We all know that correct indexing is king when it comes to achieving high levels of performance in SQL Server. When indexing combines with the enterprise features partitioning and compression, you can find substantial performance gains.
Data warehouse designers often ignore the specific needs of an OLAP database. In this session, John will outline the best ways to optimise your relational database to support your multidimensional OLAP cubes
This session will show you how the query optimizer has been updated to work with the new SQL Server 2014 features and to provide better performance to existing ones. Topics include Hekaton, the new cardinality estimator and incremental statistics.
This session will take a look at how parallel select into can be scaled to the nth degree in SQL Server such that all available hardware resources are utilised as fully as possible.
In this humorous session I’ll be contesting many of the so called "best practices" in SQL Server and demonstrating counter arguments. Come along to see how so called "pillars" of design are starting to break down.
We all know that ‘Indexing’ is KING when it comes to achieving high levels of performance in SQL Server. When Indexing also combines 2 of the Enterprise features: Partitioning & Compression
This session will discuss the recommended approaches and best practices for partitioning and scaling Windows Azure SQL Database, allowing you to fully leverage the managed relational database service and take advantage of massive scale-out scenarios.
Processing of SSAS OLAP databases can be a tricky business, particularly when it comes to incremental processing of dimensions. John will give you real life examples of why certain approaches work and others do not.
“Just use partitioning” is the answer you hear, when you need to manage very large data sets in your Data Warehouse. But how do you design and implement it? We will walk through different ways to design partitioning, including layered partitioning.
An introduction to scaling out packages using parallelism with the "Work pile" pattern, balanced data distributor and "Roll your own" techniques.