Representative consulting outcomes

96% duplicate data reduction
50% Elastic Cloud cost reduction
50% lower search latency
0 downtime major upgrades
24 mo. without unplanned outages

Problems We Fix

Slow search and aggregations

Queries timing out, dashboards crawling, exports pinning the cluster, and adding nodes hasn't helped. It rarely does — the shape of the work is usually the problem.

Indexing and ingestion bottlenecks

Kafka, Beats, Fluent Bit, or Logstash are backing up and no one can point to where the pressure actually starts. We trace it end-to-end and tell you.

Bad schema, mappings, and templates

Elasticsearch isn't a SQL database. Shards aren't tables, indexes aren't free, and dynamic mapping drift compounds every week you ignore it.

Cluster instability

Heap pressure, GC stalls, tripped circuit breakers, allocation failures, recovery storms, recurring yellow or red. We have debugged all of these in production. None of them are mysterious once you know where to look.

Observability cost growth

Log volume up and to the right, high-cardinality fields, duplicate telemetry, retention nobody owns, and three overlapping tools billing you for the same data.

Migration and upgrade risk

Elastic Cloud moves, self-managed to OpenSearch, major version jumps, blue-green cutovers. We have done these without downtime — yours can go the same way.

Solution Areas

Elasticsearch & OpenSearch Rescue

We dig into slow, unstable, or expensive clusters and separate symptoms from structural causes: topology, shards, heap, recovery behavior, mappings, and the shape of the workload itself.

Learn More →

Query & Index Performance

Cut search latency, aggregation pressure, query fan-out, export bottlenecks, and indexing lag — without throwing more hardware at the problem.

Schema, Mapping & Data Modeling

Fix mapping debt, template drift, dynamic field explosions, oversharding, SQL-shaped data models, and index designs that don't match how the data actually gets queried.

Observability Architecture

Set practical boundaries across logs, metrics, and traces — Elastic Observability, Prometheus, Grafana, Datadog, Fluent Bit, OpenTelemetry — so you stop paying three vendors for the same signal.

Learn More →

Cost Control & Retention

Cut duplicate logs, high-cardinality fields, useless ingestion, overlong retention, wrong storage tiers, and observability spend that nobody owns.

Migration, Upgrade & Cloud Modernization

Plan Elastic Cloud moves, OpenSearch assessments, version jumps, blue-green cutovers, reindexing, validation, and rollback. We have done a lot of these without causing an outage.

See Packages →

Common Failure Patterns

Top Elasticsearch & OpenSearch Mistakes

  1. Treating Elasticsearch like a SQL database. It isn't one. It never will be.
  2. Creating shards and indexes like they're free. They aren't.
  3. Letting dynamic mappings and templates grow with no one owning them.
  4. Writing expensive queries, aggregations, and pagination patterns without ever measuring fan-out.
  5. Adding nodes before fixing data modeling, retention, and the shape of the queries.

Top Observability Misses

  1. Treating logs, metrics, and traces as interchangeable. They aren't.
  2. Sending high-cardinality and duplicate telemetry everywhere "just in case".
  3. Building dashboards nobody actually opens during an incident.
  4. Alerting on symptoms with no owner and no runbook.
  5. Running overlapping Elastic, Datadog, Prometheus, Grafana, and cloud-native stacks with no cost boundary.

Our Background

We help engineering, platform, and SRE teams take back control of Elasticsearch, OpenSearch, and observability systems that have gotten away from them.

With 12+ years on the keyboard in production, we focus on diagnosis and implementation, not slideware: what's failing, why, what to do first, and what architecture will still hold up 12–24 months from now.

Staff+ Operator Judgment

What we have actually done in production:

  • Designed and rescued Elasticsearch and OpenSearch clusters under real load — not lab conditions
  • Untangled schema, mapping, shard, template, ILM, and data-modeling debt that took years to accumulate
  • Tuned query, aggregation, indexing, and recovery behavior for high-throughput workloads
  • Run safe migrations across Elastic Cloud, self-managed Elasticsearch, OpenSearch, and Kubernetes
  • Cut observability cost without losing the signal teams actually need at 3 a.m.
  • Built monitoring, alerting, dashboards, and runbooks that on-call engineers thank you for
Elasticsearch & OpenSearch Core: Cluster design, shard allocation, node config, HA topologies, cross-version migrations
Query & Index Optimization: Query tuning, aggregation pressure, index design that matches access patterns, search performance analysis
Performance Tuning: JVM, thread pools, resource limits, finding the actual bottleneck instead of the obvious one
Index Templates & Datastreams: Templates, ILM, lifecycle, retention strategies that don't silently break in six months
Observability Stack Architecture: Elastic Observability, APM, RUM, Fluent Bit, Prometheus, Grafana, Datadog, OpenTelemetry, alerting, cost control
Cluster Operations: Backup/restore, security hardening, monitoring, alerting, and operational hygiene that survives staff turnover

Start With The Problem

Tell us what's slow, unstable, expensive, or hard to explain. We'll help you spot the likely failure mode and the right first move.

Or email us directly at cbrown@nosqlrevolution.com