Data Integration and ETL Processes

Understanding ETL (Extract, Transform, Load) Difference between ETL and ELT Overview of common ETL and ELT workflows and use cases

Key ETL tools: Informatica, Talend, Apache Nifi Tool-specific features and strengths Selecting the right tool for various data requirements

Principles of ETL process design Data extraction techniques from sources like databases, files, and APIs Structuring the transformation pipeline: cleaning, validation, enrichment

Data quality checks: handling nulls, duplicates, and format validation Governance principles for ETL processes Best practices in data governance

Error handling techniques within ETL processes Logging best practices to ensure traceability and auditability Tools and strategies for monitoring ETL pipelines

In the Data Integration and ETL Processes course, you will gain in-depth skills to design and manage data flows for warehousing and analytics. Key learning outcomes include: Mastering ETL and ELT Concepts: Understand the key differences between ETL (Extract, Transform, Load) and ELT and apply these methodologies to various data projects. Hands-on with ETL Tools: Get familiar with popular ETL tools like Informatica, Talend, and Apache Nifi, learning when and how to use each based on data requirements. Effective ETL Process Design: Learn to design efficient ETL workflows, including data extraction techniques from diverse sources such as databases, files, and APIs, alongside data transformation (cleaning, validation, enrichment). Ensuring Data Quality and Governance: Conduct data quality checks and understand governance practices to maintain data integrity and compliance. Advanced Error Handling and Logging: Build robust error-handling mechanisms and implement logging for data pipeline transparency, auditability, and monitoring.

Data Engineers and ETL Developers seeking to advance their expertise in data integration processes and improve efficiency and quality within ETL workflows. Data Warehouse Specialists who need a deeper understanding of ETL tools and best practices for data governance and quality management. Database Administrators and BI Analysts looking to expand their skills in ETL processes, data quality handling, and data integration from multiple sources. IT Professionals with a foundational knowledge in data warehousing who want to strengthen their skills in data extraction, transformation, and loading for enterprise data solutions.