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Merge pull request #107341 from rolyon/rolyon-landing-data-lake-store
[Data Lake Storage Gen1] landing page update
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‎articles/azure-databricks/what-is-azure-databricks.md

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![What is Azure Databricks?](./media/what-is-azure-databricks/azure-databricks-overview.png "What is Azure Databricks?")
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Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics service. For a big data pipeline, the data (raw or structured) is ingested into Azure through Azure Data Factory in batches, or streamed near real-time using Kafka, Event Hub, or IoT Hub. This data lands in a data lake for long term persisted storage, in Azure Blob Storage or Azure Data Lake Storage. As part of your analytics workflow, use Azure Databricks to read data from multiple data sources such as [Azure Blob Storage](../storage/blobs/storage-blobs-introduction.md), [Azure Data Lake Storage](../data-lake-store/index.md), [Azure Cosmos DB](../cosmos-db/index.yml), or [Azure SQL Data Warehouse](../sql-data-warehouse/index.yml) and turn it into breakthrough insights using Spark.
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Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics service. For a big data pipeline, the data (raw or structured) is ingested into Azure through Azure Data Factory in batches, or streamed near real-time using Kafka, Event Hub, or IoT Hub. This data lands in a data lake for long term persisted storage, in Azure Blob Storage or Azure Data Lake Storage. As part of your analytics workflow, use Azure Databricks to read data from multiple data sources such as [Azure Blob Storage](../storage/blobs/storage-blobs-introduction.md), [Azure Data Lake Storage](../data-lake-store/index.yml), [Azure Cosmos DB](../cosmos-db/index.yml), or [Azure SQL Data Warehouse](../sql-data-warehouse/index.yml) and turn it into breakthrough insights using Spark.
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![Databricks pipeline](./media/what-is-azure-databricks/databricks-pipeline.png)
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‎articles/data-lake-store/TOC.yml

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- name: Data Lake Storage Gen1 Documentation
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href: index.md
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- name: Data Lake Storage Gen1 documentation
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href: index.yml
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- name: Switch to Data Lake Storage Gen2 documentation
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href: https://docs.microsoft.com/azure/storage/data-lake-storage/introduction
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- name: Overview

‎articles/data-lake-store/index.md

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‎articles/data-lake-store/index.yml

