Rating 3.82 out of 5 (577 ratings in Udemy)

• This course provides a high level approach to implement an ETL framework in typical Data Warehouse environments. This approach can be used for a new application that needs to design and implement ETL solution which is highly reusable with data loading, error handling, audit handling, job scheduling and re-start-ability features. This framework will help reduce time and increase quality due to high re-usability and design standards.
• Metadata Categories, learn the commonly used types of metadata in a real time project and how these are different from the Business and Technical viewpoints.
• ETL Framework process flow, the process flow and different activities which should be taken care during the ETL framework implementation from file (source data) validations, Exception handling and Audit Control.
• Data Sourcing, the different types of Data Sourcing possible in a Data Warehouse environment, different mechanisms in which the data sourcing can happen like the Scheduled events, Change Data Capture, Pub- Sub, Web services/API connectivity and the classification.
• Different commonly required/used scripts for Data Sourcing, the different validations required to be performed for Data Sourcing and what functionality to be included in the scripts (shell/bat).
• File Validation process, post file validation steps and file validation failure notifications.
• Staging Layer, the need for staging layer, Reference Data, Audit columns for Staging and Reference tables, Data retention in the staging layer, partitions and DB standards.
• Business Validation Layer, different situations possible during the data processing, concurrent workflow process, partitions in staging and business validation layer.
• Data warehouse Layer, Dimension Load, Fact Load types/process, Fact partitions, Fact Summary Load and Source File Management/Archival.
• Exception Handling/Error Handling, Data model for exception handling, Error Category, Error Code and different possible solutions for exception handling.
• Sample Project Setup, Steps to download the project setup, executing the DDLs for metadata, project explanation and importing the code base into Informatica.
• Extending the Operational Metadata’s Data Model for exception handling with additional supporting tables.
• Error Handling Data Model, the framework for the data model design.
• Using PMREP tables, for exception handling.
• Audit, Balance and Control, the need, different technology components involved, table structure and data model, workflow example.
• Configuration Management, Software Change Management, Identification, Tracking and Management of all the assets/objects of a project, One of the standard project management processes, the formal way for managing changes of the software and the process for deploying code from development to testing to production.