SQL Server 2016 is enterprise ready for transaction processing, data warehousing, and business intelligence.
As part of SQL Server 2016, R Services expands the scope of these existing data technologies to include advanced analytics in enterprise workflows allowing for the building of intelligent, predictive applications by leveraging the R language and in-database analytics.
Azure SQL Data Warehouse is a fully managed, elastic data warehouse-as-a-service for processing enterprise workloads.
The process of loading data into a traditional data warehouse typically follows the pattern of Extracting, Transforming, and Loading (ETL). Most businesses use a dedicated server to perform these operations.
However, with MPP architecture and the ability to load data fast, the new standard is becoming Extract, Load, and Transform or ELT.
We all want to make good business decisions. They are crucial to the success of our enterprises. The quality of our decisions stem from two factors:
1. Sound decision processes and skills.
This is why we get MBAs and continue to learn and study all our lives.
2. Good (true) knowledge (data) of the environment and situations which command our attention.
If you make a sound decision based on incorrect or the wrong data, you will get a bad decision and possibly a bad outcome.
When a software package has been around as long as Microsoft Windows™, Office™, or SQL Server™ (all dating to circa 1990), it seems to become harder and harder to add significant new features to such a mature product. Other than pivot and the ribbon, Office™ is virtually unchanged from the early days. Windows™ has taken more risk yielding successes as well as flops like Millennium™ and Vista™. But SQL Server™ just seems always to get better and better while maintaining continuity. The upcoming 2016 release (June 2016) is no exception.
