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.

Optimizing Spark and Cloud Data Storage for Analytics

Optimizing Spark and Cloud Data Storage for Analytics
This course is part of Open source Data Engineering with Spark, dbt & Airflow Professional Certificate

Instructor: Professionals from the Industry
Access provided by PALC Dev
Recommended experience
What you'll learn
Optimize Spark job performance through strategic partitioning and caching, achieving 30%+ runtime improvements using data access analysis.
Implement transactional data lakes with Delta format, enabling versioning, ACID operations, and schema evolution for reliable datasets.
Provision secure cloud data infrastructure using IAM policies, private networks, and encrypted storage following security best practices.
Evaluate and benchmark storage formats (Parquet, ORC, Avro) to select optimal solutions for analytical workloads and cost efficiency.
Skills you'll gain
Tools you'll learn
Details to know

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March 2026
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There are 11 modules in this course
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Felipe M.

Jennifer J.

Larry W.

Chaitanya A.
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