Data Engineering: Fundamentals of Data Engineering and ETL Processes

In this first session of DataGrooves' Data Engineering series, we dive into the foundational concepts of data engineering, focusing on the ETL (Extract, Transform, Load) process. Learn how raw transactional and CRM data from databases like NoSQL and relational sources are ingested into data lakes and transformed into structured data warehouses for business intelligence and AI applications. Key points covered: Overview of ETL and ELT processes in modern data engineering Differences between data lakes and data warehouses (e.g. Snowflake) Data formats often encountered: JSON, XML, text logs, and transactional data Using Pandas for data manipulation: loading, cleaning, handling missing data, and managing duplicates Essential data validation and quality checks to ensure reliability How clean, well-structured data supports machine learning, AI agents, and business analytics Timestamps: 0:00 Introduction to ETL and data engineering basics 2:10 Types and sources of data (transactional, CRM, NoSQL, etc.) 5:00 The role of data lakes and data warehouses 7:20 Using business intelligence tools and Pandas for data analysis 11:00 Data cleaning techniques in Pandas 14:30 Handling missing data and duplicates 18:00 Data validation and quality checks 21:15 Summary and importance for AI agents Explore more sessions in the Agentic AI series at DataGrooves and start your free data science and agentic AI upskilling journey. Visit https://datagrooves.com to join our free programme and discover master-level courses in data engineering and machine learning.