Introduction
Today, ensuring data quality is paramount for organizations to make informed decisions and drive business success. Our client, seeking to automate data quality assessment across various data stores, approached us to develop a sophisticated web application, OwlDQ, capable of comparing and scoring data using Spark-based jobs. This success story outlines our journey in leveraging AWS Cloud infrastructure to host OwlDQ and overcome performance challenges, ultimately enhancing data quality management for the client.
Analyzing the Problem
The client faced challenges in manually assessing data quality across multiple data stores, leading to inefficiencies and inaccuracies in decision-making. Existing solutions lacked sophistication and automation, requiring manual rule application for data quality assessment. Additionally, performance issues arose when processing large datasets, hindering scalability and efficiency. The client sought a scalable and automated solution to address these challenges and improve data quality management.
Initial Challenges
The initial challenges faced by the client included:
Performance Issues: Processing large datasets with millions of records posed performance challenges, leading to slow job execution and resource constraints.
Job Queuing and Overlapping: Managing job queues and preventing job overlaps proved to be a significant challenge, impacting job scheduling and execution.
Job Hangs and Service Restarts: Spark jobs frequently hung, causing service disruptions and necessitating frequent restarts, resulting in operational inefficiencies.
Deadlock Issues: Deadlocks and job hang-ups persisted despite attempts to optimize resource allocation and database maintenance.
Our Solution
We devised a comprehensive solution leveraging AWS Cloud infrastructure and OwlDQ's advanced data quality assessment capabilities:
AWS Cloud Architecture: Utilizing AWS services such as EMR, RDS, EC2, and ELB, we designed a scalable architecture to host OwlDQ and support Spark-based job execution.
Automated Data Quality Assessment: OwlDQ applies the latest advancements in Data Science and Machine Learning to automate data quality assessment without the need for manual rules.
Spark Workload Execution: OwlDQ creates and submits Spark workloads on EMR clusters to run analytical jobs and generate data quality reports between different data stores.
Performance Optimization: By optimizing RDS PostgreSQL instances, separating web and agent components, and tuning EC2 instance types, we addressed performance issues and improved job execution speed and efficiency.
Key Results Achieved
The implementation of our solution yielded significant results for the client:
Improved Performance: Turning off the auto-disable feature in RDS instances and optimizing EC2 instance types resulted in a 32% improvement in job performance.
Cost Reduction: The optimized architecture and resource allocation led to a 15% reduction in operational costs while maintaining performance levels.
Enhanced Reliability: By addressing deadlock issues and optimizing job queues, we improved system reliability and reduced service disruptions.
Scalability: The scalable AWS Cloud architecture enabled seamless scaling to handle large datasets and increased workload demands.
Conclusion
Through our partnership with the client and leveraging AWS Cloud infrastructure, we successfully addressed performance challenges and revolutionized data quality management with OwlDQ. This success story highlights the transformative impact of automated data quality assessment and scalable cloud infrastructure in driving operational efficiency and decision-making accuracy for organizations in today's data-driven world.
