Capillary reduced ops tickets by 95%

“Our releases are fast. And with less developer time needed our teams can focus on building exciting features. We’ve saved countless hours and costs.”

Piyush K,
Chief Architect, Capillary Technologies,

Treebo reduced production issues by 70%

"With Facets, our staging environments look identical to production environments. So in case of production issues, we can be sure there are no infra drifts."

Kadam Jeet Jain,
Co-Founder & CTO, Treebo Hotels and Hotel Superhero.

GGX switched from AWS to GCP in 2 weeks

"Facets has radically changed our DevOps for the better. They did all the heavy lifting and saved us precious time and resources in our when we switched from AWS to GCP."

Kaustubh Bhoyar,
Head of engineering, GGX

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Application Orchestration

Apache/mesos

An open-source distributed systems manager designed for resource management and job scheduling across clusters of machines

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Summary

A powerful distributed systems manager that provides efficient resource management and job scheduling across clusters of machines. It abstracts CPU, memory, storage, and other compute resources, allowing users to deploy and manage applications seamlessly. With its fault-tolerant architecture and scalable design, Mesos is widely used in large-scale data centers and cloud environments to optimize resource utilization and ensure high availability of applications.

Key Features

  • Mesos abstracts CPU, memory, storage, and other compute resources, providing efficient management across clusters of machines.
  • Designed to scale horizontally, enabling users to seamlessly add or remove resources as needed to accommodate changing workloads.
  • Mesos has an inbuilt fault-tolerant architecture, ensuring high availability of applications by automatically handling machine failures and resource constraints.
  • Mesos provides flexible job scheduling mechanisms, allowing users to define tasks and dependencies and schedule them based on resource availability and constraints.

How does it work?

Real world example

Pros

  • Apache Mesos can effortlessly scale to thousands of nodes, making it a robust choice for large-scale distributed systems.
  • With its efficient resource management capabilities, Mesos maximizes resource utilization across the cluster, leading to cost savings and improved infrastructure efficiency.
  • Equipped with built-in fault tolerance mechanisms, ensuring high availability of applications even in the face of node failures or network partitions.
  • Mesos supports diverse workloads and frameworks, including batch processing, real-time analytics, and container orchestration, offering flexibility for various application requirements.

Cons

  • Operating and maintaining Mesos clusters demands ongoing effort and expertise, including tasks such as monitoring, troubleshooting, and performing upgrades.
  • While Mesos offers excellent resource utilization and fault tolerance, it may introduce some performance overhead due to its resource allocation and scheduling mechanisms.
  • Setting up and managing Mesos clusters can be complex, requiring a deep understanding of distributed systems and infrastructure management principles.

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Capillary reduced ops tickets by 95%

“Our releases are fast. And with less developer time needed our teams can focus on building exciting features. We’ve saved countless hours and costs.”

Piyush K,
Chief Architect, Capillary Technologies,

Treebo reduced production issues by 70%

"With Facets, our staging environments look identical to production environments. So in case of production issues, we can be sure there are no infra drifts."

Kadam Jeet Jain,
Co-Founder & CTO, Treebo Hotels and Hotel Superhero.

GGX switched from AWS to GCP in 2 weeks

"Facets has radically changed our DevOps for the better. They did all the heavy lifting and saved us precious time and resources in our when we switched from AWS to GCP."

Kaustubh Bhoyar,
Head of engineering, GGX

Trusted by companies to run production at scale

Capillary | FacetsMPL | FacetsTreebo | FacetsPurplle | Facets

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