
Advanced Big Data Engineering
This course delves into advanced concepts and tools for managing complex big data systems. It focuses on optimizing distributed storage, stream processing, and big data frameworks like Apache Spark. Students will learn to design scalable ETL pipelines, automate data workflows, and implement machine learning models on big data. The course also covers cloud-based big data solutions, data governance, security, and emerging trends like data lakehouses and quantum computing.
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Course Duration:
36 hours
Level:
Intermediate to Advanced

Course Objectives
Optimize distributed storage systems and query performance
Master Apache Spark for large-scale data processing
Automate and orchestrate ETL pipelines with Apache Airflow
Design real-time data processing pipelines using Apache Kafka and Flink
Build scalable cloud-based big data solutions
Implement advanced big data analytics with tools like Presto and Druid
Develop and deploy machine learning models on big data
Ensure data governance, security, and compliance
Optimize performance in big data systems
Explore emerging trends like data lakehouses and quantum computing
Prerequisites
Proficiency in distributed computing tools (e.g., Apache Spark, Hadoop)
Experience with ETL pipelines and cloud platforms
Strong programming skills in Python or Java
Familiarity with containerization (Docker) and orchestration (Kubernetes)
