Learn how to implement a data warehouse platform to support a BI solution.

This is a hands-on course. It includes lots of time to practise what you are being taught and try things out for yourself.

By the time you finish, you’ll be comfortable using a wide range of SQL’s BI tools.

✔ A complete SQL BI course for SQL professionals.
✔ Plenty of one to one attention and time to ask questions.
✔ Friendly expert trainers, small groups and a comfortable place to learn.
✔ All the materials and extras that you’ll ever need.
✔ Ongoing support and help with issues you have after the course.

What Will I Learn?

This 4-day course will show you how to implement a BI platform to support information worker analytics.

You will learn how to:

  • Create a data warehouse with Microsoft SQL Server.
  • Implement ETL with SQL Server Integration Services.
  • Validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

Am I Ready For This Course?

This course is intended for database professionals who need to fulfil a Business Intelligence (BI) Developer role.

To get the most from this course you will need a working knowledge of:

  • Database administration and maintenance.
  • Core Windows Server skills.
  • Writing queries using Microsoft SQL Server Transact-SQL.

The Training Day

Our courses run from 9.30am to roughly 4.15pm with refreshments throughout the day and a break for lunch.

We offer a relaxed, supportive learning environment, fully air-conditioned training facilities and some of the nicest instructors on the planet.

Also, you’ll receive:

✔ A full-colour manual covering everything in the course so that you can recap.
✔ A USB stick for the course exercises to let you practise more in your own time.
✔ A Certificate of Attendance.

Course Details

  • Module Overview
  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution
  • Lab: Exploring a Data Warehouse Solution:
    • Exploring data sources
    • Exploring an ETL process
    • Exploring a data warehouse
  • Module Overview
  • Considerations for data warehouse infrastructure
  • Planning data warehouse hardware.
  • Lab: Planning Data Warehouse Infrastructure
  • Module Overview
  • Designing dimension tables
  • Designing fact tables
  • Physical Design for a Data Warehouse
  • Lab: Implementing a Data Warehouse Schema
  • Module Overview
  • Introduction to Columnstore Indexes
  • Creating Columnstore Indexes
  • Working with Columnstore Indexes
  • Lab: Using Columnstore Indexes
  • Module Overview
  • Introduction to ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow
  • Lab: Implementing Data Flow in a SSIS Package
  • Module Overview
  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Managing consistency.
  • Lab: Implementing Control Flow in a SSIS Package
  • Lab: Using Transactions and Checkpoints
  • Module Overview
  • Debugging a SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in a SSIS Package
  • Lab: Debugging and Troubleshooting a SSIS Package
  • Module Overview
  • Introduction to Incremental ETL
  • Extracting Modified Data
  • Loading modified data
  • Temporal Tables
  • Lab: Extracting Modified Data
  • Lab: Loading a data warehouse
  • Module Overview
  • Introduction to Data Quality
  • Using Data Quality Services to Cleanse Data
  • Using Data Quality Services to Match Data
  • Lab: Cleansing Data
  • Lab: De-duplicating Data
  • Module Overview
  • Introduction to Master Data Services
  • Implementing a Master Data Services Model
  • Hierarchies and collections
  • Creating a Master Data Hub
  • Lab: Implementing Master Data Services
  • Module Overview
  • Using scripting in SSIS
  • Using custom components in SSIS
  • Lab: Using scripts
  • Module Overview
  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution
  • Lab: Deploying and Configuring SSIS Packages
  • Module Overview
  • Introduction to Business Intelligence
  • An Introduction to Data Analysis
  • Introduction to reporting
  • Analyzing Data with Azure SQL Data Warehouse
  • Lab: Using a data warehouse