Merge pull request #206921 from sreekzz/patch-84

Removing Storm Section

Author: PMEds28

articles/data-lake-store/data-lake-store-data-scenarios.md

@@ -5,7 +5,7 @@ description: Understand the different scenarios and tools using which data can i
 author: normesta
 ms.service: data-lake-store
 ms.topic: conceptual
-ms.date: 06/27/2018
+ms.date: 08/05/2022
 ms.author: normesta
 
 ---
@@ -41,7 +41,6 @@ This represents data that can be generated by various sources such as applicatio
 Following are tools that you can use:
 
 * [Azure Stream Analytics](../stream-analytics/stream-analytics-define-outputs.md) - Events ingested into Event Hubs can be written to Azure Data Lake Storage Gen1 using an Azure Data Lake Storage Gen1 output.
-* [Azure HDInsight Storm](../hdinsight/storm/apache-storm-write-data-lake-store.md) - You can write data directly to Data Lake Storage Gen1 from the Storm cluster.
 * [EventProcessorHost](../event-hubs/event-hubs-dotnet-standard-getstarted-send.md) – You can receive events from Event Hubs and then write it to Data Lake Storage Gen1 using the [Data Lake Storage Gen1 .NET SDK](data-lake-store-get-started-net-sdk.md).
 
 ### Relational data
@@ -123,4 +122,4 @@ You can use a mix of services to create visual representations of data stored in
 ![Visualize data in Data Lake Storage Gen1](./media/data-lake-store-data-scenarios/visualize-data.png "Visualize data in Data Lake Storage Gen1")
 
 * You can start by using [Azure Data Factory to move data from Data Lake Storage Gen1 to Azure Synapse Analytics](../data-factory/copy-activity-overview.md)
-* After that, you can [integrate Power BI with Azure Synapse Analytics](/power-bi/connect-data/service-azure-sql-data-warehouse-with-direct-connect) to create visual representation of the data.
+* After that, you can [integrate Power BI with Azure Synapse Analytics](/power-bi/connect-data/service-azure-sql-data-warehouse-with-direct-connect) to create visual representation of the data.

articles/data-lake-store/data-lake-store-hdinsight-hadoop-use-portal.md

@@ -188,12 +188,10 @@ You can use a Spark cluster to run Spark jobs on data that is stored in a Data L
 
 ### Use Data Lake Storage Gen1 in a Storm topology
 
-You can use the storage account with Data Lake Storage Gen1 to write data from a Storm topology. For instructions on how to achieve this scenario, see [Use Azure Data Lake Storage Gen1 with Apache Storm with HDInsight](../hdinsight/storm/apache-storm-write-data-lake-store.md).
-
 ## See also
 
 * [Use Data Lake Storage Gen1 with Azure HDInsight clusters](../hdinsight/hdinsight-hadoop-use-data-lake-storage-gen1.md)
 * [PowerShell: Create an HDInsight cluster to use Data Lake Storage Gen1](data-lake-store-hdinsight-hadoop-use-powershell.md)
 
 [makecert]: /windows-hardware/drivers/devtest/makecert
-[pvk2pfx]: /windows-hardware/drivers/devtest/pvk2pfx
+[pvk2pfx]: /windows-hardware/drivers/devtest/pvk2pfx

articles/data-lake-store/data-lake-store-performance-tuning-storm.md

@@ -18,7 +18,6 @@ Understand the factors that should be considered when you tune the performance o
 * **An Azure subscription**. See [Get Azure free trial](https://azure.microsoft.com/pricing/free-trial/).
 * **An Azure Data Lake Storage Gen1 account**. For instructions on how to create one, see [Get started with Azure Data Lake Storage Gen1](data-lake-store-get-started-portal.md).
 * **An Azure HDInsight cluster** with access to a Data Lake Storage Gen1 account. See [Create an HDInsight cluster with Data Lake Storage Gen1](data-lake-store-hdinsight-hadoop-use-portal.md). Make sure you enable Remote Desktop for the cluster.
-* **Running a Storm cluster on Data Lake Storage Gen1**. For more information, see [Storm on HDInsight](../hdinsight/storm/apache-storm-overview.md).
 * **Performance tuning guidelines on Data Lake Storage Gen1**.  For general performance concepts, see [Data Lake Storage Gen1 Performance Tuning Guidance](./data-lake-store-performance-tuning-guidance.md).  
 