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### YamlMime:Landing
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title: Azure Data Lake Storage Gen1 documentation
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summary: Learn how to set up, manage, and access a hyper-scale, Hadoop-compatible data lake repository for analytics on data of any size, type, and ingestion speed. Azure Data Lake Storage Gen2 is generally available. We recommend that you start using it today.
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metadata:
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title: Azure Data Lake Storage Gen1 documentation
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description: Learn how to set up, manage, and access a hyper-scale, Hadoop-compatible data lake repository for analytics on data of any size, type, and ingestion speed.
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ms.service: data-lake-store
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ms.topic: landing-page
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author: twooley
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ms.author: twooley
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ms.date: 03/11/2020
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# linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | sample | tutorial | video | whats-new
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landingContent:
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# Card
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- title: About Azure Data Lake Storage Gen1
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linkLists:
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- linkListType: overview
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links:
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- text: What is Azure Data Lake Storage Gen1?
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url: data-lake-store-overview.md
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- text: Compare with Azure Storage
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url: data-lake-store-comparison-with-blob-storage.md
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- text: Switch to Data Lake Storage Gen2 documentation
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url: ../storage/data-lake-storage/introduction.md
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# Card
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- title: Get started
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linkLists:
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- linkListType: get-started
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links:
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- text: Azure portal
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url: data-lake-store-get-started-portal.md
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- text: Azure PowerShell
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url: data-lake-store-get-started-powershell.md
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- text: Azure CLI
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url: data-lake-store-get-started-cli-2.0.md
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# Card
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- title: Load and move data
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linkLists:
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- linkListType: how-to-guide
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links:
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- text: Using Azure Data Factory
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url: ../data-factory/load-azure-data-lake-store.md
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- text: Using Storage Explorer
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url: data-lake-store-in-storage-explorer.md
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- text: Using AdlCopy
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url: data-lake-store-copy-data-azure-storage-blob.md
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- text: Using DistCp
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url: data-lake-store-copy-data-wasb-distcp.md
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- text: Using Sqoop
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url: data-lake-store-data-transfer-sql-sqoop.md
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# Card
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- title: Secure data
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linkLists:
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- linkListType: overview
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links:
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- text: Security overview
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url: data-lake-store-security-overview.md
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- text: Access control
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url: data-lake-store-access-control.md
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- linkListType: how-to-guide
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links:
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- text: Secure stored data
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url: data-lake-store-secure-data.md
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- text: Encryption
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url: data-lake-store-encryption.md
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- text: Virtual network integration
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url: data-lake-store-network-security.md
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# Card
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- title: Authentication
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linkLists:
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- linkListType: overview
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links:
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- text: Authentication options
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url: data-lakes-store-authentication-using-azure-active-directory.md
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- linkListType: how-to-guide
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links:
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- text: End-user authentication
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url: data-lake-store-end-user-authenticate-using-active-directory.md
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- text: Service-to-service authentication
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url: data-lake-store-service-to-service-authenticate-using-active-directory.md
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# Card
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- title: Filesystem operations
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linkLists:
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- linkListType: how-to-guide
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links:
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- text: .NET SDK
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url: data-lake-store-data-operations-net-sdk.md
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- text: Java SDK
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url: data-lake-store-get-started-java-sdk.md
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- text: REST API
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url: data-lake-store-data-operations-rest-api.md
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- text: Python
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url: data-lake-store-data-operations-python.md
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# Card
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- title: Account management operations
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linkLists:
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- linkListType: how-to-guide
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links:
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- text: .NET SDK
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url: data-lake-store-get-started-net-sdk.md
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- text: REST API
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url: data-lake-store-get-started-rest-api.md
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- text: Python
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url: data-lake-store-get-started-python.md

‎articles/hdinsight/hadoop/apache-hadoop-on-premises-migration-best-practices-storage.md

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The following methods can be used to import certificates into the Java trust store:
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Download the Azure Blob ssl cert to a file
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Download the Azure Blob SSL cert to a file
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```bash
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echo -n | openssl s_client -connect <storage-account>.blob.core.windows.net:443 | sed -ne '/-BEGIN CERTIFICATE-/,/-END CERTIFICATE-/p' > Azure_Storage.cer
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Azure Data Lake Storage Gen2 is the latest storage offering. It unifies the core capabilities from the first generation of Azure Data Lake Storage with a Hadoop compatible file system endpoint directly integrated into Azure Blob Storage. This enhancement combines the scale and cost benefits of object storage with the reliability and performance typically associated only with on-premises file systems.
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ADLS Gen 2 is built on top of [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md) and allows you to interface with data using both file system and object storage paradigms. Features from [Azure Data Lake Storage Gen1](../../data-lake-store/index.md), such as file system semantics, file-level security, and scale are combined with low-cost, tiered storage, high availability/disaster recovery capabilities, and a large SDK/tooling ecosystem from [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md). In Data Lake Storage Gen2, all the qualities of object storage remain while adding the advantages of a file system interface optimized for analytics workloads.
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ADLS Gen 2 is built on top of [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md) and allows you to interface with data using both file system and object storage paradigms. Features from [Azure Data Lake Storage Gen1](../../data-lake-store/index.yml), such as file system semantics, file-level security, and scale are combined with low-cost, tiered storage, high availability/disaster recovery capabilities, and a large SDK/tooling ecosystem from [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md). In Data Lake Storage Gen2, all the qualities of object storage remain while adding the advantages of a file system interface optimized for analytics workloads.
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A fundamental feature of Data Lake Storage Gen2 is the addition of a [hierarchical namespace](../../storage/data-lake-storage/namespace.md) to the Blob storage service, which organizes objects/files into a hierarchy of directories for performant data access. The hierarchical structure enables operations such as renaming or deleting a directory to be single atomic metadata operations on the directory rather than enumerating and processing all objects that share the name prefix of the directory.
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