Introduction
In the aviation industry, efficient data processing is essential for managing operations, analyzing data, and making informed decisions. Our client, a leading aviation company, relied on EMR clusters to run ingestion, analytics, and data science jobs. However, the high cost of EMR EC2 instances and the need for better Spark performance posed challenges. This success story outlines our approach to cost optimization and performance improvement for the client's EMR clusters using AWS Graviton Processor.
Analyzing the Problem
The client faced challenges related to cost optimization and Spark performance in their EMR clusters:
High EC2 Instance Costs: Running multiple EC2 instances for EMR clusters led to increased expenditure, impacting the client's budget.
Suboptimal Spark Performance: Inefficient Spark performance hindered data processing speed and responsiveness, affecting business agility and decision-making.
Initial Challenges
The primary challenges the client faced included:
High Operational Costs: The extensive use of EC2 instances resulted in substantial monthly expenses.
Performance Issues: The existing setup was not providing the desired response times for Spark jobs, impacting overall efficiency and business operations.
Scalability: While the system was scalable, the associated costs and performance trade-offs were becoming unsustainable.
Our Solution
We implemented a solution leveraging AWS Graviton Processor and optimized Spark performance:
Adoption of AWS Graviton Processor: We leveraged AWS Graviton Processor designed for EMR cloud workloads to optimize cost and performance.
Enhanced Business Agility: Graviton provided exceptional business agility by connecting applications to a modern and agile approach for software development and infrastructure.
Improved Spark Performance: Apache Spark was performance-optimized on Amazon EMR cluster version release 5.28.0 and later, enhanced Spark performance on Graviton.
Cost Reduction: Migrating to the Graviton platform resulted in a 30% reduction in cluster costs while maintaining or improving performance levels.
Key Results Achieved
The implementation of Graviton Processor and Spark optimization yielded significant benefits for the client:
Cost Reduction: Migrating to the Graviton platform led to a 30% reduction in the cost of EMR clusters, resulting in significant cost savings for the client.
Performance Improvement: Spark performance increased by 15% on Graviton processors, enhancing data processing speed and responsiveness.
Business Agility: The adoption of Graviton Processor and Spark optimization improved business agility, scalability, and time-to-market for the client's data processing tasks.
Conclusion
Through our partnership with the leading aviation client and leveraging AWS Graviton Processor, we successfully addressed cost optimization and performance improvement challenges for their EMR clusters. This success story highlights the transformative impact of adopting innovative technologies and optimizing cloud workloads to drive cost savings, performance enhancements, and business agility for organizations in the aviation industry.
