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Module 1: Introduction to Data Warehousing
- What is data warehousing?
- What is a data mart?
- Why build a data warehouse?
- What are some business scenarios that would require a data warehouse?
Skills
- Describe data warehousing and the reasons for implementing a data warehousing solution.
- Explain the relationship of data marts to a data warehouse.
- Describe the data warehousing process, including its basic elements and the tools that manage it.
- Describe how data is structured in a data warehouse.
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Module 2: Designing a Data Warehousing System
- Business analysis process
- Data warehousing system
- Modelling a data warehouse
- Choosing the grain
- Establishing dimensions
- Establishing a fact table
- Implementing a star schema
- Lab: Designing a star schema
- Lab: Implementing a star schema
Skills
- Identify specific steps involved in analyzing and implementing a data warehousing system.
- Use star and snowflake schema to model a data warehouse or data mart database.
- Design a data warehouse or data mart database.
- Choose an appropriate grain for the fact table.
- Define dimensions and facts.
- Create a data warehouse or data mart database.
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Module 3: Populating a Data Warehouse
- Process overview
- Methods of populating a data warehouse
- Tools for populating a data warehouse
- Populating a data warehouse by using DTS
- Lab: Populating a data warehouse
Skills
- Describe the process of populating a data warehouse.
- Describe several methods of populating a data warehouse.
- Describe the Microsoft SQL Server version 7.0 tools available for populating a data warehouse.
- Populate a data warehouse by using Data Transformation Services (DTS).
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Module 4: Creating Cubes
- Introduction to cubes
- Defining cubes
- Managing access to cubes
- Demonstration: using OLAP manager
- Storing cubes
- Processing cubes
- Customizing cubes
- Lab: Creating and processing cubes
Skills
- Define a cube by identifying fact tables, defining dimensions, and creating aggregations.
- Establish security protocol for accessing data in cubes.
- Choose an appropriate storage mechanism for a cube, such as relational online analytical processing (ROLAP), multidimensional OLAP (MOLAP), or hybrid OLAP (HOLAP).
- Create cubes with calculated members and create virtual cubes.
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Module 5: Analysing Cube Data Using Clients
- Concepts of data analysis
- Analysing local cubes
- Analysing data using the Web
- Tools for analysing data
- Lab: Browsing cube data using Office 2000
Skills
- Describe basic data analysis concepts.
- Describe how to analyse cube data when disconnected from the network.
- Use OLAP Manager and Microsoft Excel to analyse data from a cube.
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Module 6: Multi-Dimensional Expressions (MDX)
- What Is MDX?
- Parts of an MDX statement
- Writing an MDX query
- Lab: Writing MDX statements
Skills
- Describe the function and use of MDX syntax.
- Describe the parts of an MDX (multidimensional expressions) statement.
- Write an MDX statement to query a cube.
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Module 7: Building OLAP Clients
- Introducing the OLAP Services architecture
- Analyzing existing cube metadata
- Creating and populating a cellset
- Retrieving data
- Creating local cubes
- Lab: Accessing data using ADO MD
Skills
- Describe Microsoft SQL Server OLAP Services client/server architecture.
- Analyze existing cube metadata by connecting to multidimensional data sources and accessing cube definitions.
- Create and populate a cellset.
- Retrieve data from cellsets and individual cells.
- Create local cubes.
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Module 8: Building Applications by Using Microsoft English Query
- Introduction to English Query
- Database normalization requirements
- Creating an English Query application
- Designing an English Query application for multidimensional databases
- Deploying an English Query application
- Lab: Building applications by using English Query
Skills
- Design a Microsoft English Query application.
- Create a Microsoft English Query application.
- Test an English Query application.
- Deploy an English Query application in a Web page or in Microsoft Visual Basic or Microsoft Visual C++® applications.
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Module 9: Maintaining a SQL Server Data Warehouse
- Developing a maintenance plan
- Synchronizing data
- Maintaining SQL Server data
- Maintaining OLAP Services data
- Backing up and restoring databases
- Automating administrative tasks
- Archiving enterprise data
- Lab: Maintaining a SQL Server data warehouse
Skills
- Develop a maintenance plan.
- Synchronize Microsoft SQL Server and Microsoft SQL Server OLAP Services data.
- Maintain SQL Server data.
- Maintain data in an online analytical processing (OLAP) environment.
- Back up and restore specific elements in a data warehouse.
- Automate administrative tasks.
- Archive and store data and metadata in Microsoft Repository.
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Module 10: Managing a SQL Server Data Warehouse
- Managing slowly changing dimensions
- Optimizing your configuration
- Optimizing your server configuration
- Optimizing data warehouse performance
- Optimizing cube design
- Creating partitions
- Optimizing based on usage
- Lab: Creating a cube with partitions
Skills
- Manage changing dimensions.
- Describe optimization strategies for configuring a server.
- Describe optimization strategies for a data warehouse.
- Describe optimization strategies for cubes.
- Create cube partitions.
- Optimize performance by determining appropriate levels of aggregations, indexing, and storage methods.
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Exams:
-
There are no exams directly associated with this course
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Price Options ex VAT:
Classroom Training
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Distance Learning
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eLearning Options
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Book Learning
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£
1750
(€2486)
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£
995.00
(€1413)
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No Books Supported for Course at present
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Call Free on 0800 169 1890
Print 2 Page Flyer
Last Modified 01 May 2008
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