Optimizing Spark and Cloud Data Storage

You will master advanced performance optimization techniques for large-scale data processing using Apache Spark and cloud storage technologies. In this hands-on course, you'll learn to diagnose and resolve performance bottlenecks that plague distributed data systems, implement strategic partitioning and caching strategies that can improve job performance by 30% or more, and design secure, cost-effective cloud data infrastructure. You will gain expertise in transactional data lake technologies like Delta Lake, evaluate storage formats to optimize analytical workloads, and provision enterprise-grade cloud infrastructure with proper security controls. Through practical exercises, you'll analyze Spark execution plans, implement data versioning and ACID transactions, and benchmark different storage formats to make informed architectural decisions. By the end, you will have the skills to optimize data pipelines at scale, reduce cloud storage costs through intelligent format selection, and build robust data infrastructure that meets enterprise security requirements. This expertise directly addresses the performance challenges faced by data engineers working with petabyte-scale datasets in production environments

• Optimize Spark job performance through strategic partitioning and caching, achieving over 30% runtime improvements
• Implement transactional data lakes with Delta format supporting versioning, ACID operations, and schema evolution
• Design secure cloud infrastructure using IAM policies, private networks, and encrypted storage
• Evaluate and benchmark storage formats (Parquet, ORC, Avro) for analytical workloads and cost efficiency

• Basic computer and internet skills
• Ability to read course instructions in English and complete short practice activities