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OD20463 Implementing a Data Warehouse with Microsoft SQL Server 2014 (90 Day)

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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.

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
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: Data Warehouse Hardware Considerations
This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.
Lessons

Considerations for building a Data Warehouse
Data Warehouse Reference Architectures and Appliances

Lab : Planning Data Warehouse Infrastructure
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

Logical Design for a Data Warehouse
Physical design for a data warehouse

Lab : Implementing a Data Warehouse Schema
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
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 PackageLab : Using Transactions and 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
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
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 a data warehouse load process
.Lessons

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

Lab : Loading a Data WarehouseAfter 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 QualityEnsuring the high quality of data is essential if the results of data analysis are to be trusted. SQL Server 2014 includes Data Quality Services (DQS) to provide a computer-assisted process for cleansing data values, as well as identifying and removing duplicate data entities. This process reduces the workload of the data steward to a minimum while maintaining human interaction to ensure accurate results.Lessons

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

Lab : Cleansing DataLab : Deduplicating Data
After completing this module, you will be able to:• Describe how DQS can help you manage data quality.• Use DQS to cleanse your data.• Use DQS to match data
.Module 10: Master Data Services
Master Data Services provides a way for organizations to standardize 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 ServicesAfter completing this module, you will be able to:• Describe the key concepts of Master Data Services.• 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 ServicesThis 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, based on SSIS.Lessons

Using Scripts in SSIS
Using Custom Components in SSIS

Lab : Using Custom Scripts
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
Microsoft SQL Server Integration Services (SSIS) 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.
Lessons

Overview of SSIS Deployment
Deploying SSIS Projects
Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages
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 BI, describing the components of Microsoft SQL Server that you can use to create a BI solution, and the client tools with which users can create reports and analyze data.
Lessons

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

Lab : Using a Data Warehouse
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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