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Home > Technical Courses > SQL Server > Implementing a SQL Data Warehouse - SSIS
This five-day instructor-led course provides you with the knowledge and skills to provision a Microsoft SQL Server database.
This five-day instructor-led course provides you with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server 2016 provision both on-premise and in Azure, and covers installing from new and migrating from an existing install.The primary audience for this course are 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 Warehouse implementation, ETL, and data cleansing.
Category
ID
Duration
Level
Price
SQL Server
M20463
5 Day(s)
Intermediate
$2,995.00
Objectives
• Describe the key elements of a data warehousing solution • Describe the main hardware considerations for building a data warehouse • Implement a logical design for a data warehouse • Implement a physical design for a data warehouse • Create columnstore indexes • Implementing an Azure SQL Data Warehouse • Describe the key features of SSIS • Implement a data flow by using SSIS • Implement control flow by using tasks and precedence constraints • Create dynamic packages that include variables and parameters • Debug SSIS packages • Describe the considerations for implement an ETL solution • Implement Data Quality Services • Implement a Master Data Services model • Describe how you can use custom components to extend SSIS • Deploy SSIS projects • Describe BI and common BI scenarios
Module 1: Introduction to Data Warehousing Describe data warehouse concepts and architecture considerations. Lessons • 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 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 solution Module 2: Planning Data Warehouse Infrastructure This module describes the main hardware considerations for building a data warehouse Lessons • Considerations for Building a Data Warehouse • Planning data warehouse hardware Lab : Planning Data Warehouse Infrastructure • Planning data warehouse hardware After completing this module, you will be able to: • Describe the main hardware considerations for building a data warehouse • Explain how to use reference architectures and data warehouse appliances to create a data warehouse Module 3: Designing and Implementing a Data Warehouse This module describes how you go about designing and implementing a schema for a data warehouse. Lessons • Data warehouse design overview • Designing dimension tables • Designing fact tables • Physical Design for a Data Warehouse Lab : Implementing a Data Warehouse Schema • Implementing a star schema • Implementing a snowflake schema • Implementing a time dimension table After completing this module, you will be able to: • Implement a logical design for a data warehouse • Implement a physical design for a data warehouse Module 4: Columnstore Indexes This module introduces Columnstore Indexes Lessons • Introduction to Columnstore Indexes • Creating Columnstore Indexes • Working with Columnstore Indexes Lab : Using Columnstore Indexes • Create a Columnstore index on the FactProductInventory table • Create a Columnstore index on the FactInternetSales table • Create a memory optimized Columnstore table After completing this module, you will be able to: • Create Columnstore indexes • Work with Columnstore Indexes Module 5: Implementing an Azure SQL Data Warehouse This module describes Azure SQL Data Warehouses and how to implement them. Lessons • Advantages of Azure SQL Data Warehouse • Implementing an Azure SQL Data Warehouse • Developing an Azure SQL Data Warehouse • Migrating to an Azure SQ Data Warehouse • Copying data with the Azure data factory Lab : Implementing an Azure SQL Data Warehouse • Create an Azure SQL data warehouse database • Migrate to an Azure SQL Data warehouse database • Copy data with the Azure data factory After completing this module, you will be able to: • Describe the advantages of Azure SQL Data Warehouse • Implement an Azure SQL Data Warehouse • Describe the considerations for developing an Azure SQL Data Warehouse • Plan for migrating to Azure SQL Data Warehouse Module 6: Creating an ETL Solution At the end of this module you will be able to implement data flow in a SSIS package. Lessons • Introduction to ETL with SSIS • Exploring Source Data • Implementing Data Flow Lab : Implementing Data Flow in an SSIS Package • Exploring source data • Transferring data by using a data row task • Using transformation components in a data row After completing this module, you will be able to: • Describe ETL with SSIS • Explore Source Data • Implement a Data Flow Module 7: Implementing Control Flow in an SSIS Package This module describes implementing control flow in an SSIS package. 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: • Describe control flow • Create dynamic packages • Use containers Module 8: Debugging and Troubleshooting SSIS Packages This module describes how to debug and troubleshoot SSIS packages 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 data flow After completing this module, you will be able to: • Debug an SSIS package • Log SSIS package events • Handle errors in an SSIS package
Module 9: Implementing a Data Extraction Solution This module describes how to implement an SSIS solution that supports incremental DW loads and changing data. Lessons • Introduction to Incremental ETL • Extracting Modified Data • Loading modified data • Temporal Tables Lab : Extracting Modified Data • Using a datetime column to incrementally extract data • Using change data capture • Using the CDC control task • Using change tracking 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 incremental ETL • Extract modified data • Describe temporal tables Module 10: Enforcing Data Quality This module describes how to implement data cleansing by using Microsoft Data Quality services. Lessons • 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 : De-duplicating Data • Creating a matching policy • Using a DS project to match data After completing this module, you will be able to: • Describe data quality services • Cleanse data using data quality services • Match data using data quality services • De-duplicate data using data quality services Module 11: Using Master Data Services This module describes how to implement master data services to enforce data integrity at source. Lessons • Introduction to Master Data Services • Implementing a Master Data Services Model • Hierarchies and collections • 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 the key concepts of master data services • Implement a master data service model • Manage master data • Create a master data hub Module 12: Extending SQL Server Integration Services (SSIS) This module describes how to extend SSIS with custom scripts and components. Lessons • Using Custom Components in SSIS • Using Scripting in SSIS Lab : Using Scripts • Using a script task After completing this module, you will be able to: • Use custom components in SSIS • Use scripting in SSIS Module 13: Deploying and Configuring SSIS Packages This module describes how to deploy and configure SSIS 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 • Creating 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, you will be able to: • Describe an SSIS deployment • Deploy an SSIS package • Plan SSIS package execution Module 14: Consuming Data in a Data Warehouse This module describes how to debug and troubleshoot SSIS packages. Lessons • Introduction to Business Intelligence • An Introduction to Data Analysis • Introduction to reporting • Analyzing Data with Azure SQL Data Warehouse Lab : Using a Data Warehouse • Exploring a reporting services report • Exploring a PowerPivot workbook • Exploring a power view report After completing this module, you will be able to: • Describe at a high level business intelligence • Show an understanding of reporting • Show an understanding of data analysis • Analyze data with Azure SQL data warehouse
Questions?
MCSA: SQL Server 2012/2014
· In addition to their professional experience, students who attend this training should already have the following technical knowledge:· Basic knowledge of the Microsoft Windows operating system and its core functionality.· Working knowledge of relational databases.· Some experience with database design.
Implementing a SQL Data Warehouse - SSIS
Class Schedule
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