Expanded Library

Implementing ETL Pipelines on the Microsoft SQL Server Platform

by Thomas LeBlanc

This course will teach you how to use the SQL Server database engine and Integration Services (SSIS) to develop, deploy, and monitor an ETL pipeline with the SQL Server data stack for dimension and fact tables.

What you'll learn

Implementing pipelines for dimensional models is very common in SQL Server with Integration Services. In this course, Implementing ETL Pipelines on the Microsoft SQL Server Platform, you will learn to use Integration Services (SSIS) to create packages to populate dimension and fact tables. Next, you will understand how to use Lookup, Data Transformation, Conditional Split, and OLEDB components to complete a pipeline for ETL processes. Finally, you will learn how to stage data to execute batch update statements for improving update performance of changing data. When you’re finished with this course, you’ll have the skills and knowledge of Implementing ETL Pipelines on the Microsoft SQL Server Platform needed to develop SSIS packages that can be deployed to production and monitored for performance and errors.

Table of contents

Course Overview

Course FAQ

What prerequisites are needed for this course?

Before beginning this course, you will want to be familiar with the SQL Server database engine, including creating tables, indexes, and foreign keys. You should also have SQL Server data tools installed for developing ETL pipelines using Integration Services, or SSIS.

What software is required for this course?

SQL Server Management Studio should be installed for managing the SQL Server instance.

What is Microsoft SQL Server?

Microsoft SQL Server is a relational database management system developed by Microsoft. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications—which may run either on the same computer or on another computer across a network.

What is an ETL Pipeline?

An ETL Pipeline refers to a set of processes extracting data from an input source, transforming the data, and loading into an output destination such as a database, data mart, or a data warehouse for reporting, analysis, and data synchronization. The letters stand for Extract, Transform, and Load.

Why is ETL process important for data warehousing?

ETL tools break down data silos and make it easy for your data scientists to access and analyze data, and turn it into business intelligence. In short, ETL tools are the first essential step in the data warehousing process that eventually lets you make more informed decisions in less time.

About the author

Thomas LeBlanc (Microsoft Data Platform MVP) is a Data Enthusiast in Baton Rouge, La. He started his IT career as a COBOL programmer in 1989 while at LSU. Since then he has spent years as a developer, DBA and Data Warehousing/Business Intelligence Architect. Community involve includes the local SQL Server and Analytics User Groups as well as SQLSaturday. Speaking engagements have been a blessing at SQL Pass Summit, Live 360, VSLive and IT Dev Connections.

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