20463: Implementing a Data Warehouse with Microsoft SQL Server Training in Rialto

Enroll in or hire us to teach our 20463: Implementing a Data Warehouse with Microsoft SQL Server class in Rialto, California by calling us @303.377.6176. Like all HSG classes, 20463: Implementing a Data Warehouse with Microsoft SQL Server may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, 20463: Implementing a Data Warehouse with Microsoft SQL Server may be taught at one of our local training facilities.

Answers to Popular Questions:

 
Yes, this class can be tailored to meet your specific training needs.
Yes, we provide consulting services.
Yes, group discounts are provided.

Course 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.
Course Length: 5 Days
Course Tuition: $2090 (US)

Prerequisites

This course requires that you meet the following prerequisites: At least 2 yearsâ?? experience of working with relational databases, including: Designing a normalized database. Creating tables and relationships. Querying with Transact-SQL. Some exposure to basic programming constructs (such as looping and branching). An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable.

Course Outline

 

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.

Lessons

    Overview of Data Warehousing
    Considerations for a Data Warehouse Solution

Lab: Exploring a Data Warehousing Solution

    Exploring Data Sources
    Exploring and ETL Process
    Exploring a Data Warehouse

After completing this module, you will be able to:

    Describe the key elements of a data warehousing solution
    Describe the key considerations for a data warehousing project

Module 2: Planning Data Warehouse Infrastructure
This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.

Lessons

    Considerations for Data Warehouse Infrastructure
    Planning Data Warehouse Hardware

Lab: Planning Data Warehouse Infrastructure

    Planning Data Warehouse Hardware

After completing this module, you will be able to:

    Describe key considerations for BI infrastructure.
    Plan 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.

Lessons

    Data Warehouse Design Overview
    Designing Dimension Tables
    Designing Fact Tables
    Physical Design for a Data Warehouse

Lab: Implementing a Data Warehouse

    Implement a Star Schema
    Implement a Snowflake Schema
    Implement a Time Dimension

After completing this module, you will be able to:

    Describe a process for designing a dimensional model for a data warehouse
    Design dimension tables for a data warehouse
    Design fact tables for a data warehouse
    Design and implement effective physical data structures for a data warehouse

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.

Lessons

    Introduction to ETL with SSIS
    Exploring Data Sources
    Implementing Data Flow

Lab: Implementing Data Flow in an SSIS Package

    Exploring Data Sources
    Transferring Data by Using a Data Flow Task
    Using Transformations in a Data Flow

After completing this module, you will be able to:

    Describe the key features of SSIS.
    Explore source data for an ETL solution.
    Implement a data flow by using SSIS

Module 5: Implementing Control Flow in an SSIS Package
This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.

Lessons

    Introduction to Control Flow
    Creating Dynamic Packages
    Using Containers
    Managing Consistency

Lab: Implementing Control Flow in an SSIS Package

    Using Tasks and Precedence in a Control Flow
    Using Variables and Parameters
    Using Containers

Lab: Using Transactions and Checkpoints

    Using Transactions
    Using Checkpoints

After completing this module, you will be able to:

    Implement control flow with tasks and precedence constraints
    Create dynamic packages that include variables and parameters
    Use containers in a package control flow
    Enforce consistency with transactions and checkpoints

Module 6: Debugging and Troubleshooting SSIS Packages
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.

Lessons

    Debugging an SSIS Package
    Logging SSIS Package Events
    Handling Errors in an SSIS Package

Lab: Debugging and Troubleshooting an SSIS Package

    Debugging an SSIS Package
    Logging SSIS Package Execution
    Implementing an Event Handler
    Handling Errors in a Data Flow

After completing this module, you will be able to:

    Debug an SSIS package
    Implement logging for an SSIS package
    Handle errors in an SSIS package

Module 7: Implementing a Data Extraction Solution
This module describes the techniques you can use to implement an incremental data warehouse refresh process.

Lessons

    Planning Data Extraction
    Extracting Modified Data

Lab: Extracting Modified Data

    Using a Datetime Column
    Using Change Data Capture
    Using the CDC Control Task
    Using Change Tracking

After completing this module, you will be able to:

    Plan data extraction
    Extract modified data

Module 8: Loading Data into a Data Warehouse
This module describes the techniques you can use to implement data warehouse load process.

Lessons

    Planning Data Loads
    Using SSIS for Incremental Loads
    Using Transact-SQL Loading Techniques

Lab: Loading a Data Warehouse

    Loading Data from CDC Output Tables
    Using a Lookup Transformation to Insert or Update Dimension Data
    Implementing a Slowly Changing Dimension
    Using the MERGE Statement

After completing this module, you will be able to:

    Describe the considerations for planning data loads
    Use SQL Server Integration Services (SSIS) to load new and modified data into a data warehouse
    Use Transact-SQL techniques to load data into a data warehouse

Module 9: 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.

Lessons

    Introduction to Data Quality
    Using Data Quality Services to Cleanse Data
    Using Data Quality Services to Cleanse Data

Lab: Cleansing Data

    Creating a DQS Knowledge Base
    Using a DQS Project to Cleanse Data
    Using DQS in an SSIS Package

After completing this module, you will be able to:

    Describe how Data Quality Services can help you manage data quality
    Use Data Quality Services to cleanse your data
    Use Data Quality Services to match data

Module 10: 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.

Lessons

    Introduction to Master Data Services
    Implementing a Master Data Services Model
    Managing Master Data
    Creating a Master Data Hub

Lab: Implementing Master Data Services

    Creating a Master Data Services Model
    Using the Master Data Services Add-in for Excel
    Enforcing Business Rules
    Loading Data Into a Model
    Consuming Master Data Services Data

After completing this module, you will be able to:

    Describe key Master Data Services concepts
    Implement a Master Data Services model
    Use Master Data Services tools to manage master data
    Use Master Data Services tools to create a master data hub

Module 11: 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.

Lessons

    Using Scripts in SSIS
    Using Custom Components in SSIS

Lab: Using Custom Scripts

    Using a Script Task

After completing this module, you will be able to:

    Include custom scripts in an SSIS package
    Describe how custom components can be used to extend SSIS

Module 12: Deploying and Configuring SSIS Packages
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.

Lessons

    Overview of SSIS Deployment
    Deploying SSIS Projects
    Planning SSIS Package Execution

Lab: Deploying and Configuring SSIS Packages

    Creating an SSIS Catalog
    Deploying an SSIS Project
    Running an SSIS Package in SQL Server Management Studio
    Scheduling SSIS Packages with SQL Server Agent

After completing this module, you will be able to:

    Describe considerations for SSIS deployment.
    Deploy SSIS projects.
    Plan SSIS package execution.

Module 13: 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.

Lessons

    Introduction to Business Intelligence
    Enterprise Business Intelligence
    Self-Service BI and Big Data

Lab: Using a Data Warehouse

    Exploring an Enterprise BI Solution
    Exploring a Self-Service BI Solution
    After completing this module, you will be able to:
    Describe BI and common BI scenarios
    Describe how a data warehouse can be used in enterprise BI scenarios
    Describe how a data warehouse can be used in self-service BI scenarios

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