 ## Tune the parallelism of the topology
@@ -128,4 +127,4 @@ To check if you are getting throttled, enable the debug logging on the client si
 ## Next steps
 Additional performance tuning for Storm can be referenced in [this blog](/archive/blogs/shanyu/performance-tuning-for-hdinsight-storm-and-microsoft-azure-eventhubs).
 
-For an additional example to run, see [this one on GitHub](https://github.com/hdinsight/storm-performance-automation).
+For an additional example to run, see [this one on GitHub](https://github.com/hdinsight/storm-performance-automation).

articles/event-hubs/event-hubs-storm-getstarted-receive.md

@@ -18,22 +18,18 @@ Before you start with the quickstart, **create an Event Hubs namespace and an ev
 
 ## Create project and add code
 
-This tutorial uses an [HDInsight Storm][HDInsight Storm] installation, which comes with the Event Hubs spout already available.
-
-1. Follow the [HDInsight Storm - Get Started](../hdinsight/storm/apache-storm-overview.md) procedure to create a new HDInsight cluster, and connect to it via Remote Desktop.
-2. Copy the `%STORM_HOME%\examples\eventhubspout\eventhubs-storm-spout-0.9-jar-with-dependencies.jar` file to your local development environment. This contains the events-storm-spout.
-3. Use the following command to install the package into the local Maven store. This enables you to add it as a reference in the Storm project in a later step.
+1. Use the following command to install the package into the local Maven store. This enables you to add it as a reference in the Storm project in a later step.
 
     ```shell
     mvn install:install-file -Dfile=target\eventhubs-storm-spout-0.9-jar-with-dependencies.jar -DgroupId=com.microsoft.eventhubs -DartifactId=eventhubs-storm-spout -Dversion=0.9 -Dpackaging=jar
     ```
-4. In Eclipse, create a new Maven project (click **File**, then **New**, then **Project**).
+1. In Eclipse, create a new Maven project (click **File**, then **New**, then **Project**).
 
     ![File -> New -> Project][12]
-5. Select **Use default Workspace location**, then click **Next**
-6. Select the **maven-archetype-quickstart** archetype, then click **Next**
-7. Insert a **GroupId** and **ArtifactId**, then click **Finish**
-8. In **pom.xml**, add the following dependencies in the `<dependency>` node.
+1. Select **Use default Workspace location**, then click **Next**
+1. Select the **maven-archetype-quickstart** archetype, then click **Next**
+1. Insert a **GroupId** and **ArtifactId**, then click **Finish**
+1. In **pom.xml**, add the following dependencies in the `<dependency>` node.
 
     ```xml  
     <dependency>
@@ -65,7 +61,7 @@ This tutorial uses an [HDInsight Storm][HDInsight Storm] installation, which com
     </dependency>
     ```
 
-9. In the **src** folder, create a file called **Config.properties** and copy the following content, substituting the `receive rule key` and `event hub name` values:
+1. In the **src** folder, create a file called **Config.properties** and copy the following content, substituting the `receive rule key` and `event hub name` values:
 
 	```java
 	eventhubspout.username = ReceiveRule
@@ -81,7 +77,7 @@ This tutorial uses an [HDInsight Storm][HDInsight Storm] installation, which com
 	eventhub.receiver.credits = 10
 	```
     The value for **eventhub.receiver.credits** determines how many events are batched before releasing them to the Storm pipeline. For the sake of simplicity, this example sets this value to 10. In production, it should usually be set to higher values; for example, 1024.
-10. Create a new class called **LoggerBolt** with the following code:
+1 . Create a new class called **LoggerBolt** with the following code:
 
