Infomatica with Big Data (BDM)

The course provides extensive knowledge of Informatica’s solutions and how to design, develop and deploy scalable, consistent, and data-driven solutions for large data sets. Learners will gain an understanding of the benefits of a unified data infrastructure that supports both traditional and big data solutions.
This course will help you gain an understanding of the technologies and architectures used to integrate, extract and transform big data and create insightful business analytics. You will also develop competency in the Informatica tools used in the Big Data space, such as Bloombox, Big Data Management, and iData Fabric.

Introduction:

• Introduction to the Developer tool
• Brief Overview of Big Data Management
• BDM 9.x & 10.x Architecture & Differences
• Big Data Basics  Developer Tool Basics
• Reviewing the Developer interface

Developing Physical Data Objects:

• Introduction to the types of physical data objects
• Using relational connections
• Using flat file connections
• Synchronize a flat file data object

Viewing Data:

• Introduction to data views
• Troubleshooting configurations
• Reviewing logs
• Monitoring activities

Developing Mapping and Transformation:

• Mapping and transformation concepts
• Core transformations
• Developing and validating a mapping
• Using transformations in a mapping

Working with Dynamic Scheme and Mapping:

• Introduction to dynamic mapping concepts
• Developing and running a dynamic mapping
• Reviewing a mapping template

Parameters:

• Parameter concepts
• Using a parameter file
• Using a parameter set Workflow:
• Workflow concepts
• Creating a workflow
• Configuring a Workflow

Working with Applications:

• Application concepts
• Creating and deploying an application
• Stopping and redeploying an application

Mapping Monitoring and troubleshooting:

• Configuring and running a mapping in Native end
• Hadoop environments
• Execution Plans
• Monitor mappings
• Troubleshoot mappings
• Viewing mapping results

Hadoop Data Integration challenges and Performance Tuning:

• Describe challenges with executing mappings in Hadoop
• Big Data Management Performance Tuning
• Hive Environment Optimization
• Mapping Level Tuning
• DIS Level Tuning
• Cluster Level Tuning
• Hadoop environment and cluster tuning