by adding a source in a Flink program.
+Then you can derive new streams from this and combine them by using API methods
+such as `map`, `filter`, and so on.
+
+### Create from a list object
+
+You can create a `DataStream` from a list object:
+
+{% highlight python %}
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+ds = env.from_collection(
+    collection=[(1, 'aaa|bb'), (2, 'bb|a'), (3, 'aaa|a')],
+    type_info=Types.ROW([Types.INT(), Types.STRING()]))
+{% endhighlight %}
+
+The parameter `type_info` is optional, if not specified, the output type of the returned `DataStream`
+will be `Types.PICKLED_BYTE_ARRAY()`.
+
+### Create using DataStream connectors
+
+You can also create a `DataStream` using DataStream connectors with method `add_source` as following:
+
+{% highlight python %}
+from pyflink.common.serialization import JsonRowDeserializationSchema
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.connectors import FlinkKafkaConsumer
+
+deserialization_schema = JsonRowDeserializationSchema.builder() \
+    .type_info(type_info=Types.ROW([Types.INT(), Types.STRING()])).build()
+
+kafka_consumer = FlinkKafkaConsumer(
+    topics='test_source_topic',
+    deserialization_schema=deserialization_schema,
+    properties={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+
+env = StreamExecutionEnvironment.get_execution_environment()
+# the sql connector for kafka is used here as it's a fat jar and could avoid dependency issues
+env.add_jars("file:///path/to/flink-sql-connector-kafka.jar")
+ds = env.add_source(kafka_consumer)
+{% endhighlight %}
+
+<span class="label label-info">Note</span> It currently only supports `FlinkKafkaConsumer` to be
+used as DataStream source connectors.
+
+### Create using Table & SQL connectors
+
+Table & SQL connectors could also be used to create a `DataStream`. You could firstly create a
+`Table` using Table & SQL connectors and then convert it to a `DataStream`.
+
+{% highlight python %}
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import StreamTableEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+t_env = StreamTableEnvironment.create(stream_execution_environment=env)
+
+t_env.execute_sql("""
+        CREATE TABLE my_source (
+          a INT,
+          b VARCHAR
+        ) WITH (
+          'connector' = 'datagen',
+          'number-of-rows' = '10'
+        )
+    """)
+
+ds = t_env.to_append_stream(
+    t_env.from_path('my_source'),
+    Types.ROW([Types.INT(), Types.STRING()]))
+{% endhighlight %}
+
+<span class="label label-info">Note</span> The StreamExecutionEnvironment `env` should be specified
+when creating the TableEnvironment `t_env`.
+
+<span class="label label-info">Note</span> As all the Java Table & SQL connectors could be used in
+PyFlink Table API, this means that all of them could also be used in PyFlink DataStream API.
+
+{% top %}
+
+DataStream Transformations
+---------------
+
+Operators transform one or more `DataStream` into a new `DataStream`. Programs can combine multiple
+transformations into sophisticated dataflow topologies.
+
+The following example shows a simple example about how to convert a `DataStream` into another
+`DataStream` using `map` transformation:
+
+{% highlight python %}
+ds = ds.map(lambda a: a + 1)
+{% endhighlight %}
+
+Please see [operators]({% link dev/stream/operators/index.md %}) for an overview of the
+available DataStream transformations.
+
+Conversion between DataStream and Table
+---------------
+
+It also supports to convert a `DataStream` to a `Table` and vice verse.
+
+{% highlight python %}
+# convert a DataStream to a Table
+table = t_env.from_data_stream(ds, 'a, b, c')
+# convert a Table to a DataStream
+ds = table.to_append_stream(table, Types.ROW([Types.INT(), Types.STRING()]))
+# or
+ds = table.to_retract_stream(table, Types.ROW([Types.INT(), Types.STRING()]))
+{% endhighlight %}
+
+{% top %}
+
+Emit Results
+----------------
+
+### Print
+
+You can call the `print` method to print the data of a `DataStream` to the standard output:
+
+{% highlight python %}
+ds.print()
+{% endhighlight %}
+
+### Emit results to a DataStream sink connector
+
+You can call the `add_sink` method to emit the data of a `DataStream` to a DataStream sink connector:
+
+{% highlight python %}
+from pyflink.common.typeinfo import Types
+from pyflink.datastream.connectors import FlinkKafkaProducer
+from pyflink.common.serialization import JsonRowSerializationSchema
+
+serialization_schema = JsonRowSerializationSchema.builder().with_type_info(
+    type_info=Types.ROW([Types.INT(), Types.STRING()])).build()
+kafka_producer = FlinkKafkaProducer(
+    topic='test_sink_topic',
+    serialization_schema=serialization_schema,
+    producer_config={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+ds.add_sink(kafka_producer)
+{% endhighlight %}
+
+<span class="label label-info">Note</span> It currently only supports FlinkKafkaProducer,
+JdbcSink and StreamingFileSink to be used as DataStream sink connectors.
