There is a new version of this course. Please see SAS Data Integration Studio: Fast Track.

This course is in a boot-camp format. It includes the content of both SAS Data Ingegration Studio: Essentials and SAS Data Integration Studio: Additional Topics. It introduces and expands the knowledge of SAS Data Integration Studio and contains topics about registering sources and targets; creating and working with jobs; and working with transformations. This course also provides information about working with slowly changing dimensions, working with the Loop transformations, and defining new transformations.

The self-study version of this course contains structured course notes that provide a detailed overview and exercises that help you develop essential skills. There is also a Virtual Lab that enables you to practice what you learn in the course.

The e-learning version includes the following:

• digital course notes for self-study
• Virtual Lab: 50 hours of hands-on software practice

Learn how to

• register source data and target tables
• create jobs and use the functionality of the Job Editor
• work with many of the transformations
• apply slowly changing dimensions
• work with Loop transformations
• create new transformations
• evaluate impact analysis
• export and import metadata
• establish checkpoints in job flow
• set up jobs for scheduling
• deploy jobs as SAS Stored Processes.

Who should attend

Data integration developers and data integration architects

Formats available	Standard duration
Classroom:	4.0 days
Live Web Classroom:	10 x 3.5 sessions	System Requirements

Before attending this course, you should have experience with

• SAS programming basics
• SQL processing
• the SAS macro facility.

You can gain this experience by completing the SAS Programming 1: Essentials, SAS SQL 1: Essentials, and SAS Macro Language 1: Essentials courses.

This course addresses SAS Data Integration Studio, SAS Data Quality Solution, SAS Analytics Platform software.

Introduction

• exploring the platform for SAS Business Analytics
• introduction to SAS Data Management applications
• introduction to the classroom environment and the course tasks

Working with Change Management

• introduction to change management
• establishing a change management environment (self-study)

Creating Metadata for Source Data

• setting up the environment
• registering source data metadata

Creating Metadata for Target Data

• registering target data metadata
• importing metadata

Creating Metadata for Jobs

• introduction to jobs and the Job Editor
• using the Join transformation

Orion Star Case Study

• defining and loading the customer dimension table
• defining and loading the organization dimension table
• defining and loading the time dimension table

Additional Features for Jobs

• importing SAS code
• propagation and mapping
• chaining jobs
• performance statistics
• metadata reports

Working with Transformations

• using the extract and summary statistics transformations
• exploring SQL transformations
• establishing status handling
• using the Data Validation transformation
• using the Transpose, Sort, Append, Rank, and List Data transformations
• using the Apply Lookup Standardization, Standardize with Definition and One-Way Frequency transformations(self-study)

Working with the Loop Transformations

• introduction to the Loop transformation
• iterating a job
• iterating a transformation

Working with Slowly Changing Dimensions

• defining slowly changing dimensions
• using the SCD Type 2 Loader and Lookup transformations
• using the SCD Type 1 Loader transformation
• introducing the Change Data Capture transformation (self-study)

Creating Custom Transformations

• using the new Transformation Wizard

Working with the Table Loader Transformations

• exploring the basics of the Table Loader transformation
• exploring the load styles of the Table Loader transformation
• managing indexes and constraints during loading
• exploring bulk loading for DBMS tables

Working with Databases

• introduction to in-database processing
• using in-database processing
• exploring extract, load, and transform (ETL) processing
• using DBMS functions

Additional Topics for SAS Data Integration Studio Users

• overview of additional topics
• analyzing metadata using impact analysis
• comparing tables
• conditional execution
• metadata promotion
• version control
• establishing checkpoints

Deploying Jobs

• introduction to deploying jobs
• deploying jobs for scheduling
• deploying jobs in batch
• deploying jobs as stored processes

Implementing Data Quality Techniques (Self-Study)

• verifying data quality settings
• using the DataFlux transformation