 	```java
 	import java.util.Map;

articles/hdinsight/.openpublishing.redirection.hdinsight.json

@@ -1,5 +1,64 @@
 {
     "redirections": [
+    {   "source_path_from_root": "/articles/hdinsight/storm/apache-storm-overview.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-quickstart.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-write-data-lake-store.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/migrate-storm-to-spark.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-develop-java-topology.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-develop-python-topology.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-develop-csharp-visual-studio-topology.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-scp-programming-guide.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-example-topology.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-develop-csharp-event-hub-topology.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-storm-deploy-monitor-topology-linux.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    }, 
+    {
+        "source_path_from_root": "/articles/hdinsight/storm/apache-troubleshoot-storm.md",
+        "redirect_url": "/azure/hdinsight/hdinsight-overview",
+        "redirect_document_id": false
+    },
     {
         "source_path_from_root": "/articles/data-lake-store/data-lake-storage-use-hdi-cluster.md",
         "redirect_url": "/azure/hdinsight/hdinsight-hadoop-use-data-lake-storage-gen2",

articles/hdinsight/TOC.yml

@@ -925,52 +925,6 @@ items:
     items:
     - name: Hive LLAP Workload Management commands
       href: ./interactive-query/workload-management-commands.md
-- name: Apache Storm
-  items:
-  - name: Overview
-    items:
-    - name: What is Apache Storm in HDInsight?
-      href: ./storm/apache-storm-overview.md
-  - name: Quickstarts
-    items:
-    - name: Create Apache Storm topology in HDInsight
-      href: ./storm/apache-storm-quickstart.md
-  - name: Tutorials
-    items:
-    - name: Use Apache Storm to write to storage
-      href: ./storm/apache-storm-write-data-lake-store.md
-    - name: Apache Kafka and Apache Storm (Java)
-      href: ./hdinsight-apache-storm-with-kafka.md
-  - name: How-to guides
-    items:
-    - name: Migrate Storm to Spark
-      href: ./storm/migrate-storm-to-spark.md
-    - name: Develop
-      items:
-      - name: Topologies
-        items:
-        - name: Develop topologies using Java
-          href: ./storm/apache-storm-develop-java-topology.md
-        - name: Develop topologies using Python
-          href: ./storm/apache-storm-develop-python-topology.md
-        - name: Develop topologies using C#
-          href: ./storm/apache-storm-develop-csharp-visual-studio-topology.md
-        - name: SCP.NET programming guide
-          href: ./storm/apache-storm-scp-programming-guide.md
-        - name: Apache Storm examples
-          href: ./storm/apache-storm-example-topology.md
-      - name: Streaming pipelines
-        items:
-        - name: Event Hubs and Apache Storm (C# & Java)
-          href: ./storm/apache-storm-develop-csharp-event-hub-topology.md
-    - name: Manage
-      items:
-      - name: Monitor topologies
-        href: ./storm/apache-storm-deploy-monitor-topology-linux.md
-    - name: Troubleshoot
-      items:
-      - name: Apache STORM troubleshooting
-        href: ./storm/apache-troubleshoot-storm.md
 - name: Enterprise readiness
   items:
   - name: Overview
@@ -1057,4 +1011,3 @@ items:
   items:
   - name: Share Hive metastore with Synapse Spark
     href: share-hive-metastore-with-synapse.md
-    

articles/hdinsight/hadoop/apache-hadoop-visual-studio-tools-get-started.md

@@ -5,7 +5,7 @@ keywords: hadoop tools,hive query,visual studio,visual studio hadoop
 ms.service: hdinsight
 ms.custom: hdinsightactive,hdiseo17may2017,seodec18
 ms.topic: how-to
-ms.date: 04/07/2022
+ms.date: 08/05/2022
 ---
 
 # Use Data Lake Tools for Visual Studio to connect to Azure HDInsight and run Apache Hive queries
@@ -14,8 +14,6 @@ Learn how to use Microsoft Azure Data Lake and Stream Analytics Tools for Visual
 
 For more information about using HDInsight, see [Get started with HDInsight](apache-hadoop-linux-tutorial-get-started.md).  
 
-For more information on connecting to Apache Storm, see [Develop C# topologies for Apache Storm by using the Data Lake tools](../storm/apache-storm-develop-csharp-visual-studio-topology.md).
-
 You can use Data Lake Tools for Visual Studio to access Azure Data Lake Analytics and HDInsight. For information about Data Lake Tools, see [Develop U-SQL scripts by using Data Lake Tools for Visual Studio](../../data-lake-analytics/data-lake-analytics-data-lake-tools-get-started.md).
 
 ## Prerequisites

articles/hdinsight/hdinsight-36-component-versioning.md

@@ -3,7 +3,7 @@ title: Apache Hadoop components and versions - Azure HDInsight 3.6
