Case study
50 Million Daily Events Parsed Consistently for a Canadian Bank
How SquareShift audited a Canadian bank's Logstash pipelines line by line and delivered 30 structured fixes to standardize parsing across 50 million daily events.
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Client
One of Canada's largest multinational banks, serving more than 12 million customers across personal banking, corporate banking, capital markets, and digital infrastructure.
Industry
Financial Services
Services
Stack
Logstash · Elasticsearch · Kibana · Grok
How SquareShift delivered it.
The challenge
The bank's Logstash pipelines parsed more than 50 million system events a day, but inconsistently — with complex conditional logic, inconsistent field names, and duplicated logic, and no shared standard to check any of it against.
What we delivered
SquareShift engineers audited the pipeline line by line and delivered 30 structured observations, ranging from quick wins to strategic fixes. Field mappings were made consistent and duplicated logic was aligned to how Elasticsearch and Kibana actually consume the data.
The outcome
The pipeline structure is now standardized, with higher parsing accuracy and lower memory use across the more than 50 million events it processes every day. For log analytics built on Elastic, see our Elastic services.
At 50 million events a day, an inconsistent field name isn't a style complaint — it's a blind spot in the bank's own analytics. A line-by-line audit is slow work, but it's the only way to actually find every one of them.
Platform & Software Engineering Practice Lead, SquareShift
