OD20463 Implementing Data Warehouse Microsoft SQL Server 2014 (MOC On-Demand / MOCOnDemand)
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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.
There can be many combinations for how a course's content maps to the various examinations and related certifications.
We have listed as many of the primary ones as possible and continually update our database - please contact us for further information.
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 BI solutions including Data Warehouse implementation, ETL, and data cleansing. Primary responsibilities include:
Implementing a data warehouse
Developing SSIS packages for data extraction, transformation, and loading
Enforcing data integrity by using Master Data Services
Cleansing data by using Data Quality Services
After completing this course, students will be able to:
Before attending this course, students must have:
MOC On-Demand is an integrated blend of video, text, hands-on labs, and knowledge checks, designed to help IT professionals and developers build their skills on Microsoft technologies. The courses provide a self-paced, stand-alone training alternative for those who prefer to learn at their own convenience.
Any labs associated with courses are delivered on the Microsoft Labs Online (MLO) platform via web browser. Students launch the labs directly from within the course; no additional set-up is required.
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.
This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.
This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation.
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.
This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.
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.
This module describes the techniques you can use to implement an incremental data warehouse refresh process.
This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and deduplicate data.
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.
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.
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.
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.