Azure SQL Data Warehouse is now GA
The Azure SQL Data Warehouse (SQL DW), that I blogged about here, is now generally available. Here is the official announcement.
In brief, SQL DW is a fully managed data-warehouse-as-a-service that you can provision in minutes and scale up in seconds. With SQL DW, storage and compute scale independently. You can dynamically deploy, grow, shrink, and even pause compute, allowing for cost savings. Also, SQL DW uses the power and familiarity of T-SQL so you can integrate query results across relational data in your data warehouse and non-relational data in Azure blob storage or Hadoop using PolyBase. SQL DW offers an availability SLA of 99.9%, the only public cloud data warehouse service that offers an availability SLA to customers.
SQL DW uses an elastic massively parallel processing (MPP) architecture built on top of the SQL Server 2016 database engine. It allows you to interactively query and analyze data using existing SQL-based tools and business intelligence applications. It uses column stores for high performance analytics and storage compression, a rich collection of aggregation capabilities of SQL Server, and state of the art query optimization capabilities.
Two customer case studies using SQL DW in production were just published: AGOOP and P:Cubed.
Also note that until recently, you had to use SSDT to connect to SQL DW. But with the July 2016 update of SSMS, you can now connect to SQL DW using SSMS (see Finally, SSMS will talk to Azure SQL DW).
More info:
Azure SQL Data Warehouse Hits General Availability
Introduction to Azure SQL Data Warehouse (video)
Microsoft Azure SQL Data Warehouse Overview (video)
Thanks James for this update. I believe the elastic MPP architecture plus the ability to scale storage and compute independently are major benefits for customers.
What are the plans for a Azure Data Center in Africa?
Africa has a very large customer base with sophisticated solutions but the bandwidth is a limitation for Azure uptake. My customers in the past have expressed lots of interest but the decision always boiled down to bandwidth costs and throughput.
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