Overview

This course is designed to provide an in-depth understanding of data engineering principles and technologies. Covering six critical sections, it ensures a structured approach to mastering key concepts, tools, and techniques used in real-world data engineering environments.1. Foundations of Data EngineeringLearn the fundamental aspects of data engineering, including data storage types (structured, semi-structured, and unstructured), data processing methodologies (batch vs. stream processing), and fundamental database concepts.2. Data Modeling and WarehousingUnderstand database normalization, entity-relationship modeling, indexing, and partitioning. Explore data warehousing concepts, including star and snowflake schemas, OLAP vs. OLTP, and the role of data marts in enterprise analytics.3. ETL and Data PipelinesGain practical insights into Extract, Transform, Load (ETL) processes, data ingestion techniques, and workflow orchestration tools like Apache Airflow and Prefect. Learn how to handle real-time data movement and transformations efficiently.4. Big Data Technologies and FrameworksDive into distributed computing and big data processing using Hadoop, Spark, and Kafka. Understand how these tools help in processing, streaming, and managing large datasets in scalable environments.5. Cloud Data EngineeringExplore cloud-based data engineering solutions, including AWS Redshift, Google BigQuery, and Azure Synapse. Understand cloud storage, data lakes, and the use of managed services for data engineering workflows.6. Performance Optimization and Best PracticesLearn strategies to optimize data pipelines, improve query performance, and manage costs effectively. Understand best practices for data governance, security, and compliance in modern data engineering.

Skills

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