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Testimonials
Working with MeteorOps was exactly the solution we looked for. We met a professional, involved, problem solving DevOps team, that gave us an impact in a short term period.
I was impressed with the amount of professionalism, communication, and speed of delivery.
Thanks to MeteorOps, infrastructure changes have been completed without any errors. They provide excellent ideas, manage tasks efficiently, and deliver on time. They communicate through virtual meetings, email, and a messaging app. Overall, their experience in Kubernetes and AWS is impressive.
We were impressed with their commitment to the project.
Nguyen is a champ. He's fast and has great communication. Well done!
They are very knowledgeable in their area of expertise.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
From my experience, working with MeteorOps brings high value to any company at almost any stage. They are uncompromising professionals, who achieve their goal no matter what.
They have been great at adjusting and improving as we have worked together.
We got to meet Michael from MeteorOps through one of our employees. We needed DevOps help and guidance and Michael and the team provided all of it from the very beginning. They did everything from dev support to infrastructure design and configuration to helping during Production incidents like any one of our own employees. They actually became an integral part of our organization which says a lot about their personal attitude and dedication.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
Good consultants execute on task and deliver as planned. Better consultants overdeliver on their tasks. Great consultants become full technology partners and provide expertise beyond their scope.
I am happy to call MeteorOps my technology partners as they overdelivered, provide high-level expertise and I recommend their services as a very happy customer.
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Free Project Planning: We dive into your goals and current state to prepare before a kickoff.
Pay-as-you-go: Use our capacity when you need it, none of that retainer nonsense.
Experts On-Demand: Get new experts from our team when you need specific knowledge or consultation.
We Don't Sleep: Just kidding we do sleep, but we can flexibly hop on calls when you need.
Ad-hoc Calls: When a video call works better than a chat, we hop on a call together.
How it works?
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
A computer database is an organized collection of data that can be manipulated and accessed through specialized software
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.
- Access controls and permission settings can be used to secure the data.
- 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.
- Automated updating and maintenance improves data accuracy and reduces errors.
- 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 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.
