Optimising Big Data Query Frameworks for Efficiency
A leading organisation in the cyber security sector partnered with Skillfield to resolve critical data processing delays impacting threat detection. Skillfield delivered a tailored solution that not only addressed their immediate challenges but also future-proofed operations with a robust, scalable and efficient framework, ensuring the organisation could meet its evolving data processing demands.
The Problem
A leading organisation in the cyber security sector faced critical challenges in processing vast amounts of data efficiently. Their existing data processing infrastructure, built on S3 storage, lacked the advanced capabilities of big data systems like Hadoop. As a result, executing complex queries, particularly those involving joins, was time-consuming and resource-intensive. These inefficiencies caused delays in threat analysis, limiting the client’s ability to respond swiftly to potential security threats. Ultimately, the inability to query their large datasets effectively hindered their operational capabilities and decision-making processes.
The Solution
Skillfield partnered with the client to optimise their big data query framework, starting with a thorough assessment of the current setup to identify performance and scalability bottlenecks. The team implemented key changes to transform the system:
- Data Architecture Optimisation: Skillfield restructured the data architecture, introduced data partitioning to improve query performance and facilitate streamlined data access
- Query Re-engineering: Resource-intensive queries were re-engineered and fine-tuned to enhance efficiency and scalability.
- Incremental Query Capability: Skillfield developed an innovative incremental query processing capability. This approach allowed extremely large datasets to be processed in smaller, manageable increments, significantly boosting system performance.
- Suspend & Resume Functionality: The incremental query capability was paired with a suspend and resume feature integrated into the Kubernetes framework. This enhancement enabled preemptive scheduling of high-priority jobs, optimising resource allocation and ensuring responsiveness during peak workloads.
The Outcome
Our client experienced transformative improvements in their data processing capabilities:
- Enhanced Efficiency: The optimised framework allowed for faster processing of large data volumes, even when executing complex queries. Tasks that previously took hours were completed in a fraction of the time.
- Improved Threat Detection: By enabling rapid querying and analysis, the new system supported faster identification and response to cyber security threats.
- Scalability and Reliability: The new architecture and query capabilities positioned our client to handle growing data volumes without compromising performance.
