OpenSearch Consulting

OpenSearch is an open-source search and analytics engine used to index, query, and visualize large-scale datasets (including logs, metrics, and application events), originally derived from Elasticsearch and maintained by the OpenSearch community under the stewardship of the OpenSearch Project. It supports near real-time full-text search and aggregations, integrates with dashboards for exploration and monitoring, and is commonly deployed in distributed clusters to scale horizontally and provide high availability. Key capabilities include RESTful APIs, index management, role-based access control and security features, ingest pipelines, alerting, and compatibility with common data shippers (e.g., Beats/Fluent Bit) and storage backends; typical use cases include centralized log analytics, observability, security analytics, and powering search experiences in applications.

The use of databases integration into any software development project out there is crucial, consisting of many useful benefits:

• Databases provide efficient means for searching, sorting and retrieving specific information.
• They help maintaining data integrity and consistency through the use of constraints and data validation.
• Centralized storage allows multiple users to access and update the data simultaneously.
• Data availability is ensured through backup and recovery options.
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• They facilitate sharing of data among multiple applications and systems, improving interoperability.
• Data integration with other systems is made easier, enhancing data analysis and reporting.
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• They can handle large amount of data and increasing number of users by scaling.

OpenSearch is an open-source search and analytics engine used to index, query, and visualize large-scale datasets such as logs, metrics, and application events. It is commonly used to power fast full-text search, observability analytics, and near real-time dashboards over high-volume data.

• Fast full-text search and filtering for applications that need low-latency queries over large document collections.
• Scales horizontally by adding nodes, supporting high ingest rates and large index sizes when properly sharded and replicated.
• Near real-time indexing makes it suitable for log and event analytics where data must be searchable shortly after ingestion.
• Rich query capabilities including aggregations enable analytics workloads such as trend analysis, top-N breakdowns, and percentile calculations.
• Built-in security features (when enabled) such as role-based access control and TLS support common multi-tenant and regulated environments.
• Index lifecycle and data retention tooling help manage time-series data costs by rolling indices and expiring old data.
• Integrates well with common ingestion pipelines (for example Fluent Bit, Logstash-compatible shippers, and OpenTelemetry exporters) for observability stacks.
• OpenSearch Dashboards provides visualization and exploration workflows for troubleshooting, ad hoc analysis, and operational reporting.
• Supports multi-index patterns and aliases, enabling safer migrations and blue/green index rollouts with minimal client changes.
• Extensible via plugins, allowing custom analyzers, authentication integrations, and specialized features where needed.

OpenSearch is a strong fit for centralized logging, SIEM-like search, and application search where query flexibility and operational control matter. Trade-offs include cluster tuning and ongoing operations (sharding strategy, memory pressure, and reindexing costs), and it may be less cost-effective than columnar analytics engines for heavy batch aggregations over long retention windows.

Common alternatives include Elasticsearch, Splunk, and ClickHouse, with selection typically driven by licensing, operational model, and workload shape.

Our experience with OpenSearch has helped us build repeatable patterns, automation, and operational knowledge for running search and analytics workloads reliably—whether for centralized logging, application search, or large-scale event analytics.

Some of the things we did include:

• Designed and deployed production OpenSearch clusters with high availability, shard strategy, and capacity planning tailored to log and event ingestion rates.
• Implemented OpenSearch on Kubernetes with automated node provisioning, rolling upgrades, and safe index lifecycle practices to reduce operational risk.
• Built ingestion pipelines for logs and metrics, integrating OpenSearch with Fluent Bit and Logstash to standardize parsing, enrichment, and routing.
• Set up dashboards and multi-tenant access patterns for teams, including role-based access control, index-level permissions, and audit-friendly configurations.
• Optimized query performance through index template design, mapping strategy, shard/replica tuning, and cache-aware query patterns for common investigative workflows.
• Implemented snapshot, backup, and disaster recovery procedures, including restore testing and runbooks for incident response and cluster recovery.
• Integrated OpenSearch alerting into on-call workflows and incident tooling, including threshold- and anomaly-based alerts for ingestion gaps and cluster health.
• Hardened clusters with network controls, TLS configuration, secrets management, and least-privilege service accounts across CI/CD and runtime environments.
• Migrated workloads from Elasticsearch-compatible deployments to OpenSearch, validating mappings, dashboards, and ingestion compatibility to minimize downtime.
• Established observability for the platform itself by exporting cluster metrics and logs into Prometheus and Grafana for proactive monitoring.

This experience helped us accumulate significant knowledge across multiple OpenSearch use-cases, and it enables us to deliver high-quality OpenSearch setups that are secure, maintainable, and tuned for real operational workloads.

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

• Assess your current search/log analytics setup and deliver a clear review report with risks, gaps, and prioritized recommendations.
• Create an adoption or migration roadmap (including sizing, index strategy, and retention) aligned to reliability, security, and cost goals.
• Design and implement production-grade OpenSearch clusters, dashboards, and data ingestion pipelines for logs, metrics, and events.
• Automate provisioning and configuration with Infrastructure as Code and GitOps-style workflows to improve consistency and auditability.
• Harden security with TLS, fine-grained access control, multi-tenancy patterns, and compliance-friendly guardrails.
• Optimize performance and cost through shard/replica tuning, index lifecycle policies, query profiling, and right-sizing.
• Improve reliability with backup/restore, disaster recovery planning, rolling upgrades, and runbooks for on-call operations.
• Integrate observability and alerting for cluster health, ingestion latency, and query performance to reduce MTTR.
• Troubleshoot ingestion, mapping, and query issues to stabilize pipelines and improve search relevance.
• Enable your team with hands-on training, operational playbooks, and best practices for day-2 operations.