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articles/hdinsight/hadoop/apache-hadoop-linux-create-cluster-get-started-portal.md

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title: 'Quickstart: Apache Hadoop, Apache Hive & Azure HDInsight portal'
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description: In this quickstart, you use the Azure portal to create a HDInsight Hadoop cluster
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description: In this quickstart, you use the Azure portal to create an HDInsight Hadoop cluster
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keywords: hadoop getting started,hadoop linux,hadoop quickstart,hive getting started,hive quickstart
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ms.service: azure-hdinsight
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ms.topic: quickstart
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In this article, you learn how to create Apache Hadoop clusters in HDInsight using Azure portal, and then run Apache Hive jobs in HDInsight. Most of Hadoop jobs are batch jobs. You create a cluster, run some jobs, and then delete the cluster. In this article, you perform all the three tasks. For in-depth explanations of available configurations, see [Set up clusters in HDInsight](../hdinsight-hadoop-provision-linux-clusters.md). For more information regarding the use of the portal to create clusters, see [Create clusters in the portal](../hdinsight-hadoop-create-linux-clusters-portal.md).
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In this quickstart, you use the Azure portal to create a HDInsight Hadoop cluster. You can also create a cluster using the [Azure Resource Manager template](apache-hadoop-linux-tutorial-get-started.md).
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In this quickstart, you use the Azure portal to create an HDInsight Hadoop cluster. You can also create a cluster using the [Azure Resource Manager template](apache-hadoop-linux-tutorial-get-started.md).
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Currently, HDInsight comes with [seven different cluster types](../hdinsight-overview.md#cluster-types-in-hdinsight). Each cluster type supports a different set of components. All cluster types support Hive. For a list of supported components in HDInsight, see [What's new in the Apache Hadoop cluster versions provided by HDInsight?](../hdinsight-component-versioning.md)
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## Clean up resources
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After you complete the quickstart, you may want to delete the cluster. With HDInsight, your data is stored in Azure Storage, so you can safely delete a cluster when it isn't in use. You're also charged for a HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use.
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After you complete the quickstart, you may want to delete the cluster. With HDInsight, your data is stored in Azure Storage, so you can safely delete a cluster when it isn't in use. You're also charged for an HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use.
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> [!NOTE]
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> If you are *immediately* proceeding to the next article to learn how to run ETL operations using Hadoop on HDInsight, you may want to keep the cluster running. This is because in the tutorial you have to create a Hadoop cluster again. However, if you are not going through the next article right away, you must delete the cluster now.

articles/hdinsight/hdinsight-hadoop-create-linux-clusters-azure-cli.md

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## Clean up resources
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After you complete the article, you may want to delete the cluster. With HDInsight, your data is stored in Azure Storage, so you can safely delete a cluster when it isn't in use. You're also charged for a HDInsight cluster, even when it's not in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use.
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After you complete the article, you may want to delete the cluster. With HDInsight, your data is stored in Azure Storage, so you can safely delete a cluster when it isn't in use. You're also charged for an HDInsight cluster, even when it's not in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use.
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Enter all or some of the following commands to remove resources:
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## Next steps
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Now that you've successfully created a HDInsight cluster using the Azure CLI, use the following to learn how to work with your cluster:
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Now that you've successfully created an HDInsight cluster using the Azure CLI, use the following to learn how to work with your cluster:
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### Apache Hadoop clusters
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articles/hdinsight/hdinsight-hadoop-create-linux-clusters-portal.md

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## Delete the cluster
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See [Delete a HDInsight cluster using your browser, PowerShell, or the Azure CLI](./hdinsight-delete-cluster.md).
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See [Delete an HDInsight cluster using your browser, PowerShell, or the Azure CLI](./hdinsight-delete-cluster.md).
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## Troubleshoot
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If you run into issues with creating HDInsight clusters, see [access control requirements](./hdinsight-hadoop-customize-cluster-linux.md#access-control).
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## Next steps
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You've successfully created a HDInsight cluster. Now learn how to work with your cluster.
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You've successfully created an HDInsight cluster. Now learn how to work with your cluster.
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* [Use Apache Hive with HDInsight](hadoop/hdinsight-use-hive.md)
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* [Get started with Apache HBase on HDInsight](hbase/apache-hbase-tutorial-get-started-linux.md)

articles/hdinsight/kafka/apache-kafka-quickstart-powershell.md

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For information on the number of fault domains in a region, see the [Availability of Linux virtual machines](/azure/virtual-machines/availability) document.
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Kafka is not aware of Azure fault domains. When yo create partition replicas for topics, it may not distribute replicas properly for high availability.
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Kafka is not aware of Azure fault domains. When you create partition replicas for topics, it may not distribute replicas properly for high availability.
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To ensure high availability, use the [Apache Kafka partition rebalance tool](https://github.com/hdinsight/hdinsight-kafka-tools). This tool must be ran from an SSH connection to the head node of your Kafka cluster.
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articles/hdinsight/spark/apache-spark-create-cluster-cli.md

