Success Stories

Situation: Data processing was growing 50% month over month with no visibility into where it was coming from. The pipeline — ELK → Kafka → data lake → Elasticsearch — was filling disks, blowing past cost forecasts, and running hours past SLA.

What we did: Piped Kafka messages into Elastic Observability and stood up a diagnostic dashboard. It immediately showed the hotspots: ELK script processing was generating thousands of duplicates per 15-minute run.

Result: Duplicate processing cut in half within hours. Overall data volume down 96% inside a week.

Situation: Four near-identical cron jobs running every 15 minutes were hammering search and the downstream pipeline. The author had left the company. Nobody wanted to touch the code.

What we did: Used GenAI to lift the logic out of the legacy code, collapsed four jobs into one, and rewrote in a more maintainable language. While diffing the new output against the old, we found that 95%+ of the legacy output was duplicates from previous runs that nobody had ever caught.

Result: Major drop in search load and Kubernetes resource usage. Downstream: 700K–900K daily duplicate writes to Kafka, the data lake, and Elasticsearch — gone.

Situation: Several Elastic Cloud clusters were oversized — not from real load, but from a creaky index architecture nobody had time to revisit.

What we did: Moved everything to modern cloud architectures and rebuilt the index design from scratch. Worked alongside the team to nail down the new data shape so the transition was a non-event.

Result: Primary cluster went from 22 data nodes to 6. Middleware search latency dropped 50% and got noticeably more consistent. Cloud bill cut in half.

Situation: Client needed to take multiple Elasticsearch clusters to a major new version without service disruption or a maintenance window they couldn't afford.

What we did: Picked the right upgrade approach per cluster — some in-place, some blue-green. Built the plan, audited every client library version across applications and languages, and ran the cutovers with the team.

Result: 3 months end-to-end across multiple clusters. Zero downtime. Several more years of version runway in the tank.

Situation: A product team was riding a treadmill of Elasticsearch outages — recovery storms, heap pressure, allocation failures. Every incident was eating hours.

What we did: Health and stability review focused on the actual failure modes and how the cluster recovered (or didn't). Fixed shard sizing, node sizing, and lifecycle. Tightened monitoring. Left them a prioritized punch list.

Result: Zero unplanned outages in the 24 months that followed. The team got to plan capacity and upgrades instead of firefighting.

Situation: Growing engineering org with ad-hoc logging and metrics — home-grown stuff, Datadog, a couple of commercial services nobody could fully explain. Every new service added cost and cardinality. The investment wasn't paying off.

What we did: Consolidated onto Elastic Observability and shut off Datadog and the other paid services. Centralized logging and APM lifted visibility. Proactive alerting got time-to-problem-discovery down dramatically.

Result: Clear architecture and a runbook to match. New services onboard with defined patterns instead of one-offs. Per-service cost down, capability up. Overall observability spend cut more than 50%.