 description: Learn about the Apache Hadoop components and versions in Azure HDInsight 3.6.
 ms.service: hdinsight
 ms.topic: conceptual
-ms.date: 02/15/2022
+ms.date: 08/05/2022
 ---
 
 # HDInsight 3.6 component versions
@@ -22,7 +22,6 @@ The table below lists the support timeframe for HDInsight 3.6 cluster types.
 | HDInsight 3.6 Kafka             | 1.1               | June 30, 2021                     | September 30, 2022                | October 1, 2022 |
 | HDInsight 3.6 HBase             | 1.1               | June 30, 2021                     | September 30, 2022                | October 1, 2022 |
 | HDInsight 3.6 Interactive Query | 2.1               | June 30, 2021                     | September 30, 2022                | October 1, 2022 |
-| HDInsight 3.6 Storm             | 1.1               | June 30, 2021                     | September 30, 2022                | October 1, 2022 |
 | HDInsight 3.6  ML Services      | 9.3               | -                                 | -                                 | December 31, 2020 |
 | HDInsight 3.6 Spark             | 2.2               | -                                 | -                                 | June 30, 2020 |
 | HDInsight 3.6 Spark             | 2.1               | -                                 | -                                 | June 30, 2020 |
@@ -44,7 +43,6 @@ The OSS component versions associated with HDInsight 3.6 are listed in the follo
 | Apache Sqoop           | 1.4.6                       |
 | Apache Oozie           | 4.2.0                       |
 | Apache Zookeeper       | 3.4.6                       |
-| Apache Storm           | 1.1.0                       |
 | Apache Mahout          | 0.9.0+                      |
 | Apache Phoenix         | 4.7.0                       |
 | Apache Spark           | 2.3.2.                      |
@@ -59,7 +57,6 @@ The OSS component versions associated with HDInsight 3.6 are listed in the follo
 - [Migrate Azure HDInsight 3.6 Hive workloads to HDInsight 4.0](interactive-query/apache-hive-migrate-workloads.md).
 - [Migrate Apache Kafka workloads to Azure HDInsight 4.0](kafka/migrate-versions.md).
 - [Migrate an Apache HBase cluster to a new version](hbase/apache-hbase-migrate-new-version.md).
-- [Migrate Azure HDInsight 3.6 Apache Storm to HDInsight 4.0 Apache Spark](storm/migrate-storm-to-spark.md).
 
 ## Next steps
 

articles/hdinsight/hdinsight-apache-storm-with-kafka.md

@@ -4,7 +4,7 @@ description: Learn how to create a streaming pipeline using Apache Storm and Apa
 ms.service: hdinsight
 ms.custom: hdinsightactive
 ms.topic: tutorial
-ms.date: 06/25/2019
+ms.date: 08/05/2022
 #Customer intent: As a developer, I want to learn how to build a streaming pipeline that uses Storm and Kafka to process streaming data.
 ---
 
@@ -29,8 +29,6 @@ In this tutorial, you learn how to:
 
 * Familiarity with creating Kafka topics. For more information, see the [Kafka on HDInsight quickstart](./kafka/apache-kafka-get-started.md) document.
 
-* Familiarity with building and deploying Storm solutions (topologies). Specifically, topologies that use [Apache Storm Flux](https://storm.apache.org/releases/current/flux.html) framework. For more information, see the [Create an Apache Storm topology in Java](./storm/apache-storm-develop-java-topology.md) document.
-
 * [Java JDK 1.8](https://www.oracle.com/technetwork/pt/java/javase/downloads/jdk8-downloads-2133151.html) or higher. HDInsight 3.5 or higher require Java 8.
 
 * [Maven 3.x](https://maven.apache.org/download.cgi)

articles/hdinsight/hdinsight-capacity-planning.md

@@ -55,7 +55,7 @@ For better performance, use only one container per storage account.
 
 ## Choose a cluster type
 
-The cluster type determines the workload your HDInsight cluster is configured to run. Types include [Apache Hadoop](./hadoop/apache-hadoop-introduction.md), [Apache Storm](./storm/apache-storm-overview.md), [Apache Kafka](./kafka/apache-kafka-introduction.md), or [Apache Spark](./spark/apache-spark-overview.md). For a detailed description of the available cluster types, see [Introduction to Azure HDInsight](hdinsight-overview.md#cluster-types-in-hdinsight). Each cluster type has a specific deployment topology that includes requirements for the size and number of nodes.
+The cluster type determines the workload your HDInsight cluster is configured to run. Types include [Apache Hadoop](./hadoop/apache-hadoop-introduction.md), [Apache Kafka](./kafka/apache-kafka-introduction.md), or [Apache Spark](./spark/apache-spark-overview.md). For a detailed description of the available cluster types, see [Introduction to Azure HDInsight](hdinsight-overview.md#cluster-types-in-hdinsight). Each cluster type has a specific deployment topology that includes requirements for the size and number of nodes.
 
 ## Choose the VM size and type