Overview of Informatica with Big Data (BDM)
This Training helps participants in learning how to work with ETL tool in Hadoop Environment using traditional RDBMS tools, Hadoop ingestion, and how to optimize the data warehouse processing amd mapping execution on to a Hadoop cluster etc…
Duration
3 Days
Prerequisite for Informatica with Big Data (BDM)
Good knowledge of how Data works.
Course Outline for Informatica with Big Data (BDM)
Module 1 – Introduction – Informatica Big Data Edition
- 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
Module 2 – 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
Module 3 – Viewing Data
- Introduction to data views
- Troubleshooting configurations
- Reviewing logs
- Monitoring activities
Module 4 – Developing Mappings and Transformations
- Mapping and transformation concepts
- Core transformations
- Developing and validating a mapping
- Using transformations in a mapping
Module 5 – Working with Dynamic Schema and Mappings
- Introduction to dynamic mapping concepts
- Developing and running a dynamic mapping
- Reviewing a mapping template
Module 6 – Parameters
- Parameter concepts
- Using a parameter file
- Using a parameter set
Module 7 – Workflows
- Workflow concepts
- Creating a workflow
- Configuring a Workflow
Module 8 – Working with Applications
- Application concepts
- Creating and deploying an application
- Stopping and redeploying an application
Module 9 – Mappings, Monitoring, and Troubleshooting
- Configuring and running a mapping in Native end
- Hadoop environments
- Execution Plans
- Monitor mappings
- Troubleshoot mappings
- Viewing mapping results
Module 10 – 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