+
+### Emit results to a Table & SQL sink connector
+
+Table & SQL connectors could also be used to write out a `DataStream`. You need firstly convert a
+`DataStream` to a `Table` and then write it to a Table & SQL sink connector.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import StreamTableEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+t_env = StreamTableEnvironment.create(stream_execution_environment=env)
+# option 1：the result type of ds is Types.ROW
+def split(s):
+    splits = s[1].split("|")
+    for sp in splits:
+        yield Row(s[0], sp)
+
+ds = ds.map(lambda i: (i[0] + 1, i[1])) \
+       .flat_map(split, Types.ROW([Types.INT(), Types.STRING()])) \
+       .key_by(lambda i: i[1]) \
+       .reduce(lambda i, j: Row(i[0] + j[0], i[1]))
+
+# option 1：the result type of ds is Types.TUPLE
+def split(s):
+    splits = s[1].split("|")
+    for sp in splits:
+        yield s[0], sp
+
+ds = ds.map(lambda i: (i[0] + 1, i[1])) \
+       .flat_map(split, Types.TUPLE([Types.INT(), Types.STRING()])) \
+       .key_by(lambda i: i[1]) \
+       .reduce(lambda i, j: (i[0] + j[0], i[1]))
+
+# emit ds to print sink
+t_env.execute_sql("""
+        CREATE TABLE my_sink (
+          a INT,
+          b VARCHAR
+        ) WITH (
+          'connector' = 'print'
+        )
+    """)
+table = t_env.from_data_stream(ds)
+table_result = table.execute_insert("my_sink")
+{% endhighlight %}
+
+<span class="label label-info">Note</span> The output type of DataStream `ds` must be composite type.
+
+Submit Job
+----------------
+
+Finally, you should call the `StreamExecutionEnvironment.execute` method to submit the DataStream
+API job for execution:
+
+{% highlight python %}
+env.execute()
+{% endhighlight %}
+
+If you convert the `DataStream` to a `Table` and then write it to a Table API & SQL sink connector,
+it may happen that you need to submit the job using `TableEnvironment.execute` method.
+
+{% highlight python %}
+t_env.execute()
+{% endhighlight %}
diff --git a/docs/dev/python/datastream-api-users-guide/intro_to_datastream_api.zh.md b/docs/dev/python/datastream-api-users-guide/intro_to_datastream_api.zh.md
new file mode 100644
index 0000000000000..06e1b2909f277
--- /dev/null
+++ b/docs/dev/python/datastream-api-users-guide/intro_to_datastream_api.zh.md
@@ -0,0 +1,333 @@
+---
+title: "Python DataStream API 简介"
+nav-parent_id: python_datastream_api
+nav-pos: 5
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+DataStream programs in Flink are regular programs that implement transformations on data streams
+(e.g., filtering, updating state, defining windows, aggregating). The data streams are initially
+created from various sources (e.g., message queues, socket streams, files). Results are returned via
+sinks, which may for example write the data to files, or to standard output (for example the command
+line terminal).
+
+Python DataStream API is a Python version of DataStream API which allows Python users could write
+Python DatStream API jobs.
