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.
In this session, we are going to explain and test different DW features in SQL Server 2012, including star join optimization through bitmap filters, table partitioning, window functions, columnstore indices and more.
Sometimes some piece of T-SQL slips by, or falls out of memory.Come and revisit old favorites, and brush up on new T-SQL features and enhancements.This session is chock full of code examples, including before-and-after demos and how-to illustrations.
With a myriad of options available, choosing the most appropriate storage solution for your company can be challenging. This session will give you a brief introduction to the technologies available, and what to focus on when making the decision.
The SQL Server provides much functionality that increases the scalability and flexibility of your solution by distributing data and jobs among low-priced commodity servers.
The technique of Recency Frequency Intensity/Monetary is a powerful analytical technique for identifying data patterns as well as business performance. An introduction to the technique will be given, however the main focus of the session will be on demonstrating on how RFI/M can be performed using a number of SQL features such as Data Windowing, the OVER clause and PARTITION BY, CROSS APPLY and Common Table Expressions and how you can nest the table expressions. The session should be of benefit to both inexperienced and experienced SQL coders and analysts, each construct will be explained as well as the query plans produced. Demo's will be done on AdventureWorks which we actually discover is going out of business!
SQL Azure is Microsoft’s new strategy for storing your data in the cloud, but what to do when you exceed the 10/50GB limit. This is where sharding or partitioning comes into play – this session shows you how it can be done in an OLTP system and show you some of the common pitfalls of SQL azure as we have discoverd in analyzing SQL Azure as an alternative to onsite SQL Server instances at different clients.
Loading Partitions directly Consuming a package in SSRS Using a DM algorithm in the pipeline to see if the new customer meets criteria Fuzzy Grouping for deduplicating