SQL Server Analysis Services for Business Intelligence (BI Series)
About this Course
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With the current explosion of data in today’s enterprise environment, traditional methods of querying and reporting on information are no longer sufficient. This course provides the skills to analyse and discover trends in your data warehouse. You learn to create On-Line Analytical Processing (OLAP) cubes using business intelligence tools and to automate their maintenance.
Why Attend this Course?
This is an extensive hands on course taught in small groups to maximise the learning experience.
What Makes this Course Stand Apart?
Delivered in small group size to maximise the learning experience.
Available with Plus1 Accelerator Option.
What you will Learn?
Leverage SQL Server Analysis Services to produce BI solutions
Create OLAP cubes and automate maintenance with XMLA scripts
Extend hierarchies and exploit advanced dimension relationships
Build custom solutions with MDX
Implement key performance indicators to monitor business objectives
Make smarter business decisions with data-mining techniques
This course is intended for SQL professionals.
Those with a working knowledge of relational databases who want to design, create or develop analysis cubes from a database.
Building and Modifying an OLAP Cube
Designing a Unified Dimension Model (UDM)
Identifying measures and their suitable granularities
Adding new measure groups and creating custom measures
Implementing a Star and Snowflake Schema
Identifying role-play dimensions
Adding dimension attributes and properties
Configuring multilanguage support
Extending the Cube with Hierarchies
Building natural hierarchies and creating attribute relationships
Discretising attribute values with the Clusters and Equal Areas algorithms
Defining parent and key attributes
Generating level captions with Naming Template
Exploiting Advanced Dimension Relationships
Storing dimension data in fact tables
Building a degenerate dimension
Configuring fact relationships
Saving space with referenced dimension relationships
Identifying candidates for referenced relationships
Utilising the Dimension Usage tab to configure referenced relationships
Including dimensions with many-to-many relationships
Implementing intermediate measure groups and dimensions
Reporting on many-to-many dimensions without double counting
Implementing a Tabular Model Database
Providing users with analytics via xVelocity and Power Pivot
Comparing DAX with MDX
Mapping out the role of SharePoint
Designing storage and aggregations
Choosing between ROLAP, MOLAP and HOLAP
Partitioning cubes for improved performance
Designing aggregations with Aggregation Design Wizard
Leveraging the Usage-Based Optimisation Wizard
Automating processing and deployment
Exploiting XMLA scripts and SSIS
Refreshing cubes with Proactive Caching
Performing Advanced Analysis with MDX
Retrieving data with MDX
Defining tuples, sets and calculated members
Querying cubes with MDX
Monitoring business performance with KPIs
Building goal, status and trend expressions
Using PARALLELPERIOD to compare past time periods
Simplifying KPI definitions using KPIValue and KPIGoal
Enhancing cubes with MDX
Adding runtime calculations to the cube
Adding drill-through and URL actions
Gaining Business Advantage with Data Mining
Determining the correct model
Identifying business tasks for data mining
Training and testing data-mining algorithms
Comparing algorithms with the accuracy chart
Performing real-world predictions
Classifying with Decision Trees, Neural Network and Naive Bayes algorithms
Predicting with the Time Series algorithm
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