+
+* This will be replaced by the TOC
+{:toc}
+
+Common Structure of Python DataStream API Programs
+--------------------------------------------
+
+The following code example shows the common structure of Python DataStream API programs.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.common.serialization import JsonRowDeserializationSchema, JsonRowSerializationSchema
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.connectors import FlinkKafkaConsumer, FlinkKafkaProducer
+
+
+def datastream_api_demo():
+    # 1. create a StreamExecutionEnvironment
+    env = StreamExecutionEnvironment.get_execution_environment()
+    # the sql connector for kafka is used here as it's a fat jar and could avoid dependency issues
+    env.add_jars("file:///path/to/flink-sql-connector-kafka.jar")
+
+    # 2. create source DataStream
+    deserialization_schema = JsonRowDeserializationSchema.builder() \
+        .type_info(type_info=Types.ROW([Types.LONG(), Types.LONG()])).build()
+
+    kafka_source = FlinkKafkaConsumer(
+        topics='test_source_topic',
+        deserialization_schema=deserialization_schema,
+        properties={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+
+    ds = env.add_source(kafka_source)
+
+    # 3. define the execution logic
+    ds = ds.map(lambda a: Row(a % 4, 1), output_type=Types.ROW([Types.LONG(), Types.LONG()])) \
+           .key_by(lambda a: a[0]) \
+           .reduce(lambda a, b: Row(a[0], a[1] + b[1]))
+
+    # 4. create sink and emit result to sink
+    serialization_schema = JsonRowSerializationSchema.builder().with_type_info(
+        type_info=Types.ROW([Types.LONG(), Types.LONG()])).build()
+    kafka_sink = FlinkKafkaProducer(
+        topic='test_sink_topic',
+        serialization_schema=serialization_schema,
+        producer_config={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+    ds.add_sink(kafka_sink)
+
+    # 5. execute the job
+    env.execute('datastream_api_demo')
+
+
+if __name__ == '__main__':
+    datastream_api_demo()
+{% endhighlight %}
+
+{% top %}
+
+Create a StreamExecutionEnvironment
+---------------------------
+
+The `StreamExecutionEnvironment` is a central concept of the DataStream API program.
+The following code example shows how to create a `StreamExecutionEnvironment`:
+
+{% highlight python %}
+from pyflink.datastream import StreamExecutionEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+{% endhighlight %}
+
+{% top %}
+
+Create a DataStream
+---------------
+
+The DataStream API gets its name from the special `DataStream` class that is
+used to represent a collection of data in a Flink program. You can think of
+them as immutable collections of data that can contain duplicates. This data
+can either be finite or unbounded, the API that you use to work on them is the
+same.
+
+A `DataStream` is similar to a regular Python `Collection` in terms of usage but
+is quite different in some key ways. They are immutable, meaning that once they
+are created you cannot add or remove elements. You can also not simply inspect
+the elements inside but only work on them using the `DataStream` API
+operations, which are also called transformations.
+
+You can create an initial `DataStream` by adding a source in a Flink program.
+Then you can derive new streams from this and combine them by using API methods
+such as `map`, `filter`, and so on.
+
+### Create from a list object
+
+You can create a `DataStream` from a list object:
+
+{% highlight python %}
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+ds = env.from_collection(
+    collection=[(1, 'aaa|bb'), (2, 'bb|a'), (3, 'aaa|a')],
+    type_info=Types.ROW([Types.INT(), Types.STRING()]))
+{% endhighlight %}
+
+The parameter `type_info` is optional, if not specified, the output type of the returned `DataStream`
+will be `Types.PICKLED_BYTE_ARRAY()`.
+
+### Create using DataStream connectors
+
+You can also create a `DataStream` using DataStream connectors with method `add_source` as following:
+
+{% highlight python %}
+from pyflink.common.serialization import JsonRowDeserializationSchema
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.connectors import FlinkKafkaConsumer
+
+deserialization_schema = JsonRowDeserializationSchema.builder() \
+    .type_info(type_info=Types.ROW([Types.INT(), Types.STRING()])).build()
+
+kafka_consumer = FlinkKafkaConsumer(
+    topics='test_source_topic',
+    deserialization_schema=deserialization_schema,
+    properties={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+
+env = StreamExecutionEnvironment.get_execution_environment()
+# the sql connector for kafka is used here as it's a fat jar and could avoid dependency issues
+env.add_jars("file:///path/to/flink-sql-connector-kafka.jar")
+ds = env.add_source(kafka_consumer)
+{% endhighlight %}
+
+<span class="label label-info">Note</span> It currently only supports `FlinkKafkaConsumer` to be
+used as DataStream source connectors.
+
+### Create using Table & SQL connectors
+
+Table & SQL connectors could also be used to create a `DataStream`. You could firstly create a
+`Table` using Table & SQL connectors and then convert it to a `DataStream`.
+
+{% highlight python %}
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import StreamTableEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+t_env = StreamTableEnvironment.create(stream_execution_environment=env)
+
+t_env.execute_sql("""
+        CREATE TABLE my_source (
+          a INT,
+          b VARCHAR
+        ) WITH (
+          'connector' = 'datagen',
+          'number-of-rows' = '10'
+        )
+    """)
+
+ds = t_env.to_append_stream(
+    t_env.from_path('my_source'),
+    Types.ROW([Types.INT(), Types.STRING()]))
+{% endhighlight %}
+
+<span class="label label-info">Note</span> The StreamExecutionEnvironment `env` should be specified
+when creating the TableEnvironment `t_env`.
