OpenSearch consulting and hands-on support

OpenSearch is an open-source search and analytics engine used to index, query, and analyze large volumes of data such as logs, events, and application documents. It’s commonly used by engineering, operations, and data teams for log analytics, observability investigations, and building search-driven applications where fast filtering and aggregations are required.

OpenSearch is typically deployed as a distributed cluster to scale horizontally and support near real-time queries, and it’s often paired with OpenSearch Dashboards for exploration and troubleshooting. Data usually arrives via ingest pipelines and common shippers, then gets organized into indices designed for time-series or document-based workloads.

• Full-text search with relevance scoring, filters, and a flexible query DSL
• Aggregations for trends, breakdowns, and exploratory analysis
• Index management patterns for high-volume, time-based data
• Dashboards and visualizations through OpenSearch Dashboards
• Security features such as role-based access control and audit logging

OpenSearch is an open-source search and analytics engine used to index, query, and aggregate large volumes of document and time-series data. It is commonly used for application search and log and event analytics where low-latency queries and near real-time visibility are required.

• Low-latency full-text search and filtering for user-facing search experiences and operational troubleshooting across structured and semi-structured data.
• Powerful aggregations for analytics patterns such as faceted navigation, dashboards, and exploratory queries on high-cardinality fields.
• Near real-time indexing to keep logs, metrics-like events, and security signals searchable quickly after ingestion.
• Horizontal scalability through sharding and replication to increase ingest throughput and query capacity as data volume grows.
• High availability via replica shards and distributed cluster design to tolerate node failures and support rolling maintenance.
• Index lifecycle management, rollover, and retention patterns to manage time-based indices and control storage costs.
• Security controls such as authentication, role-based access control, and encryption options for multi-tenant and regulated environments.
• Ecosystem compatibility with Elasticsearch-style APIs and common language clients, reducing integration effort and easing migrations.
• Flexible deployment across VMs, Kubernetes, and managed offerings to fit operational constraints, network topology, and compliance requirements.
• Commonly paired with log shippers and observability pipelines to centralize search and analytics across distributed systems.

OpenSearch is a strong fit for log analytics, security analytics, and search-driven applications where query latency and aggregation performance matter. Typical trade-offs include operational overhead for shard sizing, index templates, capacity planning, and ongoing tuning to avoid hot shards, high heap pressure, and expensive query patterns at scale.

Common alternatives include Elasticsearch, Splunk, and Apache Solr.

Our experience with OpenSearch helped us develop repeatable deployment patterns, automation, and operational runbooks so clients can index, query, and visualize logs and business datasets reliably across cloud, Kubernetes, and on-prem environments.

Some of the things we did include:

• Designed highly available clusters with appropriate node roles, shard/replica strategies, and safe rolling upgrade procedures with version pinning.
• Deployed OpenSearch on Kubernetes using Helm, including storage sizing, PodDisruptionBudgets, node affinity/taints, and controlled scaling during peak ingest windows.
• Built ingestion pipelines with Fluent Bit and Logstash, standardizing parsing, enrichment, index naming, and multi-tenant routing.
• Migrated workloads from Elasticsearch to OpenSearch by validating mappings, templates, ingest pipelines, and client compatibility to reduce cutover risk and query regressions.
• Implemented index lifecycle policies for rollover, retention, and deletion, aligning hot/warm/cold patterns with compliance requirements and cost targets.
• Hardened clusters with TLS, fine-grained access control, audit logging, and index-level RBAC aligned with organizational access patterns.
• Improved ingest and query performance through shard sizing, refresh interval tuning, mapping/analyzer adjustments, query profiling, and cache-aware query patterns.
• Integrated OpenSearch Dashboards with Grafana for consistent dashboards and alerting workflows across platform and application teams.
• Implemented snapshot-based backup and disaster recovery to object storage, and ran restore drills to validate RPO/RTO expectations.
• Automated provisioning and configuration with infrastructure-as-code and CI/CD pipelines, including environment promotion, drift detection, and repeatable cluster bootstrapping.
• Set up observability for cluster health (JVM, thread pools, latency, disk watermarks) and created on-call playbooks for incident response and capacity planning.

This delivery experience helped us accumulate significant knowledge across search, observability, security, and operations use-cases, enabling us to deliver high-quality OpenSearch solutions that are stable, scalable, and straightforward to run.

Some of the things we can help you do with OpenSearch include:

• Audit your current search/log analytics platform and deliver a prioritized report covering reliability, security, performance, and cost.
• Define an adoption roadmap for data sources, index design, retention/ILM policies, access controls, and operational ownership.
• Design and deploy production-ready OpenSearch clusters with capacity planning, failure-domain strategy, and clear SLOs.
• Build and harden ingestion pipelines for logs and events, including mappings, index templates, rollover strategies, and schema governance.
• Implement security and compliance guardrails with RBAC, encryption, network controls, audit logging, and least-privilege access patterns.
• Improve query and indexing performance through shard/replica tuning, query profiling, caching strategies, and workload-based right-sizing.
• Operationalize day-2 excellence with observability, alerting, snapshot/restore, backup validation, and incident-ready runbooks.
• Automate provisioning and change management using IaC and CI/CD to reduce drift and enable safe, repeatable releases.
• Troubleshoot cluster instability and latency end-to-end, from ingestion bottlenecks and mapping conflicts to resource pressure and slow queries.
• Enable your team with hands-on training and documentation for operations, governance, and continuous improvement.