Guest post by Tiffany Wissner, Senior Director, Data Platform Yesterday at Microsoft’s Ignite conference, we demoed the first sneak peek of Azure SQL Data Warehouse.
Guest post by Kasper de Jonge, Senior PM Microsoft It’s that exciting time of the year again, spring is in the air and conference season is starting up again. Usually one of the first conferences that I am really excited about is the PASS Business Analytics Conference.
Interested in growing your BI and Big Data skills in 2015? Maybe your new year’s resolution is all about learning something new or taking your analytics knowledge to the next level?
This blog post was authored by: Matt Usher, Senior PM on the Microsoft Analytics Platform System (APS) team Microsoft is happy to announce the release of the Analytics Platform System (APS) Appliance Update (AU) 3. APS is Microsoft’s big data in a box appliance for serving the needs of relational data warehouses at massive scale.
Have you been watching Data Exposed over on Channel 9? If you’re a data developer, Data Exposed is a great place to learn more about what you can do with data: relational and non-relational, on-premises and in the cloud, big and small.
by Rob Farley, LobsterPot Solutions The Analytics Platform System, with its MPP SQL Server engine (SQL Server Parallel Data Warehouse) can deliver performance and scalability for analytics workloads that you may not have expected from SQL Server.
This blog post was authored by: Murshed Zaman, AzureCAT PM and Sumin Mohanan, DS SDET With the advent of SQL Server Parallel Data Warehouse (the MPP version of SQL Server) V2 AU1 (Appliance Update 1), PDW got a new name: the Analytics Platform System [Appliance] or APS.
Earlier today, Microsoft hosted a customer event in San Francisco where I joined CEO Satya Nadella and COO Kevin Turner to share our perspective on the role of data in business. Satya outlined his vision of a platform built for an era of ambient intelligence.
If your business relies on data, you know that it is a constant challenge to store, manage, and analyze it effectively as your data continues to grow. It’s also expensive to keep enough data on “hot” storage where it is readily available for analysis.
