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HQ: New York City, NY
Claroty is a cybersecurity firm that specializes in protecting industrial, medical, IoT, and XIoT devices.
They empower industrial, healthcare, and commercial organizations to secure all cyberphysical systems in their environments.
Infrastructure: Python, C++ and Spark on Amazon EKS / EC2
Claroty runs most of their business logic on Kubernetes clusters and uses Apache Spark to handle, process and analyze incoming data. Their backend applications are written in Python and C++ and are running both on EKS and EC2 machines. They also leverage Kafka for transferring data between services, and use PostgreSQL RDS as their database and ElastiCache as their managed caching service.
Performance is crucial for the company’s product and reputation within the cybersecurity industry. Their applications must be ready to monitor, locate and resolve threats at all times.
Claroty is dealing with large-scale environments that are constantly changing and require real-time AIprocessing. This means that they require the best possible cloud service performance with minimal
latency, working with multiple databases and distributed systems. This large scale resource utilization leads to increasing cloud costs.
The cybersecurity company has grown rapidly over the past few years, and the DevOps group was tasked with reducing cloud costs. They needed a solution that would be able to deliver results quickly, with simple implementation and with as little time required from the developer team as possible.
Claroty first installed Granulate’s open-source continuous profiler on their EKS cluster. They saw that there was significant potential for optimization, so their DevOps decided to install the Granulate agent to optimize their Apache Spark cluster, which was running on multiple nodes in EKS on AWS.
Less than two weeks after implementing Granulate’s runtime and capacity optimization solutions, Claroty saw impactful performance improvements.
They were then able to accelerate their real-time data processing and AI pipeline with better integration of DB and ETL.
After seeing significant value on their largest Spark cluster, which was geographically oriented towards the United States, Claroty expanded to additional regions. These expansions garnered immediate results, requiring no R&D efforts nor the need for the agent to relearn.
These performance results translated into leaner and more efficient use of resources, including a 50% reduction in memory usage and a 15% reduction in CPU utilization. Claroty applied those reductions to improve their bottom line, achieving a 20% reduction in costs and consequently decided to deploy Granulate across the rest of their Big Data clusters.