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ELT Performance Testing in Databricks

6 Inputs
1 Hour
Beginner
scenario poster
Industry
general
Skills
approach
quality
data-understanding
data-wrangling
Tools
python
databricks

Learning Objectives

Differentiate between Volume, Load, Stress, Spike, Endurance, and Scalability testing methodologies
Implement comprehensive performance testing strategies for large-scale ELT pipelines in Databricks
Analyze query execution plans and optimize performance bottlenecks effectively
Design multi-dimensional scalability tests across data volume, users, and infrastructure
Execute systematic performance testing sequences aligned with SLA requirements
Troubleshoot complex performance issues using data-driven testing approaches

Overview

The Performance Testing Journey: Real-World Scenarios

The Crisis Scenario: It's Monday morning. Your company's ELT pipeline that processes 100GB of customer data has suddenly slowed from 2 hours to 8 hours. The data volume quietly grew to 500GB over the quarter, and your system is now missing critical business deadlines. This masterclass ensures you never face this nightmare.

Volume Testing Reality: Experience what happens when your "small" test dataset becomes production reality. Learn to handle explosive data growth from GB to TB scale using Databricks, discovering why memory management can make or break your pipeline's performance when Black Friday data volumes hit.

Load vs Stress Testing: Simulate concurrent users accessing your pipeline during peak business hours. You'll deliberately push systems beyond limits, creating realistic failure scenarios in Databricks clusters, and learn to identify breaking points before they impact production operations.

Spike & Endurance Scenarios: Handle sudden traffic spikes from viral campaigns and test 24/7 holiday processing periods. Master multi-dimensional scalability as your business grows, implementing advanced testing strategies that adapt to unexpected business demands and extended operational periods.

Your Evolution: In 6 hours, transform from someone who reacts to performance problems to someone who prevents them, armed with enterprise-grade testing methodologies and confidence to tackle any ELT performance challenge.

Prerequisites

  • Fundamental understanding of ETL/ELT concepts and data pipeline architectures
  • Basic SQL knowledge including SELECT, JOIN, GROUP BY, and aggregate functions
  • Experience with data warehousing concepts and cloud computing fundamentals
  • Previous exposure to Apache Spark or Databricks platform (basic level preferred)
  • Access to Databricks workspace and basic Python/SQL scripting capabilities
  • Understanding of performance concepts like execution time, throughput, and resource utilization