Modern Data Engineering 

Who Should Attend?


Duration: 3 Days

Data engineering is a crucial part to enable and operationalize big data analytics and its applications in the big data ecosystem. Modern data engineering ensures fast, secure, and high-quality implementation of new systems that streamline operations and reduce costs with minimal workforce interruption. It provides an extensible, highly scalable set of tools to access, transform, and integrate data from any business system. This course is designed for anyone who wants to perform data integration and management tasks. Participants work on projects to monitor the process and database changes. In addition, participants would also learn about how big data technology is able to help IT to reduce hardware dependencies and software management.

  • Understand and execute the concept of Extract, Transform and Load (ETL)
  • Perform data integration process and manage the tasks given
  • Monitor the process and database changes
  • Understand the importance of big data technology

1. What is Data Engineering?
• Overview
• Data Engineering Skillset
• Data Engineering Roles

2. Joining data sources
▪ Creating metadata
▪ Joining data sources
▪ Capturing rejects
▪ Correcting a lookup

3. Filtering data
▪ Filtering output data
▪ Using multiple filters

4. Data Engineering Execution
• Working with files
• iBasic transformation (Join, Filter,
Expression Editor)
• Using context variables
• Error Handling
• Working with Databases
• Working with web services
• Master Job
• Documenting a Job
• Remote Repository and Execution
• Resource usage and basic
• Activity Monitoring Console (AMC)
• Parallel Execution
• Joblets
• Creating a Unit Test
• Change Data Capture (CDC)

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