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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.
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.
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.
I was impressed with the amount of professionalism, communication, and speed of delivery.
They are very knowledgeable in their area of expertise.
Nguyen is a champ. He's fast and has great communication. Well done!
We were impressed with their commitment to the project.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
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.
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.
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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?
ClickHouse is an open-source, column-oriented OLAP database designed for high-performance analytics, originally developed at Yandex and now maintained by the ClickHouse community and ClickHouse, Inc.; it is built to process and analyze very large datasets with low-latency, high-throughput SQL queries. ClickHouse is commonly used for real-time dashboards, log and event analytics, observability workloads, and time-series style aggregations, with features such as vectorized query execution, data compression, partitioning and indexing options, materialized views, and a rich set of aggregate functions. It supports distributed deployments for horizontal scaling, replication for high availability, and integrates with common ingestion and data ecosystem tools (e.g., Kafka, object storage, and various ETL/streaming pipelines), making it suitable for both batch and streaming-oriented analytical workloads. For more details, see the official ClickHouse documentation.
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.
ClickHouse is an open-source, column-oriented OLAP database built for fast analytical queries over large datasets. It is commonly used when teams need low-latency aggregations and high concurrency for dashboards, reporting, and event analytics.
- Columnar storage speeds up scans and aggregations by reading only the columns needed for a query.
- High compression reduces storage costs and improves I/O efficiency on large fact tables.
- Vectorized execution and parallel query processing deliver low-latency analytics at high throughput.
- Designed for append-heavy workloads, making it a strong fit for logs, metrics, clickstream, and event data.
- Partitioning and primary key ordering help prune data early and keep queries predictable at scale.
- Materialized views and projections can pre-aggregate or reshape data to accelerate common query patterns.
- Distributed tables and sharding enable horizontal scaling across nodes for larger datasets and higher concurrency.
- TTL policies support automated data retention and tiering for time-series and compliance-driven datasets.
- Broad ingestion options, including HTTP, Kafka engine, and integrations with common ETL tools, simplify pipelines.
- SQL support with rich functions for analytics, including approximate algorithms, window functions, and time-series operations.
ClickHouse performs best for read-heavy analytics and large-scale aggregations, but it is not a general-purpose OLTP database. Schema and sort key design matter for consistent performance, and some workloads may require careful tuning for joins, high-cardinality dimensions, and frequent updates or deletes.
Common alternatives include Snowflake, BigQuery, Apache Druid, and PostgreSQL. For more details on ClickHouse architecture and capabilities, see https://clickhouse.com/docs.
Our experience with ClickHouse helped us build repeatable deployment patterns, performance tuning playbooks, and operational tooling that we now use to help clients run fast analytical queries reliably at scale.
Some of the things we did include:
- Designed and implemented ClickHouse clusters for high-ingest analytics workloads, including sharding/replication layouts aligned to query patterns and growth expectations.
- Built Kubernetes-based deployments with automated provisioning, upgrades, and day-2 operations, including resource sizing and safe rollout strategies.
- Implemented ingestion pipelines from streaming and batch sources, integrating ClickHouse with Kafka and Apache Airflow for reliable, observable data movement.
- Optimized schemas (MergeTree family engines), partitioning, primary keys, and compression settings to reduce latency and storage while maintaining concurrency.
- Set up query governance and workload isolation using users/roles, quotas, and settings profiles; hardened network access and secrets handling for production environments.
- Implemented observability for ClickHouse services and query performance, integrating metrics and dashboards with Prometheus and Grafana.
- Planned and executed migrations from legacy analytical stores to ClickHouse, including backfills, validation checks, and cutover plans with minimal downtime.
- Established backup/restore and disaster recovery procedures, including testing restore paths and documenting RPO/RTO expectations.
- Enabled self-service analytics access patterns by standardizing database objects, retention policies, and data contracts for downstream teams.
- Delivered hands-on enablement for engineering and data teams, including runbooks, incident response guidance, and query tuning workshops.
This delivery work helped us accumulate significant knowledge across multiple ClickHouse use-cases, from ingestion-heavy pipelines to high-concurrency dashboards, and enables us to deliver high-quality ClickHouse solutions and setups for clients.
Some of the things we can help you do with ClickHouse include:
- Assess your current analytics workload and ClickHouse readiness, then deliver a findings report with prioritized recommendations.
- Create an adoption roadmap covering data modeling, ingestion patterns, retention/TTL strategy, and operational ownership.
- Design and implement production-grade ClickHouse deployments (single-node to clustered) with repeatable automation using IaC and CI/CD.
- Build reliable ingestion pipelines and batch/stream loading patterns, including schema evolution and backfill strategies.
- Optimize query performance and concurrency with partitioning, primary keys, codecs, materialized views, and caching where appropriate.
- Establish security and compliance guardrails: network controls, authentication/authorization, encryption, secrets management, and audit-friendly configurations.
- Reduce infrastructure cost while meeting SLOs through capacity planning, right-sizing, storage tiering, and workload-aware scaling.
- Implement observability for ClickHouse (metrics, logs, tracing where applicable), alerting, and runbooks for on-call operations.
- Harden reliability with backup/restore, disaster recovery planning, upgrade strategies, and incident response playbooks.
- Enable your team with hands-on training, best-practice workshops, and knowledge transfer for ongoing operations and tuning.
