SQL Data Warehouse

Official Documentation

Service Description

With SQL Data Warehouse, Data Warehouse solutions can be set up flexibly and their resources can be adapted to the specific data evaluation requirements. Compute and storage services can be scaled independently of one another, allowing the compute resources to be extended, reduced, or suspended while retaining the storage systems (for relational data or non-relational Blob storage).

Getting Started

  1. 9/28/2016, Video, 1:03:40
    Learn how SQL Data Warehouse can help you succeed with a complex hybrid architecture or deployment in Microsoft Azure. Deep views on integration with Azure services such as...
  2. 9/26/2017, Video, 1:14:36
    Did you know that *almost everything* tastes better with pesto? That’s especially true when you are dining on data with a high-carb, Petabyte scale, cloud data warehouse...
  3. 9/28/2017, Video, 1:12:13
    Microsoft Azure provides a broad range of services for working with data. Using these services well requires knowing at least a little bit about all of them. In this session,...



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Web Content

SQL Data Warehouse Documentation

1. Overview
     1.1. About SQL Data Warehouse
     1.2. Cheat sheet
2. Release Notes
     2.1. August 2018
     2.2. July 2018
     2.3. June 2018
     2.4. May 2018
     2.5. April 2018
3. Quickstarts
     3.1. Create and connect
          3.1.1. Portal
          3.1.2. PowerShell
     3.2. Pause and resume compute
          3.2.1. Portal
          3.2.2. PowerShell
     3.3. Scale compute
          3.3.1. Portal
          3.3.2. PowerShell
          3.3.3. T-SQL
4. Concepts
     4.1. Security
          4.1.1. Overview
          4.1.2. Access control
          4.1.3. Firewall rules
          4.1.4. Firewall virtual network rules
          4.1.5. Authentication
          4.1.6. Azure AD
          4.1.7. Logins and users
          4.1.8. Multi-factor auth
          4.1.9. Auditing
          4.1.10. Column-level security
     4.2. Data loading
          4.2.1. Overview
          4.2.2. Best practices
          4.2.3. Columnstore compression
     4.3. Development
          4.3.1. Overview
          4.3.2. Data warehouse components
          4.3.3. Best practices
          4.3.4. MPP architecture
          4.3.5. Data warehouse units
          4.3.6. Capacity limits
          4.3.7. FAQ
          4.3.8. Tables
               4.3.8.1. Overview
               4.3.8.2. CTAS
               4.3.8.3. Data types
               4.3.8.4. Distributed tables
               4.3.8.5. Indexes
               4.3.8.6. Identity
               4.3.8.7. Partitions
               4.3.8.8. Replicated tables
               4.3.8.9. Statistics
               4.3.8.10. Temporary
          4.3.9. Queries
               4.3.9.1. Dynamic SQL
               4.3.9.2. Group by options
               4.3.9.3. Labels
          4.3.10. T-SQL language elements
               4.3.10.1. Loops
               4.3.10.2. Stored procedures
               4.3.10.3. Transactions
               4.3.10.4. Transactions Best Practices
               4.3.10.5. User-defined schemas
               4.3.10.6. Variable assignment
               4.3.10.7. Views
     4.4. Workload Management
          4.4.1. Resource classes & workload management
          4.4.2. Memory & concurrency limits
     4.5. Manageability & Monitoring
          4.5.1. Query Activity & Resource Utilization
          4.5.2. Data protection
          4.5.3. Scale-out, pause, resume
          4.5.4. Maintenance schedule
          4.5.5. Recommendations
          4.5.6. Troubleshoot
     4.6. Integration
          4.6.1. Overview
          4.6.2. SQL Database elastic query
5. How-to guides
     5.1. Secure
          5.1.1. Configure Azure AD auth
          5.1.2. Conditional access
          5.1.3. Virtual network rules by PowerShell
          5.1.4. Enable encryption - portal
          5.1.5. Enable encryption - T-SQL
          5.1.6. Threat detection
     5.2. Load Data
          5.2.1. New York taxi cab data
          5.2.2. Contoso public data
          5.2.3. Azure Data Lake Store
          5.2.4. Azure Databricks
          5.2.5. Data Factory
          5.2.6. SSIS
          5.2.7. Load WideWorldImporters
     5.3. Develop
          5.3.1. Overview
          5.3.2. Connection strings
          5.3.3. sqlcmd
          5.3.4. Query with SSMS
          5.3.5. Query with Visual Studio
          5.3.6. Install Visual studio
     5.4. Tune Workload
          5.4.1. Analyze your workload
     5.5. Manage and Monitor
          5.5.1. Restore - Portal
          5.5.2. Restore - PowerShell
          5.5.3. Restore - REST API
          5.5.4. Upgrade to Gen2
          5.5.5. Automate performance levels
     5.6. Integrate
          5.6.1. Configure SQL Database elastic query
          5.6.2. Add an Azure Stream Analytics job
          5.6.3. Use machine learning
          5.6.4. Visualize with Power BI
6. Reference
     6.1. T-SQL
          6.1.1. Full reference
          6.1.2. SQL DW language elements
          6.1.3. SQL DW statements
     6.2. System views
     6.3. PowerShell cmdlets
     6.4. REST APIs
7. Resources
     7.1. Azure Roadmap
     7.2. Forum
     7.3. Pricing
     7.4. Pricing calculator
     7.5. Feature requests
     7.6. Service updates
     7.7. Stack Overflow
     7.8. Support
     7.9. Videos
     7.10. Partners
          7.10.1. Business intelligence
          7.10.2. Data integration
          7.10.3. Data management

Web Pages

Content Type
Build 2016 Workshops: Azure Data Services Lab

Online Training Content

Date Title
5/24/2017 Delivering a Data Warehouse in the Cloud
12/11/2015 Deliver an Elastic Data Warehouse as a Service

Tools

Tool Description
Scale Azure SQL Data Warehouse Scale Azure SQL Data WarehouseThis is a simple runbook that will allow you to scale your Azure SQL Data Warehouse.Depending on your goals with Azure SQL Data Warehouse, at time it is important to scale up and down depending the incoming workload or amount of data. In Automation,
Resume Azure SQL Data Warehouse Resume Azure SQL Data WarehouseThis is a simple runbook that will allow you to resume a Azure SQL Data Warehouse.Resuming as part of a ETL/ELT Data WorkflowWe commonly get asked if there is some way that an Azure SQL Data Warehouse can be resumed on a schedule. Using this, you ca

Videos

Date Title Length
5/6/2018
ETL 2.0 - Data Engineering for developers
1:33:22
5/6/2018
Develop scalable analytical solutions with Azure Data Factory & Azure SQL Data Warehouse
1:33:22
5/3/2018
mssql-cli, an open source and cross-platform CLI for SQL Server
0:16:03
4/19/2018
Moving your Data Warehouse to the Cloud
0:20:26
3/5/2018
Introduction to Azure SQL Data Warehouse
0:01:14
1/8/2018
Azure SQL Data Warehouse compute optimized performance tier
0:08:58
11/26/2017
Cloud Tech 10 - 27th November 2017 - Jenkins with ACI, SQL DW with Functions and more!
0:08:45
11/17/2017
Optimize for compute: New Azure SQL Data Warehouse performance tier | T140
0:08:12
11/17/2017
Use Azure Databricks Notebooks for high-performance data pipelines | T165
0:09:38
11/15/2017
Use Azure Databricks Notebooks for high-performance data pipelines
0:09:37

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