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articles/app-service/tutorial-dotnetcore-sqldb-app.md

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1. Select **Delete**.
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:::image type="content" source="./media/tutorial-dotnetcore-sqldb-app/azure-portal-clean-up-resources-3.png" alt-text="A screenshot of the confirmation dialog for deleting a resource group in the Azure portal." lightbox="./media/tutorial-dotnetcore-sqldb-app/azure-portal-clean-up-resources-3.png":::
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articles/app-service/tutorial-nodejs-mongodb-app.md

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1. Select **Delete**.
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:::image type="content" source="./media/tutorial-nodejs-mongodb-app/azure-portal-clean-up-resources-3.png" alt-text="A screenshot of the confirmation dialog for deleting a resource group in the Azure portal." lightbox="./media/tutorial-nodejs-mongodb-app/azure-portal-clean-up-resources-3.png":::
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articles/app-service/tutorial-php-mysql-app.md

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:::image type="content" source="./media/tutorial-php-mysql-app/azure-portal-clean-up-resources-3.png" alt-text="A screenshot of the confirmation dialog for deleting a resource group in the Azure portal." lightbox="./media/tutorial-php-mysql-app/azure-portal-clean-up-resources-3.png":::
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articles/app-service/tutorial-python-postgresql-app-django.md

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:::image type="content" source="./media/tutorial-python-postgresql-app-django/azure-portal-clean-up-resources-3.png" alt-text="A screenshot of the confirmation dialog for deleting a resource group in the Azure portal." lightbox="./media/tutorial-python-postgresql-app-django/azure-portal-clean-up-resources-3.png":::
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articles/app-service/tutorial-python-postgresql-app-fastapi.md

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:::image type="content" source="./media/tutorial-python-postgresql-app-fastapi/azure-portal-clean-up-resources-3.png" alt-text="A screenshot of the confirmation dialog for deleting a resource group in the Azure portal." lightbox="./media/tutorial-python-postgresql-app-fastapi/azure-portal-clean-up-resources-3.png":::
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articles/app-service/tutorial-python-postgresql-app-flask.md

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articles/azure-cache-for-redis/cache-tutorial-functions-getting-started.md

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<!-- Fran, need new screenshot here -->
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1. Go to your cache in the Azure portal, and locate **Access keys** on the Resource menu. Write down or copy the contents of the **Primary** box. You'll use this for creating your connection string.

