VALUE TYPE: COST SAVINGS
Cybersecurity company running a complex big-data infrastructure
Cutting Big Data Costs by ~40%
// At a Glance
Area
Big Data / Apache Spark
Implementation Time
~18 hours
Diagnosis Time
~2 hours
Result
40%
in cloud cost
THE SITUATION
What Was Running, and What Raised the Flag
The company ran dozens of Spark-based workloads that cost roughly $100,000 a month in cloud spend, and the bill kept climbing. Workload volume hadn't meaningfully changed, and the internal team had already tried several rounds of optimization on its own. Each attempt made things worse, not better. They brought in Jutomate to run a focused assessment.
WHAT WE FOUND
The Configuration, Process, or Architecture Behind the Issue
Suboptimal configuration of the Spark environment.
Infrastructure settings that no longer matched real usage patterns.
Gaps in monitoring that made it hard to see where resources were actually being spent.
Processes that increased resource consumption without real business value.
DIAGNOSIS & IMPLEMENTATION TIME
The core drivers were identified within the first couple of hours. About 18 additional hours went into fixing the configuration, infrastructure, and monitoring, and tracking costs until they stabilized.
WHAT WE CHANGED
The Actions Taken
Improved the Spark environment's configuration for more efficient resource use.
Corrected infrastructure settings.
Stood up real-time monitoring and alerting.
Supported the team through stabilization and validated the results.
The Value Created
A roughly 40% reduction in the big data environment's monthly costs.
The new monitoring let the team catch anomalies in real time.
The team shifted from reacting to costs after the invoice to managing spend proactively.
BOTTOM LINE
Before investing in more infrastructure or switching technology, it's worth confirming the existing environment is configured, monitored, and managed correctly.
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