This session goes beyond the classical star schema modeling, exploring new techniques to model data with Power Pivot and SSAS Tabular. You will see how brute-force power in DAX allows different data models than those used in SSAS Multidimensional
T4 templating will be a first class citizen in SSDT for SQL Server 2014. This session will show why you should use this technology for SQL code generation and how you can automate the process. The session will be demo rich.
A deep dive in the internals of the database architecture, discovering how Vertipaq stores information, in order to gain better insights into the engine and understand the best way to model your data warehouse to leverage the features of VertiPaq.
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.
Users love flexible analytics but hate to wait for the data to be loaded into a traditional data warehouse. John will describe how to build an infrastructure to support real-time loading of your OLAP cubes so your user's get exactly what they want
This session looks at some of the different methods available to load slowly changing dimension data into a data warehouse, and compares the relative performance given different data scenarios and traditional storage compared with FusionIO
Snapshots without snapshots...is that possible? Take a "Classic" snapshot fact table, add some temporal data theory and you'll get a new fact table than can store snapshot data without doing snapshots. A life saver when you have a lot of data.
Organizations risk being overwhelmed by data. How can you effectively provide a “single version of the truth”, while unlocking the key trends and insights that will allow your business to succeed? Come to this session to find out how.
Do you have complex dimensions in your data warehouse? Parent-child, late arriving, type 3 or type 6? In this session, we'll cover some SSIS patterns for handling each of these, along with tips for making them perform well.
Steps involved in implementing a near-real-time data warehousing solution.