Overview

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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.

Syllabus

Spark Performance Analysis Foundation

You will discover why systematic performance analysis beats random configuration changes and master reading Spark UI metrics to identify bottlenecks.

Performance Analysis & Acceleration

You will implement partitioning and caching strategies to achieve measurable performance improvements in distributed data processing.

Analyze Spark Execution Plans

You will develop foundational skills for analyzing distributed execution plans to identify performance bottlenecks caused by data shuffle and skew patterns in Spark applications.

Resolve Performance Bottlenecks

You will apply advanced optimization strategies to resolve identified performance bottlenecks through partition tuning, broadcast joins, and configuration optimization techniques.

Apply Transactional and Versioning Features - Foundation

You will understand why transactional features are essential for data lake reliability, explore the fundamental concepts of ACID transactions and versioning, and learn how to convert existing Parquet tables to transactional Delta format.

Apply Transactional and Versioning Features - Core Application & Assessment

You will execute atomic write and delete operations with conditions, query historical table versions for audit purposes, verify rollback capabilities through version history, and demonstrate mastery through hands-on lab work and comprehensive assessment.

Cloud Security Foundations

You will understand fundamental cloud security principles, encryption methods, and access control concepts needed to provision secure data infrastructure using Infrastructure as Code.

Secure Infrastructure Implementation

You will implement secure cloud data infrastructure using Terraform, creating encrypted storage with proper access controls and network isolation that demonstrates practical application of security principles.

Storage Architecture Evaluation Foundations

You will establish foundational understanding of storage format trade-offs and begin evaluating columnar versus row-oriented approaches for analytical workloads.

Performance Benchmarking and Recommendations

You will conduct hands-on performance benchmarking of storage formats and create evidence-based recommendations that mirror professional data engineering decision-making processes.

Project: Optimizing Spark and Cloud Data Storage for Analytics

You will create a comprehensive data infrastructure optimization project that integrates Spark performance tuning, cloud security provisioning, and storage architecture evaluation. This project synthesizes distributed computing optimization, cloud infrastructure design, and data warehousing principles into a realistic enterprise solution.