+
+<span class="label label-info">Note</span> As all the Java Table & SQL connectors could be used in
+PyFlink Table API, this means that all of them could also be used in PyFlink DataStream API.
+
+{% top %}
+
+DataStream Transformations
+---------------
+
+Operators transform one or more `DataStream` into a new `DataStream`. Programs can combine multiple
+transformations into sophisticated dataflow topologies.
+
+The following example shows a simple example about how to convert a `DataStream` into another
+`DataStream` using `map` transformation:
+
+{% highlight python %}
+ds = ds.map(lambda a: a + 1)
+{% endhighlight %}
+
+Please see [operators]({% link dev/stream/operators/index.zh.md %}) for an overview of the
+available DataStream transformations.
+
+Conversion between DataStream and Table
+---------------
+
+It also supports to convert a `DataStream` to a `Table` and vice verse.
+
+{% highlight python %}
+# convert a DataStream to a Table
+table = t_env.from_data_stream(ds, 'a, b, c')
+# convert a Table to a DataStream
+ds = table.to_append_stream(table, Types.ROW([Types.INT(), Types.STRING()]))
+# or
+ds = table.to_retract_stream(table, Types.ROW([Types.INT(), Types.STRING()]))
+{% endhighlight %}
+
+{% top %}
+
+Emit Results
+----------------
+
+### Print
+
+You can call the `print` method to print the data of a `DataStream` to the standard output:
+
+{% highlight python %}
+ds.print()
+{% endhighlight %}
+
+### Emit results to a DataStream sink connector
+
+You can call the `add_sink` method to emit the data of a `DataStream` to a DataStream sink connector:
+
+{% highlight python %}
+from pyflink.common.typeinfo import Types
+from pyflink.datastream.connectors import FlinkKafkaProducer
+from pyflink.common.serialization import JsonRowSerializationSchema
+
+serialization_schema = JsonRowSerializationSchema.builder().with_type_info(
+    type_info=Types.ROW([Types.INT(), Types.STRING()])).build()
+kafka_producer = FlinkKafkaProducer(
+    topic='test_sink_topic',
+    serialization_schema=serialization_schema,
+    producer_config={'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'})
+ds.add_sink(kafka_producer)
+{% endhighlight %}
+
+<span class="label label-info">Note</span> It currently only supports FlinkKafkaProducer,
+JdbcSink and StreamingFileSink to be used as DataStream sink connectors.
+
+### Emit results to a Table & SQL sink connector
+
+Table & SQL connectors could also be used to write out a `DataStream`. You need firstly convert a
+`DataStream` to a `Table` and then write it to a Table & SQL sink connector.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import StreamTableEnvironment
+
+env = StreamExecutionEnvironment.get_execution_environment()
+t_env = StreamTableEnvironment.create(stream_execution_environment=env)
+# option 1：the result type of ds is Types.ROW
+def split(s):
+    splits = s[1].split("|")
+    for sp in splits:
+        yield Row(s[0], sp)
+
+ds = ds.map(lambda i: (i[0] + 1, i[1])) \
+       .flat_map(split, Types.ROW([Types.INT(), Types.STRING()])) \
+       .key_by(lambda i: i[1]) \
+       .reduce(lambda i, j: Row(i[0] + j[0], i[1]))
+
+# option 1：the result type of ds is Types.TUPLE
+def split(s):
+    splits = s[1].split("|")
+    for sp in splits:
+        yield s[0], sp
+
+ds = ds.map(lambda i: (i[0] + 1, i[1])) \
+       .flat_map(split, Types.TUPLE([Types.INT(), Types.STRING()])) \
+       .key_by(lambda i: i[1]) \
+       .reduce(lambda i, j: (i[0] + j[0], i[1]))
+
+# emit ds to print sink
+t_env.execute_sql("""
+        CREATE TABLE my_sink (
+          a INT,
+          b VARCHAR
+        ) WITH (
+          'connector' = 'print'
+        )
+    """)
+table = t_env.from_data_stream(ds)
+table_result = table.execute_insert("my_sink")
+{% endhighlight %}
+
+<span class="label label-info">Note</span> The output type of DataStream `ds` must be composite type.
+
+Submit Job
+----------------
+
+Finally, you should call the `StreamExecutionEnvironment.execute` method to submit the DataStream
+API job for execution:
+
+{% highlight python %}
+env.execute()
+{% endhighlight %}
+
+If you convert the `DataStream` to a `Table` and then write it to a Table API & SQL sink connector,
+it may happen that you need to submit the job using `TableEnvironment.execute` method.
+
+{% highlight python %}
+t_env.execute()
+{% endhighlight %}