Performance Engineering for Data Systems

Did you know that 96% of organizations experience unplanned downtime, costing an average of $5,600 per minute? This critical reality makes engineering resilient cloud data infrastructure not just a best practice—it's a business imperative. This Short Course was created to help data engineers and platform architects accomplish the mission-critical task of building cloud data warehouses that deliver both optimal ROI and bulletproof reliability. By completing this course, you'll be able to automate infrastructure provisioning with code-based deployment systems, make data-driven decisions on compute and storage configurations that maximize cost-effectiveness, and architect disaster recovery systems that protect against catastrophic failures with minimal data loss. By the end of this course, you will be able to: - Apply Infrastructure as Code (IaC) to provision a cloud data warehouse - Analyze infrastructure cost versus performance across compute and storage options - Create a cross-region disaster recovery architecture with a 15-minute Recovery Point Objective This course is unique because it combines hands-on Terraform automation with real-world TPC-DS benchmarking and enterprise-grade disaster recovery planning—skills that directly translate to building production-ready data platforms. To be successful in this project, you should have a background in SQL, basic cloud computing concepts, and familiarity with data warehouse fundamentals.

Most database schemas start simple, but as data grows and queries become complex, performance bottlenecks emerge. What separates skilled data engineers from the rest is the ability to architect schemas that scale. This Short Course was created to help data engineers and database professionals accomplish advanced schema design and optimization that directly impacts query performance and system scalability. By completing this course, you'll be able to implement DDL partitioning and clustering strategies, make informed decisions about when to denormalize for performance gains, and create professional ER diagrams that communicate complex data relationships. These are the exact skills you'll use to optimize slow-running queries and design schemas that handle enterprise-scale workloads. By the end of this course, you will be able to: - Apply partitioning and clustering strategies using SQL Data Definition Language (DDL) - Analyze the trade-off between database normalization and query performance to propose schema refactoring - Create Entity-Relationship diagrams to model and document data structures This course is unique because it combines hands-on DDL implementation with strategic schema design decisions that directly address real-world performance challenges. To be successful in this project, you should have a solid foundation in SQL querying, basic database design principles, and experience working with relational databases.

Fix Data Bottlenecks: Optimize Spark Performance Did you know that inefficient data shuffling can slow Spark jobs by over 70%? Understanding how to detect and fix these bottlenecks is essential for achieving peak performance in distributed data systems. This Short Course was created to help professionals in this field optimize data pipeline performance and eliminate processing bottlenecks in distributed Spark environments. By completing this course, you will be able to analyze Spark execution plans, identify causes of data skew and shuffle inefficiencies, and apply optimization strategies—skills that improve processing speed, scalability, and overall data workflow efficiency. By the end of this 3-hour long course, you will be able to: Analyze distributed execution plans to resolve performance bottlenecks caused by data shuffle and skew. This course is unique because it blends practical Spark debugging with real-world optimization techniques, giving you hands-on experience in diagnosing distributed performance issues and fine-tuning large-scale data operations. To be successful in this project, you should have: Basic Spark concepts SQL fundamentals Understanding of distributed computing principles Data processing experience

Did you know that a single poorly optimized SQL query can slow down an entire data warehouse, impacting dashboards, applications, and business decisions? Identifying and fixing performance bottlenecks is critical to keeping analytical systems fast and scalable. This Short Course was created to help professionals in this field master advanced SQL performance optimization techniques for maintaining scalable data warehouses and analytical platforms. By completing this course, you will be able to analyze SQL query performance, interpret execution behaviors, and diagnose bottlenecks that impact speed and efficiency—skills that enable you to optimize workloads and sustain high-performing data environments. By the end of this 3-hour long course, you will be able to: Analyze query performance to diagnose and resolve execution bottlenecks. This course is unique because it combines real-world performance tuning with deep execution analysis, giving you a practical foundation for optimizing complex SQL workloads and improving end-to-end system responsiveness. To be successful in this project, you should have: Intermediate SQL querying experience Understanding of database concepts Data warehouse experience Familiarity with execution plan basics Did you know that a single poorly optimized SQL query can slow down an entire data warehouse, impacting dashboards, applications, and business decisions? Identifying and fixing performance bottlenecks is critical to keeping analytical systems fast and scalable. This Short Course was created to help professionals in this field master advanced SQL performance optimization techniques for maintaining scalable data warehouses and analytical platforms. By completing this course, you will be able to analyze SQL query performance, interpret execution behaviors, and diagnose bottlenecks that impact speed and efficiency—skills that enable you to optimize workloads and sustain high-performing data environments. By the end of this 3-hour long course, you will be able to: Analyze query performance to diagnose and resolve execution bottlenecks. This course is unique because it combines real-world performance tuning with deep execution analysis, giving you a practical foundation for optimizing complex SQL workloads and improving end-to-end system responsiveness. To be successful in this project, you should have: Intermediate SQL querying experience Understanding of database concepts Data warehouse experience Familiarity with execution plan basics

In large-scale data engineering environments, performance issues such as slow transformations, excessive shuffle operations, and unbalanced workloads can impact analytics, reporting, and SLA commitments. This course teaches you how to analyze, diagnose, and optimize Apache Spark applications so they run faster, more efficiently, and more reliably. In this course, you’ll start by learning the fundamentals of Spark job execution, including how stages, tasks, shuffle operations, and execution plans reveal where bottlenecks occur. You’ll explore Spark’s built-in monitoring tools to interpret job behavior. From there, you’ll apply practical optimization techniques, including improving data partitioning, mitigating data skew, optimizing joins, configuring caching strategies, and choosing efficient file formats. You’ll also learn how to tune executors, memory, cores, and dynamic allocation to balance cost and performance across workloads. Learners should be familiar with basic knowledge of Python and Spark DataFrames; familiarity with JSON and SQL. This course is designed for data engineers and developers who need to diagnose and optimize Spark jobs running on large-scale distributed data pipelines. By the end, you’ll have the skills to confidently apply advanced tuning strategies, improve throughput, reduce shuffle overhead, and optimize resource usage.

Unlock the performance potential of your Apache Spark applications! This course transforms beginners into confident Spark performance optimizers who can dramatically improve job execution times and resource efficiency. This course is a direct response to industry demand, designed for the data engineer who is tired of reactive firefighting and ready to build proactively optimized, scalable systems. This Short Course was created to help data management and engineering professionals accomplish systematic Spark job optimization through strategic analysis of partitioning and caching patterns. By completing this course, you'll be able to inspect query execution plans in Spark UI, implement strategic partitioning keys that minimize data shuffling, persist intermediate DataFrames with appropriate storage levels, and validate performance improvements that you can apply immediately in your workplace. By the end of this course, you will be able to: Analyze partitioning and caching strategies to optimize Spark job performance This course is unique because it combines hands-on analysis using real Spark UI inspection with practical implementation techniques that deliver measurable performance gains – often 30% or more runtime improvements. To be successful in this project, you should have a background in basic Apache Spark concepts and data processing fundamentals.