Columnar storage brings a completely new set of possibilities in SQL Server. You can get lightning performance of analytical queries right from your data warehouse, without a special analytical database management system. This chapter started by describing features that support analytical queries in SQL Server other that columnar storage. You can use row or page data compression levels, bitmap filtered hash joins, filtered indexes, indexed views, window analytical and aggregate functions, table partitioning, and more. However, columnar storage adds an additional level of compression and performance boost. You learned about the algorithms behind the fantastic compression with columnar storage. This chapter also includes a lot of code, showing you how to create and use the nonclustered and the clustered columnstore indexes, including updating the data, creating constraints, and adding additional B-tree nonclustered indexes.

In the next two chapters, you are going to learn about a completely different way of improving the performance of your databases: memory-optimized tables. In addition, this chapter only started with analytics in SQL Server; the last two chapters introduce R, a whole analytical language supported by SQL Server 2016.