Course Outline: Course: Implementing a Data Warehouse with Microsoft SQL Server 2012 Learning Method: Instructor-led Classroom Learning

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1 Course Outline: Course: Implementing a Data with Microsoft SQL Server 2012 Learning Method: Instructor-led Classroom Learning Duration: 5.00 Day(s)/ 40 hrs Overview: This 5-day instructor-led course describes how to implement a BI platform to support information worker analytics. Students will learn how to create a data warehouse with SQL Server 2012, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services Who Should Attend: The primary audience for this course is database professionals who need to fulfill a Business Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including Data implementation, ETL, and data cleansing. Primary responsibilities will include: Implementing as data warehouse Developing SSIS packages for data extraction and loading/transfer/transformation Enforcing data integrity using Master Data Services Cleansing data using Data Quality Services At Course Completion: After completing this course, students will be able to:

2 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 Data Flow in an SSIS Package. Debug and Troubleshoot SSIS packages. Implement an SSIS solution that supports incremental DW loads and changing data. Integrate cloud data into a data warehouse ecosystem infrastructure. Implement data cleansing by using Microsoft Data Quality Services. Implement Master Data Services to enforce data integrity at source. Extend SSIS with custom scripts and components. Deploy and Configure SSIS packages. Describe how information workers can consume data from the data warehouse Outline: Module 1: Introduction to Data Warehousing Describe data warehouse concepts and architecture considerations Considerations for a Data Solution Lab : Exploring a Data Warehousing Solution

3 Exploring Data Sources Exploring an ETL Process Exploring a Data Describe data warehouse concepts and architecture considerations. Module 2: Data Hardware Considerations The Challenges of Building a Data Data Reference Architectures Data Appliances Logical Design for a Data Physical Design for a Data Lab : Implementing a Data Schema Implementing a Star Schema Implementing a Snowflake Schema Implement a Time Dimension Table Design and implement a schema for a data warehouse. Module 4: Design and implement a schema for a data warehouse Module 3: Designing and Implementing a Data Introduction to ETL with SSIS Exploring Source Data Implementing Data Flow

4 Lab : Implementing Data Flow in an SSIS Package Exploring Source Data Transfer Data with a Data Flow Task Using Transformations in a Data Flow Implement Data Flow in an SSIS Package Module 5: Implementing Control Flow in an SSIS Package 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 Implement control flow in an SSIS package. Module 6: Debugging and Troubleshooting SSIS Packages 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

5 Logging SSIS Package Execution Implementing an Event Handler Handling Errors in a Data Flow Debug and Troubleshoot SSIS packages. Module 7: Implementing an Incremental ETL 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 DateTime Column to Incrementally Extract Data Using Change Tracking Lab : Loading Incremental Changes Using a Lookup task to insert dimension data Using a Lookup task to insert or update dimension data Implementing a Slowly Changing Dimension Using a MERGE statement to load fact data Implement an SSIS solution that supports incremental DW loads and changing data. Module 8: Incorporating Data from the Cloud in a Data Overview of Cloud Data Sources SQL Server Azure Azure Data Market Lab : Using Cloud data in a Data Solution

6 Extracting data from SQL Azure Acquiring Data from the Azure Data Market Integrate cloud data into a data warehouse ecosystem. Module 9: Enforcing Data Quality Introduction to Data Cleansing 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 Use DQS in an SSIS Package Lab : De-Duplicating Data Creating a Matching Policy Using a DQS Project to Match Data Implement data cleansing by using Microsoft Data Quality Services. Module 10: Using Master Data Services Master Data Services Concepts Implementing a Master Data Services Model Using the Master Data Services Excel Add-in Lab : Implementing Master Data Services Creating a Basic MDS Model Editing an MDS Model With Excel Loading Data into MDS Enforcing Business Rules Consuming Master Data Services Data Implement Master Data Services to enforce data integrity at source.

7 Module 11: Extending SSIS Using Custom Components in SSIS Using Scripting in SSIS Lab : Using Scripts and Custom Components Using a Custom Component Using the Script Task Extend SSIS with custom scripts and components Module 12: Deploying and Configuring SSIS Packages Overview of Deployment Deploying SSIS Projects Planning SSIS Package Execution Lab : Deploying and Configuring SSIS Packages Create an 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 Deploy and configure SSIS packages. Module 13: Consuming Data in a Data Using Excel to Analyze Data in a data. An Introduction to PowerPivot An Introduction to Crescent

8 Lab : Using a Data Use PowerPivot to Query the Data Visualizing Data by Using Crescent

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