Success Stories

Situation: Data processing was increasing 50% month over month with no visibility into the source. The pipeline—ELK to Kafka to data lake to Elasticsearch—was growing at an unsustainable rate, filling disks, increasing costs, and backing up for hours past SLAs.

What we did: Implemented a service to pipe Kafka messages to Elastic Observability and created a diagnostic dashboard. The dashboard immediately revealed hotspots where ELK script processing was generating thousands of duplicates per 15-minute run.

Results: Duplicate processing cut by 50% within hours. Overall data processing reduced by 96% after one week.

Situation: Four similar cron jobs running at 15-minute intervals were generating significant periodic search load and downstream data processing. The code had been written by a developer no longer with the company, making it difficult to maintain and debug.

What we did: Used GenAI to extract the code logic, consolidate four jobs into one, and transcode to a more maintainable language. During output verification between the new and legacy jobs, we discovered that over 95% of the legacy output consisted of duplicates from previous runs.

Results: Significant reduction in search load and Kubernetes cluster resources. Downstream impact included eliminating 700,000–900,000 daily duplicate writes to Kafka, the data lake, and Elasticsearch.

Situation: Multiple Elastic clusters were oversized due to suboptimal index architecture. Competing priorities had prevented right-sizing efforts.

What we did: Migrated all clusters to modern cloud architectures and redesigned the index structure. We partnered with the team to define a new data architecture that enabled the transition.

Results: Primary cluster reduced from 22 to 6 data nodes. Middleware search latency dropped by 50% and consistency improved. Cloud service costs reduced by 50%.

Situation: The client needed to upgrade multiple Elasticsearch clusters to a major new version without downtime or service disruption.

What we did: We selected upgrade strategies based on each use case—including in-place and blue-green approaches. We defined the upgrade plan, evaluated client library versions across applications and languages, and partnered with the team to execute the migration.

Results: Migration planned and completed in 3 months across multiple clusters. Zero downtime or service disruption. Extended version runway for years.

Situation: A product team was facing recurring Elasticsearch outages: recovery storms, memory pressure, and shard allocation issues. Each incident was taking hours to resolve.

What we did: Health and stability review focused on failure modes and recovery behavior. We addressed shard sizing, node sizing, and index lifecycle. We also tightened monitoring and gave the team a prioritized remediation list.

Results: No unplanned outages in the 24 months after remediation. The team could plan capacity and upgrades with confidence.

Situation: A growing engineering org had ad-hoc logging and metrics using home-grown solutions, Datadog, and other commercial services. Every new service added cost and cardinality, but the disjointed tools provided little practical value.

What we did: Consolidated observability onto Elastic Observability, eliminating Datadog and other service costs. Centralized logging and APM increased problem visibility. Proactive alerting reduced time to problem discovery.

Results: A clear architecture and runbook. New services onboard with defined patterns. Reduced per-service costs while expanding observability capabilities. Overall costs reduced over 50% while increasing service value.