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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.
Indexing is crucial for performance. Typical BI queries have different indexing needs that operational queries. This module explains how to create and maintain columnstore indexes, which are ideal suited for most BI needs.
This module introduces how to host a data warehouse in Azure using provisioned databases in Azure Synapse Analytics, formerly known as Azure SQL Data Warehouse.
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.
Ensuring the high quality of data is essential if the results of data analysis are to be trusted. SQL Server 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.
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.
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, based on SSIS.
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.
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.
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 2022 or earlier, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.
This course is intended for database professionals who need to create and support a data warehousing solution. Primary responsibilities include:
This course does not assume any prior knowledge with SQL Server Integration Services or building a relational data warehouse.