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 I Fix

Slow search and aggregations

Queries are timing out, dashboards are dragging, exports are punishing the cluster, or adding nodes is not helping.

Indexing and ingestion bottlenecks

Batch jobs, Kafka pipelines, Beats, Fluent Bit, or Logstash are backing up and nobody can tell where the pressure starts.

Bad schema, mappings, and templates

Elasticsearch is not a SQL database. Shards are not tables, indexes are not free, and mapping drift becomes operational debt.

Cluster instability

Heap pressure, GC, circuit breakers, shard allocation failures, recovery storms, or recurring red and yellow cluster states.

Observability cost growth

Log volume, high-cardinality fields, duplicate telemetry, long retention, and overlapping tools are driving costs up every month.

Migration and upgrade risk

Elastic Cloud, self-managed Elasticsearch, OpenSearch, version upgrades, blue-green migrations, and zero-downtime planning.

Solution Areas

Elasticsearch & OpenSearch Rescue

Diagnose slow, unstable, or expensive clusters and separate symptoms from structural causes: topology, shards, heap, recovery behavior, mappings, and workload shape.

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Query & Index Performance

Reduce search latency, aggregation pressure, query fan-out, export bottlenecks, indexing lag, and resource waste without defaulting to larger clusters.

Schema, Mapping & Data Modeling

Repair mapping debt, template drift, field explosion, oversharding, SQL-shaped data models, and index designs that do not match query patterns.

Observability Architecture

Design practical boundaries across logs, metrics, traces, Elastic Observability, Prometheus, Grafana, Datadog, Fluent Bit, and OpenTelemetry.

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Cost Control & Retention

Reduce duplicate logs, high-cardinality data, unnecessary ingestion, long retention, inefficient storage tiers, and overlapping observability spend.

Migration, Upgrade & Cloud Modernization

Plan Elastic Cloud moves, OpenSearch assessments, version upgrades, blue-green cutovers, reindexing, validation, and rollback strategies.

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Common Failure Patterns

Top Elasticsearch & OpenSearch Mistakes

  1. Treating Elasticsearch like a SQL database.
  2. Creating too many shards and indexes because they feel cheap at first.
  3. Letting mappings, dynamic fields, and templates grow without ownership.
  4. Using expensive queries, aggregations, and pagination patterns without measuring fan-out.
  5. Adding nodes before fixing data modeling, retention, and query shape.

Top Observability Misses

  1. Treating logs, metrics, and traces as interchangeable.
  2. Sending high-cardinality and duplicate telemetry everywhere.
  3. Building dashboards that do not help during incidents.
  4. Alerting on symptoms without clear ownership or runbooks.
  5. Keeping overlapping Elastic, Datadog, Prometheus, Grafana, and cloud-native data with no cost boundary.

Our Expertise

NosqlRevolution LLC helps engineering, platform, and SRE teams get control of Elasticsearch, OpenSearch, and observability systems that have become hard to reason about.

With over 12 years of hands-on production experience, we focus on practical diagnosis and implementation: what is failing, why it is failing, what to do first, and what architecture will hold up over the next 12 to 24 months.

Senior Operator Judgment

Our extensive experience includes:

  • Designing and rescuing Elasticsearch and OpenSearch clusters under real production pressure
  • Untangling schema, mapping, shard, index template, ILM, and data modeling problems
  • Optimizing query, aggregation, indexing, and recovery behavior for high-throughput workloads
  • Planning safe migrations across Elastic Cloud, self-managed Elasticsearch, OpenSearch, and Kubernetes environments
  • Reducing observability cost while preserving the signal teams need during incidents
  • Building practical monitoring, alerting, dashboard, and runbook patterns for operations teams
Elasticsearch & OpenSearch Core: Cluster design, shard allocation, node configuration, high availability setups, and cross-version migrations
Query & Index Optimization: Advanced query tuning, aggregation optimization, index design patterns, and search performance analysis
Performance Tuning: JVM optimization, thread pool configuration, resource management, and bottleneck identification
Index Templates & Datastreams: Automated index management, lifecycle policies, data retention strategies, and ILM implementation
Observability Stack Architecture: Elastic Observability, APM, RUM, Fluent Bit, Prometheus, Grafana, Datadog, OpenTelemetry, alerting, and cost control
Cluster Operations: Backup and restore strategies, security hardening, monitoring and alerting, and operational best practices

Start With The Problem

Tell me what is slow, unstable, expensive, or hard to explain. I will help you identify the likely failure mode and the best first step.

Or email us directly at cbrown@nosqlrevolution.com