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## Clean up resources
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After you complete the quickstart, you may want to delete the cluster. With HDInsight, your data is stored in Azure Storage, so you can safely delete a cluster when it isn't in use. You're also charged for a HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use.
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After you complete the quickstart, you may want to delete the cluster. With HDInsight, your data is stored in Azure Storage, so you can safely delete a cluster when it isn't in use. You're also charged for an HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use.
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## Next steps
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In this quickstart, you learned how to create an Apache Spark cluster in Azure HDInsight using Azure CLI. Advance to the next tutorial to learn how to use a HDInsight cluster to run interactive queries on sample data.
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In this quickstart, you learned how to create an Apache Spark cluster in Azure HDInsight using Azure CLI. Advance to the next tutorial to learn how to use an HDInsight cluster to run interactive queries on sample data.
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> [!div class="nextstepaction"]
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> [Run interactive queries on Apache Spark](./apache-spark-load-data-run-query.md)

articles/hdinsight/spark/apache-spark-jupyter-spark-sql-use-portal.md

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## Create an Apache Spark cluster in HDInsight
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You use the Azure portal to create a HDInsight cluster that uses Azure Storage Blobs as the cluster storage. For more information on using Data Lake Storage Gen2, see [Quickstart: Set up clusters in HDInsight](../hdinsight-hadoop-provision-linux-clusters.md).
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You use the Azure portal to create an HDInsight cluster that uses Azure Storage Blobs as the cluster storage. For more information on using Data Lake Storage Gen2, see [Quickstart: Set up clusters in HDInsight](../hdinsight-hadoop-provision-linux-clusters.md).
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1. Sign in to the [Azure portal](https://portal.azure.com/).
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## Clean up resources
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HDInsight saves your data in Azure Storage or Azure Data Lake Storage, so you can safely delete a cluster when it isn't in use. You're also charged for a HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use. If you plan to work on the tutorial listed in [Next steps](#next-steps) immediately, you might want to keep the cluster.
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HDInsight saves your data in Azure Storage or Azure Data Lake Storage, so you can safely delete a cluster when it isn't in use. You're also charged for an HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use. If you plan to work on the tutorial listed in [Next steps](#next-steps) immediately, you might want to keep the cluster.
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:::image type="content" source="./media/apache-spark-jupyter-spark-sql-use-portal/hdinsight-azure-portal-delete-cluster.png " alt-text="Azure portal delete a HDInsight cluster." border="true":::sight cluster" border="true":::
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:::image type="content" source="./media/apache-spark-jupyter-spark-sql-use-portal/hdinsight-azure-portal-delete-cluster.png " alt-text="Azure portal delete an HDInsight cluster." border="true":::sight cluster" border="true":::
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You can also select the resource group name to open the resource group page, and then select **Delete resource group**. By deleting the resource group, you delete both the HDInsight cluster, and the default storage account.
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## Next steps
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In this quickstart, you learned how to create an Apache Spark cluster in HDInsight and run a basic Spark SQL query. Advance to the next tutorial to learn how to use a HDInsight cluster to run interactive queries on sample data.
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In this quickstart, you learned how to create an Apache Spark cluster in HDInsight and run a basic Spark SQL query. Advance to the next tutorial to learn how to use an HDInsight cluster to run interactive queries on sample data.
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> [!div class="nextstepaction"]
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> [Run interactive queries on Apache Spark](./apache-spark-load-data-run-query.md)

articles/hdinsight/spark/apache-spark-jupyter-spark-sql-use-powershell.md

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> [!IMPORTANT]
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Creating an HDInsight cluster includes creating the following Azure objects and resources:
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HDInsight saves your data in Azure Storage or Azure Data Lake Storage, so you can safely delete a cluster when it isn't in use. You're also charged for a HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use. If you plan to work on the tutorial listed in [Next steps](#next-steps) immediately, you might want to keep the cluster.
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HDInsight saves your data in Azure Storage or Azure Data Lake Storage, so you can safely delete a cluster when it isn't in use. You're also charged for an HDInsight cluster, even when it isn't in use. Since the charges for the cluster are many times more than the charges for storage, it makes economic sense to delete clusters when they aren't in use. If you plan to work on the tutorial listed in [Next steps](#next-steps) immediately, you might want to keep the cluster.
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:::image type="content" source="./media/apache-spark-jupyter-spark-sql-use-powershell/hdinsight-azure-portal-delete-cluster.png " alt-text="Azure portal delete a HDInsight cluster." border="true":::
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:::image type="content" source="./media/apache-spark-jupyter-spark-sql-use-powershell/hdinsight-azure-portal-delete-cluster.png " alt-text="Azure portal delete an HDInsight cluster." border="true":::
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In this quickstart, you learned how to create an Apache Spark cluster in HDInsight and run a basic Spark SQL query. Advance to the next tutorial to learn how to use a HDInsight cluster to run interactive queries on sample data.
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In this quickstart, you learned how to create an Apache Spark cluster in HDInsight and run a basic Spark SQL query. Advance to the next tutorial to learn how to use an HDInsight cluster to run interactive queries on sample data.
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> [!div class="nextstepaction"]
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> [Run interactive queries on Apache Spark](./apache-spark-load-data-run-query.md)

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