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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.

Course Duration:

36 hours

Level:

Advanced

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)

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