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Home Courses 204633: SQL Server Integration Services 2012-2014 (3 day version of course 20463)

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204633: SQL Server Integration Services 2012-2014 (3 day version of course 20463)

Length: 3    Cost: $1,945 + GST    Version: SQL Server 2012 - 2014

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About this Course

This three day course has been created for SQL users, who would like to focus on creating packages for data extraction, transformation and loading (ETL) using integration services over a 3 day period.

For a more in-depth training course that covers data warehousing and integration services or if you are working towards certification or have Microsoft software assurance vouchers that you would like to utilise for training then the following 5 day course is recommended. 20463: Implementing a Data Warehouse with Microsoft SQL Server 2012.

Please note from 1 April 2017 we will only supply digital courseware, if a hard copy is preferred an additional cost will apply.



Audience


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 ETL, and data cleansing. Primary responsibilities include:

  • Developing SQL Server Integration Services (SSIS) packages for data extraction, transformation, and loading (ETL).
  • Enforcing data integrity by using Master Data Services
  • Cleansing data by using Data Quality Services

Prerequisites

Before attending this course, student should have:

  • At least 2 years_ 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

At Course Completion

This course is designed to provide training on how to implement Extract, Transform and Load (ETL) using Microsoft SQL Server 2012 and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

After completing this course, participants will be able to:

  • Describe data warehouse concepts and architecture considerations
  • Implement Data Flow in an SSIS Package
  • Implement Control Flow in an SSIS Package
  • Debug and Troubleshoot SSIS packages
  • Implement an SSIS solution that supports incremental data warehouse loads and changing data
  • Implement data cleansing by using Microsoft Data Quality Services
  • Implement Master Data Services to enforce data integrity
  • Deploy and configure SSIS packages

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 starting a data warehousing project.

  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution

Lab : Exploring a Data Warehousing Solution

  • Exploring data sources
  • Exploring an ETL solution
  • Exploring a data warehouse

After completing this module, students will be able to:

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

Module 2: Creating an ETL Solution with SSIS

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

  • Introduction to ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow

Lab : Implementing Data Flow in a SSIS Package

  • Exploring Source Data
  • Transferring Data by Using a Data Flow Task
  • Using Transformations in a Data Flow

After completing this module, students will be able to:

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

Module 3: Implementing Control Flow in an SSIS Package

Control flow in SQL Server Integration Services packages enables you to implement complex ETL solutions that combine multiple tasks and workflow logic. This module covers how to implement control flow, and design robust ETL processes for a data warehousing solution that coordinate data flow operations with other automated tasks.

  • 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, students 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 4: Debugging and Troubleshooting SSIS Packages

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

  • 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, students will be able to:

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

Module 5: Implementing an Incremental ETL Process

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

  • Introduction to Incremental ETL
  • Extracting Modified Data
  • Loading Modified Data

Lab : Extracting Modified Data

  • Using a DateTime Column to Incrementally Extract Data
  • Using a Change Data Capture
  • Using Change Tracking

Lab : Loading Incremental Changes

  • Using a Lookup Transformation to Insert Dimension Data
  • Using a Lookup Transformation to Insert or Update Dimension Data
  • Implementing a Slowly Changing Dimension
  • Using a MERGE Statement to Load Fact Data

After completing this module, students will be able to:

  • Describe the considerations for implementing an incremental extract, transform, and load (ETL) solution.
  • Use multiple techniques to extract new and modified data from source systems.
  • Use multiple techniques to insert new and modified data into a data warehouse.

Module 6: Enforcing Data Quality

Ensuring the high quality of data is essential if the results of data analysis are to be trusted. This module explains how to use the SQL Server 2012 Data Quality Services (DQS) to provide a computer assisted process for cleansing data values and identifying and removing duplicate data entities.

  • Introduction to Data Quality
  • Using Data Quality Services to Cleanse Data
  • Using Data Quality Services to Match Data

 Lab : Cleansing Data

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

Lab : Deduplicating Data

  • Creating a Matching Policy
  • Using a DQS Project to Match Data

After completing this module, students 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 7: Using Master Data Services

This module introduces Master Data Services and explains the benefits of using it in a data warehousing context. The module also describes the key configuration options for Master Data Services, and explains how to import and export data. Finally, the module explains how to apply rules that help to preserve data integrity, and introduces the new Master Data Services Add-in for Excel.

  • Introduction to Master Data Services
  • Implementing a Master Data Services Model
  • Using the Master Data Services Add-in for Excel

Lab : Implementing Master Data Services

  • Creating a Basic Model
  • Editing a Model by Using the Master Data Services Add-in for Excel
  • Loading Data into a Model
  • Enforcing Business Rules
  • Consuming Master Data Services Data

After completing this module, students will be able to:

  • Describe key Master Data Services concepts.
  • Implement a Master Data Services model.
  • Use the Master Data Services Add-in for Excel to view and modify a model.

Module 8: Deploying and Configuring SSIS Packages

SQL Server Integration Services provides tools that make it easy to deploy packages to another computer. The deployment tools also manage any dependencies, such as configurations and files that the package needs. In this module, you will learn how to use these tools to install packages and their dependencies on a target computer.

  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

  • Create a SSIS Catalog
  • Deploy an SSIS Project
  • Create Environments for an SSIS Solution
  • Running an SSIS Package in SQL Server Management Studio
  • Scheduling SSIS Packages with SQL Server Agent

After completing this module, students will be able to:

  • Describe SSIS deployment.
  • Explain how to deploy SSIS projects using the project deployment model.
  • Plan SSIS package execution.

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