Implementing a Data Warehouse with Microsoft® SQL Server® 2014 (20463D) - Agilis

Implementing a Data Warehouse with Microsoft® SQL Server® 2014 (20463D)

Warehouse photo

Description

This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft® SQL Server® 2014, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

Note: This course is designed for customers who are interested in learning SQL Server 2012 or SQL Server 2014. It covers the new features in SQL Server 2014, but also the important capabilities across the SQL Server data platform.

Syllabus

Module 1: Introduction to Data Warehousing

This module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when you embark on a data warehousing project.

Lab: Exploring a Data Warehousing Solution

Module 2: Data Warehouse Hardware Considerations

This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.

Lab: Planning Data Warehouse Infrastructure

Module 3: Designing and Implementing a Data Warehouse

This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation.

Lab: Implementing a Data Warehouse Schema

Module 4: Creating an ETL Solution with SSIS

This module discusses considerations for implementing an ETL process, and then focuses on Microsoft SQL Server Integration Services (SSIS) as a platform for building ETL solutions.

Lab: Implementing Data Flow in an SSIS Package

Module 5: Implementing Control Flow in an SSIS Package

This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.

Lab: Implementing Control Flow in an SSIS Package

Lab: Using Transactions and Checkpoints

Module 6: Debugging and Troubleshooting SSIS Package

This module describes how you can debug packages to find the cause of errors that occur during execution. It then discusses the logging functionality built into SSIS that you can use to log events for troubleshooting purposes. Finally, the module describes common approaches for handling errors in control flow and data flow.

Lab: Debugging and Troubleshooting an SSIS Package

Module 7: Implementing an Incremental ETL Process

This module describes the techniques you can use to implement an incremental data warehouse refresh process.

Lab: Extracting Modified Data

Lab: Loading Incremental Changes

Module 8: Enforcing Data Quality

This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and deduplicate data.

Lab: Cleansing Data

Lab: De-duplicating data

Module 9: Using Master Data Services

Master Data Services provides a way for organizations to standardize data and improve the quality, consistency, and reliability of the data that guides key business decisions. This module introduces Master Data Services and explains the benefits of using it.

Lab : Implementing Master Data Services

Module 10: Extending SQL Server Integration Services

This module describes the techniques you can use to extend SSIS. The module is not designed to be a comprehensive guide to developing custom SSIS solutions, but to provide an awareness of the fundamental steps required to use custom components and scripts in an ETL process that is based on SSIS.

Lab: Using Custom Components and Scripts

Module 11: Deploying and Configuring SSIS Package

In this module, students will learn how to deploy packages and their dependencies to a server, and how to manage and monitor the execution of deployed packages.

Lab: Deploying and Configuring SSIS Packages

Module 12: Consuming Data in a Data Warehouse

This module introduces business intelligence (BI) solutions and describes how you can use a data warehouse as the basis for enterprise and self-service BI.

Lab: Using Business Intelligence Tools

Learning Outcomes

After completing this course, students will be able to:

  • Describe data warehouse concepts and architecture considerations.
  • Select an appropriate hardware platform for a data warehouse.
  • Design and implement a data warehouse.
  • Implement Data Flow in an SSIS Package.
  • Implement Control Flow in an SSIS Package.
  • Debug and Troubleshoot SSIS packages.
  • Implement an ETL solution that supports incremental data extraction.
  • Implement an ETL solution that supports incremental data loading.
  • Implement data cleansing by using Microsoft Data Quality Services.
  • Implement Master Data Services to enforce data integrity.
  • Extend SSIS with custom scripts and components.
  • Deploy and Configure SSIS packages.
  • Describe how BI solutions can consume data from the data warehouse.

Participants and Requirements

Before attending this course, students must have:
• At least 2 years’ experience of working with relational databases, including:
• Designing a normalised database.
• Creating tables and relationships.
• Querying with Transact-SQL.
• Some exposure to basic programming constructs (such as looping and branching).

This course is intended for database professionals who need to fulfil a Business Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including Data Warehouse implementation, ETL, and data cleansing. Primary responsibilities include:
• Implementing a data warehouse.
• Developing SSIS packages for data extraction, transformation, and loading.
• Enforcing data integrity by using Master Data Services.
• Cleansing data by using Data Quality Services.

Certificate

Microsoft Certificate

Course Duration

5 days

Top