[GitHub] [airflow] mik-laj commented on a change in pull request #13461: Add How To Guide for Dataflow

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mik-laj commented on a change in pull request #13461:
URL: https://github.com/apache/airflow/pull/13461#discussion_r553372275



##########
File path: docs/apache-airflow-providers-google/operators/cloud/dataflow.rst
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@@ -0,0 +1,274 @@
+ .. 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.
+
+Google Cloud Dataflow Operators
+===============================
+
+`Dataflow <https://cloud.google.com/dataflow/>`__ is a managed service for
+executing a wide variety of data processing patterns. These pipelines are created
+using the Apache Beam programming model which allows for both batch and streaming.
+
+.. contents::
+  :depth: 1
+  :local:
+
+Prerequisite Tasks
+^^^^^^^^^^^^^^^^^^
+
+.. include::/operators/_partials/prerequisite_tasks.rst
+
+Ways to run a data pipeline
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are multiple options to execute a Dataflow pipeline on Airflow. If looking to execute the pipeline

Review comment:
       I would like to develop it, because it is very problematic for users.
   
   ```
   There are several ways to run a Dataflow pipeline depending on your environment, source files:
   - **Non-templated pipeline**: Developer can run the pipeline as a local process on the worker if you have a '*.jar' file for Java or a '* .py` file for Python. This also means that the necessary system dependencies must be installed on the worker.  For Java, worker must have the JRE Runtime installed. For Python, the Python interpreter. The runtime versions must be compatible with the pipeline versions. This is the fastest way to start a pipeline, but because of its frequent problems with system dependencies, it often causes problems. 
   - **Templated pipeline**: The programmer can make the pipeline independent of the environment by preparing a template that will then be run on a machine managed by Google. This way, changes to the environment won't affect your pipeline. There are two types of the templates:
        - **Classic templates**. Developers run the pipeline and create a template. The Apache Beam SDK stages files in Cloud Storage, creates a template file (similar to job request), and saves the template file in Cloud Storage.
       - **Flex Templates**. Developers package the pipeline into a Docker image and then use the `gcloud` command-line tool to build and save the Flex Template spec file in Cloud Storage. 
   - **SQL pipeline**: Developer can write pipeline as SQL statement and then execute it in Dataflow.
   
   It is a good idea to test your pipeline using the non-templated pipeline, and then run the pipeline in production using the templates.
   
   For details on the differences between the pipeline types, see `Dataflow templates <https://cloud.google.com/dataflow/docs/concepts/dataflow-templates>__` in the Google Cloud documentation.
   ```




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