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SAS(R) Data Integration Studio: Fast Track

Course Details
Code: DIFT49
Tuition (USD): $4,000.00 • Classroom (4 days)
Course Details
GSA (USD): $3,627.20 • Classroom (4 days)

This course is in a boot-camp format. It includes the content of both SAS(R) Data Integration Studio 1: Essentials and SAS(R) Data Integration Studio 2: 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 e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual Lab time to practice.

Skills Gained

  • 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 Can Benefit

  • Data integration developers and data integration architects

Prerequisites

  • Before attending this course, you should have experience with
  • SAS programming basics
  • SQL processing
  • the SAS macro facility.

Course Details

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
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