articles/data-factory/connector-azure-file-storage.md

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| fileName | **Name or wildcard filter** for the file(s) under the specified "folderPath". If you don't specify a value for this property, the dataset points to all files in the folder. <br/><br/>For filter, allowed wildcards are: `*` (matches zero or more characters) and `?` (matches zero or single character).<br/>- Example 1: `"fileName": "*.csv"`<br/>- Example 2: `"fileName": "???20180427.txt"`<br/>Use `^` to escape if your actual file name has wildcard or this escape char inside.<br/><br/>When fileName isn't specified for an output dataset and **preserveHierarchy** isn't specified in the activity sink, the copy activity automatically generates the file name with the following pattern: "*Data.[activity run ID GUID].[GUID if FlattenHierarchy].[format if configured].[compression if configured]*", for example "Data.0a405f8a-93ff-4c6f-b3be-f69616f1df7a.txt.gz"; if you copy from tabular source using table name instead of query, the name pattern is "*[table name].[format].[compression if configured]*", for example "MyTable.csv". |No |
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| modifiedDatetimeStart | Files filter based on the attribute: Last Modified. The files will be selected if their last modified time is greater than or equal to `modifiedDatetimeStart` and less than `modifiedDatetimeEnd`. The time is applied to UTC time zone in the format of "2018-12-01T05:00:00Z". <br/><br/> Be aware the overall performance of data movement will be impacted by enabling this setting when you want to do file filter from huge amounts of files. <br/><br/> The properties can be NULL that means no file attribute filter will be applied to the dataset. When `modifiedDatetimeStart` has datetime value but `modifiedDatetimeEnd` is NULL, it means the files whose last modified attribute is greater than or equal with the datetime value will be selected. When `modifiedDatetimeEnd` has datetime value but `modifiedDatetimeStart` is NULL, it means the files whose last modified attribute is less than the datetime value will be selected.| No |
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| modifiedDatetimeEnd | Files filter based on the attribute: Last Modified. The files will be selected if their last modified time is greater than or equal to `modifiedDatetimeStart` and less than `modifiedDatetimeEnd`. The time is applied to UTC time zone in the format of "2018-12-01T05:00:00Z". <br/><br/> Be aware the overall performance of data movement will be impacted by enabling this setting when you want to do file filter from huge amounts of files. <br/><br/> The properties can be NULL that means no file attribute filter will be applied to the dataset. When `modifiedDatetimeStart` has datetime value but `modifiedDatetimeEnd` is NULL, it means the files whose last modified attribute is greater than or equal with the datetime value will be selected. When `modifiedDatetimeEnd` has datetime value but `modifiedDatetimeStart` is NULL, it means the files whose last modified attribute is less than the datetime value will be selected.| No |
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| format | If you want to **copy files as-is** between file-based stores (binary copy), skip the format section in both input and output dataset definitions.<br/><br/>If you want to parse or generate files with a specific format, the following file format types are supported: **TextFormat**, **JsonFormat**, **AvroFormat**, **OrcFormat**, **ParquetFormat**. Set the **type** property under format to one of these values. For more information, see [Text Format](supported-file-formats-and-compression-codecs-legacy.md#text-format), [Json Format](supported-file-formats-and-compression-codecs-legacy.md#json-format), [Avro Format](supported-file-formats-and-compression-codecs-legacy.md#avro-format), [Orc Format](supported-file-formats-and-compression-codecs-legacy.md#orc-format), and [Parquet Format](supported-file-formats-and-compression-codecs-legacy.md#parquet-format) sections. |No (only for binary copy scenario) |
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| format | If you want to **copy files as-is** between file-based stores (binary copy), skip the format section in both input and output dataset definitions.<br/><br/>If you want to parse or generate files with a specific format, the following file format types are supported: **TextFormat**, **JsonFormat**, **AvroFormat**, **OrcFormat**, **ParquetFormat**. Set the **type** property under format to one of these values. For more information, see [Text Format](supported-file-formats-and-compression-codecs-legacy.md#text-format), [JSON Format](supported-file-formats-and-compression-codecs-legacy.md#json-format), [Avro Format](supported-file-formats-and-compression-codecs-legacy.md#avro-format), [Orc Format](supported-file-formats-and-compression-codecs-legacy.md#orc-format), and [Parquet Format](supported-file-formats-and-compression-codecs-legacy.md#parquet-format) sections. |No (only for binary copy scenario) |
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| compression | Specify the type and level of compression for the data. For more information, see [Supported file formats and compression codecs](supported-file-formats-and-compression-codecs-legacy.md#compression-support).<br/>Supported types are: **GZip**, **Deflate**, **BZip2**, and **ZipDeflate**.<br/>Supported levels are: **Optimal** and **Fastest**. |No |
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>[!TIP]

articles/hdinsight/hadoop/apache-hadoop-on-premises-migration-motivation.md

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|Programming Languages|Python, Scala, Java||
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|Scripting|Shell, Python||
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|**Topic**: **Data**|||
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|Data sources|Flat files, Json, Kafka, RDBMS||
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|Data sources|Flat files, JSON, Kafka, RDBMS||
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|Data orchestration|Oozie workflows, Airflow||
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|In memory lookups|Apache Ignite, Redis||
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|Data destinations|HDFS, RDBMS, Kafka, MPP ||

articles/site-recovery/hyper-v-deployment-planner-overview.md

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## Download and extract the deployment planner tool
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1. Download the latest version of the [Azure Site Recovery deployment planner](https://aka.ms/asr-deployment-planner).
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1. Download the latest version of the [Azure Site Recovery deployment planner](https://download.microsoft.com/download/7491a137-315d-40ca-9307-f74f913f7e6a/ASRDeploymentPlanner-v3.2.zip).
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The tool is packaged in a .zip folder. The same tool supports both VMware to Azure and Hyper-V to Azure disaster recovery scenarios. You can use this tool for Hyper-V-to secondary site disaster recovery scenario as well but ignore the Azure infrastructure recommendation from the report.
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1. Copy the .zip folder to the Windows Server on which you want to run the tool. You can run the tool on a Windows Server 2012 R2 or Windows Server 2016. The server must have network access to connect to the Hyper-V cluster or Hyper-V host that holds the VMs to be profiled. We recommend that you have the same hardware configuration of the VM, where the tool is going to run, as that of the Hyper-V server, which you want to protect. Such a configuration ensures that the achieved throughput that the tool reports matches the actual throughput that Azure Site Recovery can achieve during replication. The throughput calculation depends on available network bandwidth on the server and hardware configuration (CPU, storage, and so forth) of the server. The throughput is calculated from the server where the tool is running to Azure. If the hardware configuration of the server differs from the Hyper-V server, the achieved throughput that the tool reports will be inaccurate.

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