Telemetry Lake Optimization for an Enterprise Security Team

The situation

A Fortune 500 technology company’s enterprise security team ran a large-scale telemetry data lake used to detect threats, investigate incidents, and monitor system health across a global infrastructure footprint. The data platform was expensive and unreliable: roughly half of all processing jobs were failing, compute costs were high relative to the work being done, and the engineering team had no clear mechanism to tell whether a given failure was caused by a platform limit or a workload written incorrectly.

The security team had significant Databricks and Azure expertise internally, but the failure rate had persisted long enough that it was clear the root cause analysis needed an outside view.

What we built

CorrDyn’s engagement started with a workload audit rather than a solution. We reviewed job configurations, failure logs, and cluster utilization data to build a picture of where costs were accumulating and where failures were originating. That audit produced two parallel tracks of work.

The first was data reliability work: we designed and implemented failure classification logic that tagged each job failure with a root cause category — platform issue, user-origin issue, or ambiguous. This gave the operations team a clear signal about where to invest. Platform failures pointed to infrastructure configuration; user-origin failures pointed to workload patterns that engineers needed to change.

The second was cost optimization: we right-sized cluster configurations across the workload inventory, eliminating over-provisioned compute that was sitting idle, and consolidated workloads where separate clusters were running jobs that could share resources without contention. No capability was removed. The 30% cost reduction came entirely from eliminating waste the audit had quantified.

Once the failure taxonomy was established, we designed a Lunch and Learn training series for the engineering organization. Rather than covering Databricks best practices in the abstract, each session was built around failure patterns that were actually occurring in the environment. Hundreds of engineers participated across the series.

What changed

Pipeline success rates improved from 50% to 90%. The training program drove a 40% reduction in failures, with engineers changing the specific workload patterns that the classification system had identified as the primary user-origin failure modes. Cluster right-sizing reduced compute spend by 30% without any reduction in analytical throughput. The security team retained the failure classification framework as an ongoing operational tool, so new failure patterns could be diagnosed and addressed before they accumulated into systemic reliability problems.

The engagement was an optimization and enablement project, not a rebuild. The client’s platform remained intact; the work was to help the existing team get more out of what they had already built.