Logstash Pipeline Optimization for a Canadian Multinational Bank
Client
The client is one of Canada's largest multinational financial institutions, operating across North America and global markets. Serving over 12 million customers, they manage services in personal and corporate banking, capital markets, and digital banking infrastructure.
Project Context
The client’s Logstash pipelines were inconsistently parsing more than 50 million system events per day, affecting ingestion rates, processing times, and reliability of analytics in Elasticsearch.
Challenges
- Complex conditional logic and regex issues
- Inconsistent field mappings and schema drift
- Pipeline duplication and lack of standardization
Solution
- Reviewed grok patterns, field naming, and plugin use
- Provided 30 structured observations (quick wins to strategic fixes)
- Collaborated with platform team for implementation rollout
Project Objectives
- Conduct audit of existing Logstash filters
- Identify parsing inefficiencies
- Improve maintainability and align pipeline structure with Elasticsearch and Kibana
Solution Delivery
SquareShift engineers conducted line-by-line audits and refactored the Logstash pipeline structure for improved parsing accuracy, lower memory usage, and better performance.
Testimonial
SquareShift streamlined our pipeline structure, making observability faster and more efficient
