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Testimonials
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.
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.
We were impressed with their commitment to the project.
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.
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.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
I was impressed with the amount of professionalism, communication, and speed of delivery.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
They are very knowledgeable in their area of expertise.
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.
Nguyen is a champ. He's fast and has great communication. Well done!
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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?
Elasticsearch is an open-source, distributed search and analytics engine built on Apache Lucene, designed for fast full-text search and near real-time querying across large datasets. It is commonly deployed as part of the Elastic Stack (ELK) for observability and log analytics, where it indexes structured and unstructured data to support low-latency retrieval and aggregation. Key capabilities include scalable indexing and search across clusters, relevance scoring and complex query DSL, aggregations for analytics, geospatial search, and time-series use cases such as metrics and event data. Elasticsearch is typically accessed via its RESTful API and integrates with a broad ecosystem of data shippers and visualization tools for building search experiences and operational dashboards; see the official documentation at https://www.elastic.co/elasticsearch/.
Logging is a software development practice in which application data about events, warnings and errors is being saved in an organized manner that allows for a better understanding of that system's operations and a quicker incidents response.
Some of the many reasons for using logging tools:
- Receive detailed information about what is happening in your code, making it easier to identify and fix bugs.
- Track changes to your code and understand how it is being used.
- Logging tools can provide information about the performance of your code, helping you identify bottlenecks and optimize performance.
- Logging tools can automatically report errors to you or a team, allowing for quick response to critical issues.
- Logging can help you meet regulatory requirements for auditing and security.
- Handle large amounts of data and easily integrate it into a centralized logging system, making it much more manageable to oversee logs across multiple servers and applications.
- Logging can be done at different levels, such as debug, info, warning, error, and critical, so you can filter out or focus on specific types of events.
Elasticsearch is a distributed search and analytics engine used to index and query large volumes of structured and unstructured data with low latency. It is commonly adopted to power full-text search, log and event analytics, and near real-time dashboards at scale.
- Fast full-text search and relevance scoring using Lucene, with support for analyzers, stemming, and language-aware tokenization.
- Near real-time indexing and querying, enabling recently ingested data to become searchable quickly for operational use cases.
- Horizontal scalability via sharding and replicas, allowing clusters to grow with data volume and query throughput demands.
- High availability features such as replication and automatic failover, improving resilience for search-dependent systems.
- Rich query DSL for complex filtering and search patterns, including boolean queries, phrase matching, and geo queries.
- Aggregations framework for analytics on indexed data, supporting metrics, bucketing, and time-series style exploration.
- Schema flexibility with mappings and dynamic fields, useful when ingesting semi-structured events and evolving data shapes.
- Ecosystem support for observability pipelines through Beats and Logstash, simplifying ingestion from logs, metrics, and traces.
- Security and access control options for multi-tenant and regulated environments, including authentication, authorization, and encryption.
- Operational tooling and APIs for cluster management, index lifecycle management, and snapshot-based backups.
Elasticsearch is a strong fit when low-latency search and aggregations are central requirements, but it requires careful sizing, index design, and lifecycle policies to control storage costs and avoid performance issues under heavy write or high-cardinality workloads. For general guidance and operational best practices, see the official Elasticsearch documentation.
Common alternatives include OpenSearch, Apache Solr, and managed cloud search offerings such as Amazon OpenSearch Service and Azure AI Search.
Our experience with Elasticsearch across logging, search, and analytics projects helped us build practical patterns, automation, and operational playbooks we reuse to deliver reliable clusters and fast query performance for clients.
Some of the things we did include:
- Designed and deployed highly available Elasticsearch clusters on Kubernetes using Helm, including node roles, pod disruption budgets, and safe rolling upgrades.
- Planned and executed migrations from self-managed VMs to managed offerings such as Amazon OpenSearch Service, including index templates, ILM policies, and reindexing strategies.
- Optimized indexing and query performance by tuning shard counts, refresh intervals, replica strategies, and mapping choices for high-ingest workloads.
- Implemented index lifecycle management (hot/warm/cold), retention policies, and snapshot/restore workflows to reduce storage costs while meeting recovery requirements.
- Built log ingestion pipelines with Logstash and Beats, including parsing, enrichment, and backpressure handling to keep clusters stable under bursts.
- Created operational dashboards and alerting for search latency, indexing throughput, JVM pressure, and disk watermarks using Kibana.
- Hardened security with TLS, role-based access control, and least-privilege patterns for service accounts and CI/CD users.
- Automated cluster provisioning and configuration with Infrastructure as Code, making environments reproducible across dev, staging, and production.
- Set up observability for Elasticsearch internals using Prometheus metrics and Grafana dashboards for proactive capacity planning.
- Ran incident-response and maintenance routines (heap tuning, segment merges, shard rebalancing) and produced runbooks to reduce time-to-recovery.
This delivery experience helped us accumulate significant knowledge across multiple Elasticsearch use-cases, enabling us to implement high-quality Elasticsearch setups and provide hands-on support from initial design through long-term operations.
We can provide you with end-to-end help utilizing Elasticsearch for your needs.
Things we can do for your company:
- Creation of log analysis dashboards using Kibana
- Deployment of Elasticsearch on Kubernetes with Helm
- Optimization of sharding and replication to enhance performance
- Adoption of best practices for managing indices in Elasticsearch
- Provision of easy interfaces for developers to ship logs automatically to Elasticsearch
- Monitoring of Elasticsearch clusters via Prometheus and Grafana
- Implementation of autoscaling for Elasticsearch clusters
