Building a Better ETL Platform with SSIS

An Introduction to Data Warehousing Using SQL Server Integration Services

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 5 lectures (4h 15m) | Size: 1.1 GB

What you’ll learn
Learn the basics of a data warehouse.
Understand what ETL stands for and why it differs from normal database processes.
Develop SQL Server Integration Services (SSIS) packages for ETL.
Deploy SSIS packages to a server.

Requirements
Students must be proficient in writing SQL.

Description
Do you need to migrate a large volume of data without tying up your infrastructure?

Do you run complex queries that max out your system resources?

Are you building an analytics platform for business intelligence or data science?

If the answer to any of these questions is “yes”, then Faster SQL Mastery is the course for you!

Building a Better ETL with SSIS introduces you to the basics of ETL using Microsoft SQL Server Integration Services, or SSIS. You’ll learn the following topics in this course

Data Warehousing Overview

ETL Architecture Strategies

Change Tracking

SSIS Development

SSIS Deployment

I am a senior SQL database architect with over fifteen years of experience working with MS SQL Server. With the increasing opportunities in data engineering, data science, and business intelligence, SQL developers must be able to transition seamlessly into the world of analytics and data engineering.

Building a data warehouse requires a different set of skills than administering an ordinary application back-end database. Here you will get some real-world experience on how to set up and manage an ETL process that feeds a data warehouse to support these applications.

If you are an intermediate SQL developer or junior DBA with only a few years of experience, this course is perfect for elevating your SQL skills to expert levels. Enroll today to enhance your SQL expertise!

Who this course is for
Advanced SQL developers learning business intelligence and data warehousing.