Azure Data Lake Store

Official Documentation

Service Description

Azure Data Lake Store is an enterprise-wide hyper-scale repository for big data analytic workloads. Azure Data Lake enables you to capture data of any size, type, and ingestion speed in one single place for operational and exploratory analytics.

Azure Data Lake Store can be accessed from Hadoop (available with HDInsight cluster) using the WebHDFS-compatible REST APIs. It is specifically designed to enable analytics on the stored data and is tuned for performance for data analytics scenarios. Out of the box, it includes all the enterprise-grade capabilities—security, manageability, scalability, reliability, and availability—essential for real-world enterprise use cases.

Getting Started

  1. 10/3/2016, Webpage
    Data Lake Store is a hyper-scale repository for big data analytic workloads that stores every type of data regardless of its size, structure, or how fast it is ingested. This...
  2. 11/21/2016, Mva
    Wondering how Azure Data Lake enables developer productivity? Get the details in this course, which explores the sophisticated tooling and language design in Azure Data Lake....
  3. 8/6/2017, Mva
    Whether you’re brand new to Azure Data Lake or already developing on the system, don’t miss this lively and helpful course hosted by expert Nishant Thacker, who promises tons...
  4. 5/23/2017, Blog
    Authors: Sean Mikha and Stephen Wu Hadoop has always been about bringing the compute closer to where the data is stored. This is achieved by utilizing local disk and attached...
  5. 6/24/2016, Video, 0:17:33
    Looking to rethink your data storage? Click: With no slides and all demo, which we REALLY like, Amit jumps right in and first provides some background...

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Web Content

Azure Data Lake Store Documentation

1. Data Lake Storage Gen1 Documentation
2. Switch to Data Lake Storage Gen2 (preview) documentation
3. Overview
     3.1. Overview of Data Lake Storage Gen1
     3.2. Compare with Azure Storage
     3.3. Processing big data
     3.4. Working with open source applications
     3.5. Best practices
4. Get started
     4.1. Using Azure Portal
     4.2. Using Azure PowerShell
     4.3. Using Azure CLI
5. How to
     5.1. Load and move data
          5.1.1. Using Azure Data Factory
          5.1.2. Using Storage Explorer
          5.1.3. Using AdlCopy
          5.1.4. Using DistCp
          5.1.5. Using Sqoop
          5.1.6. Upload data from offline sources
          5.1.7. Migrate Data Lake Storage Gen1 across regions
     5.2. Secure data
          5.2.1. Security overview
          5.2.2. Access control
          5.2.3. Securing stored data
          5.2.4. Encryption
          5.2.5. Virtual network integration (preview)
     5.3. Authenticate with Data Lake Storage Gen1
          5.3.1. Authentication options
          5.3.2. End-user authentication
      Using Java
      Using .NET SDK
      Using REST API
      Using Python
          5.3.3. Service-to-service authentication
      Using Java
      Using .NET SDK
      Using REST API
      Using Python
     5.4. Work with Data Lake Storage Gen1
          5.4.1. Account management operations
      Using .NET SDK
      Using REST API
      Using Python
          5.4.2. Filesystem operations
      Using .NET SDK
      Using Java SDK
      Using REST API
      Using Python
     5.5. Performance
          5.5.1. Overview
          5.5.2. Using Azure PowerShell
          5.5.3. Using Spark on HDInsight
          5.5.4. Using Hive on HDInsight
          5.5.5. Using MapReduce on HDInsight
          5.5.6. Using Storm on HDInsight
     5.6. Integrate with Azure Services
          5.6.1. With HDInsight
      Using Azure portal
      Using Azure PowerShell (default storage)
      Using Azure PowerShell (additional storage)
      Using Azure template
          5.6.2. Access from VMs in Azure VNET
          5.6.3. Use with Data Lake Analytics
          5.6.4. Use with Azure Event Hubs
          5.6.5. Use with Data Factory
          5.6.6. Use with Stream Analytics
          5.6.7. Use with Power BI
          5.6.8. Use with Data Catalog
          5.6.9. Use with PolyBase in SQL Data Warehouse
          5.6.10. Use with SQL Server Integration Services
          5.6.11. More Azure integration options
     5.7. Manage
          5.7.1. Access diagnostic logs
          5.7.2. Plan for high availability
6. Reference
     6.1. Code samples
     6.2. Azure PowerShell
     6.3. .NET
     6.4. Java
     6.5. Node.js
     6.6. Python (Account Mgmt.)
     6.7. Python (Filesystem Mgmt.)
     6.8. REST
     6.9. Resource Manager template
     6.10. Azure CLI
7. Resources
     7.1. Azure Roadmap
     7.2. Data Lake Store Blog
     7.3. Give feedback on UserVoice
     7.4. MSDN Forum
     7.5. Pricing
     7.6. Pricing calculator
     7.7. Stack Overflow Forum
     7.8. Videos

Web Pages

Content Type
Azure Data Lake Store Learning Path Webpage

Online Training Content

Date Title
8/6/2017 Introducing Azure Data Lake
5/24/2017 Processing Big Data with Azure Data Lake Analytics
11/21/2016 Introducing Azure Data Lake


Tool Description
Azure Data Lake Store PowerShell Toolkit Working with the Azure Data Lake Store can sometimes be difficult, especially when performing actions on several items. PowerShell can be used to perform various tasks. This toolkit contains several scripts, which makes automation in the Data Lake a little easier
Azure Data Lake Tools for Visual Studio Azure Data Lake Tools for Visual Studio
AdlCopy Tool zum Kopieren von Daten zwischen Azure Blob Storage und Azure Data Lake Store


Date Title Length
ISV Showcase: End-to-end Machine Learning using H2O on Azure
ISV Showcase: End-to-end Machine Learning using H2O on Azure : Build 2018
Connecting On-premises Hadoop to Azure Data Lake Store
Demystifying Cloud Data Services for an App Developer
Loading Data into Azure SQL DW using Polybase
Cloud Tech 10 - 20th March 2017
Deep Dive of SSIS 2016 + vNext
Azure Data Lake GA!
Build your fully managed, petabyte-scale, secure data store with Azure Data Lake Store
Azure Data Lake: PowerShell, CLI, SDKs, and APIs

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