Elasticsearch and OpenSearch reward query-shaped data, deliberate mappings, controlled shard counts, and operational discipline. When teams treat them like SQL databases or generic log buckets, the problems compound — quietly, until they don't.
OpenSearch still shares a lot of operational DNA with Elasticsearch 7.x, but migrations, plugin compatibility, security models, managed-service behavior, and the diverging long-term ecosystems all need deliberate planning. We have moved clients in both directions and the answer is never "just flip the switch."
Top Elasticsearch & OpenSearch Mistakes
- Treating Elasticsearch like a SQL database instead of designing query-shaped documents.
- Creating shards, aliases, and indexes like they're free. They aren't.
- Letting dynamic mappings, field counts, and templates grow with nobody reviewing them.
- Using aggregations, wildcard searches, and deep pagination without ever measuring fan-out.
- Buying time with more nodes before fixing schema, retention, and the shape of the workload.
Where We Usually Start
- Cluster topology, node roles, heap, CPU, storage, and recovery behavior.
- Shard count, shard size, index count, replica strategy, and allocation rules.
- Mappings, templates, datastreams, ILM, retention, and field cardinality.
- Slow logs, hot queries, aggregation pressure, indexing pressure, and task history.
- Upgrade, migration, rollback, and validation constraints.
What We Do
Cluster Architecture & Design
We design Elasticsearch and OpenSearch clusters around the actual workload — not a generic reference architecture. That means looking at:
- Cluster sizing and node configuration
- Shard allocation strategies that match query patterns
- Index design and data modeling
- Network topology and data center placement
- Multi-cluster setups for disaster recovery
- Cloud-native deployments (AWS, Azure, GCP)
- Elastic Cloud, Amazon OpenSearch Service, and self-managed tradeoffs — honestly
High Availability & Reliability
Make the cluster survive things that will absolutely happen:
- Master node configuration and quorum settings
- Replica strategies for data redundancy
- Cross-zone and cross-region replication
- Disaster recovery planning that's actually been tested
- Backup and restore strategies
- Cluster health monitoring and alerting
Performance Optimization
Find the actual bottleneck. Fix it. Don't just throw nodes at it:
- JVM heap sizing and GC tuning
- Thread pool configuration
- Index settings (refresh interval, translog, etc.)
- Shard sizing and count
- Query performance analysis and tuning
- Resource allocation and capacity planning
Cluster Operations & Maintenance
The unglamorous work that keeps you off the pager:
- Cluster upgrade and migration strategies
- Index lifecycle management (ILM)
- Rolling restarts and zero-downtime maintenance
- Security hardening and access control
- Monitoring, logging, and alerting setup
- Performance troubleshooting and root-cause analysis
Version Migration & Upgrades
Major upgrades are where teams get surprised. We plan them so you don't:
- Pre-upgrade assessment and planning
- Breaking changes analysis and mitigation
- Rolling upgrade procedures
- Full cluster restart upgrade strategies
- Post-upgrade validation
- Rollback plans that actually work
- Elastic to OpenSearch (or back) compatibility assessment
Elastic Cloud & Managed Services
Managed services are great until you hit their edges. We help you avoid the edges:
- Elastic Cloud account setup and configuration
- Deployment sizing and capacity planning
- Multi-region and cross-cloud deployments
- Cloud-native features and integrations
- Cost optimization and right-sizing
- Migration from self-managed to Elastic Cloud
- Hybrid architectures (on-prem + cloud)
Docker & Kubernetes Deployments
Run Elasticsearch on Kubernetes without re-learning the worst lessons publicly:
- Docker containerization and image optimization
- Kubernetes StatefulSets and operators (ECK)
- Helm chart development and customization
- Persistent volume management and storage classes
- Service discovery and networking
- Resource limits and pod scheduling
- Autoscaling strategies (HPA, VPA, cluster autoscaler)
- Multi-zone and multi-cluster Kubernetes setups
- Monitoring and logging inside containerized environments
Index Templates & Component Templates
Templates that work the same in six months as they do today:
- Index template design and versioning
- Component templates for reusable config
- Template composition and inheritance
- Dynamic template patterns and matching
- Mapping templates for consistent field definitions
- Settings templates for index configuration
- Alias management and rollover
- Template precedence and conflict resolution
Security & Access Control
Lock it down without making the cluster unusable:
- Elasticsearch Security (X-Pack) configuration
- User authentication (native, LDAP, AD, SAML, OIDC)
- Role-based access control (RBAC) design
- Index-level and document-level security
- Field-level security and data masking
- API key management and rotation
- SSL/TLS certificate management
- Network security and firewall configuration
- Audit logging and compliance requirements
- Multi-tenancy and data isolation strategies
Kibana Dashboards & Visualizations
Dashboards people actually open during incidents:
- Kibana dashboard design and development
- Custom visualizations (Lens, Vega, Timelion)
- Data tables and pivot configurations
- Time series and trend analysis
- Geographic visualizations and maps
- Dashboard sharing and embedding
- Saved object management and versioning
- Dashboard performance optimization
- Custom Kibana plugins and extensions
Why Work With Us
- 12+ years in production: We have designed and rescued Elasticsearch, Elastic Cloud, and OpenSearch clusters across plenty of industries and workload shapes.
- Real clusters, not slides: Our recommendations come from clusters running millions of documents and thousands of queries per second — under real failure conditions.
- We look at the whole system: Architecture, performance, security, and operations. Fixing one in isolation usually breaks another.
- We right-size your spend: Performance and cost aren't opposing forces if you set the cluster up right.
- Knowledge transfer is part of the job: When we're done, your team understands every decision and can keep running the cluster without us.