Pluralsight – Optimizing OLTP and Data Warehousing with SQL Server 2014

Pluralsight - Optimizing OLTP and Data Warehousing with SQL Server 2014

Title: Pluralsight – Optimizing OLTP and Data Warehousing with SQL Server 2014
Video Format: MP4
File Size: 357 MB
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This course focuses on the database developers and architects engaged in the increasingly difficult task of achieving the best performance of database applications. SQL Server 2014 addresses these needs, optimizing for both OLTP and Data Warehousing workloads using In-Memory OLTP and Clustered Columnstore Indexes, allowing you to realize the fullest potential of your database systems and underlying hardware.
This course focuses on SQL Server 2014 and two of the latest additions to the database engine. We first look at how the shift in technology trends established the need for a different breed of technologies. Columnar, or Column-Oriented, databases have become increasingly popular in data warehousing applications because they allow for better compression and multi-fold performance improvements, and we’ll see how SQL Server 2014 implements this with clustered columnstore indexes. In addition, as technology makes a bigger shift in hardware to multi-core CPU architectures and large amounts of memory are even cheaper and more attainable, it’s important for software to adjust and utilize these changes. We’ll see how In-Memory OLTP addresses these trends and more, letting you create tables in memory, eliminating locks and implementing optimistic multi-version concurrency. This course discusses in depth In-Memory OLTP (memory optimized tables), previously known as Hekaton. We’ll cover creating memory-optimized tables, loading data, durability and the affect that has on the transaction log. We’ll also look at indexing in detail, explaining the new index types created specifically to support In-Memory OLTP and how and when to apply each type based on your use case and we’ll discuss how In-Memory OLTP and Clustered Columnstore indexes can fit a variety of applications while comparing them with each other and traditional disk-based tables and indexes.

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