https://data-trenches.leandrof.space/tags/big-data/Recent content in Big Data on Data TrenchesHugo -- gohugo.ioen-usleandrojlfernandes@gmail.com (Leandro Fernandes)leandrojlfernandes@gmail.com (Leandro Fernandes)Sat, 17 Feb 2024 00:00:00 +0000https://data-trenches.leandrof.space/projects/pyspark-optimization/Sat, 17 Feb 2024 00:00:00 +0000leandrojlfernandes@gmail.com (Leandro Fernandes)https://data-trenches.leandrof.space/projects/pyspark-optimization/<h2 id="the-challenge">The Challenge</h2> <p>Handling massive-scale data processing while maintaining reasonable query latency and managing compute resource costs in a distributed environment.</p> <h2 id="the-solution">The Solution</h2> <p>Architected distributed processing jobs using PySpark with multiple optimization strategies:</p> <ul> <li>Algorithmic improvements to reduce computational complexity</li> <li>Storage optimization using Trino and Hive</li> <li>Query execution plan optimization</li> <li>Resource allocation tuning</li> <li>Data partitioning strategies</li> </ul> <h3 id="technologies-used">Technologies Used</h3> <ul> <li>PySpark</li> <li>Apache Hadoop</li> <li>Trino</li> <li>Hive</li> <li>Distributed Systems</li> </ul> <h2 id="impact">Impact</h2> <ul> <li><strong>25% reduction</strong> in query latency</li> <li><strong>25% decrease</strong> in resource consumption</li> <li>Improved processing efficiency for massive datasets</li> <li>Significant cost savings on compute resources</li> </ul> <p>This optimization effort required deep understanding of distributed systems, Spark internals, and data storage patterns to achieve measurable performance gains.</p>