Prometheus

Prometheus vs InfluxDB: which is better for large-scale time-series monitoring?

The question is about Prometheus .

Answer:

Prometheus is tailored for large-scale time-series monitoring, particularly continuous system metrics in infrastructure environments. InfluxDB, while supporting monitoring, extends this with additional capabilities in long-term storage, advanced querying, and enhanced data retention customisation.

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Javid is a Senior Full Stack Software Engineer with 6 years of experience in building scalable applications and enhancing system performance. He specializes in Java, Kotlin, and JavaScript, leveraging frameworks such as Sprin... Read More

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40 h/w
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7 yrs.
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Danylo is an experienced DevOps Engineer with 5 years in cloud infrastructure and microservices architecture. He specializes in Amazon (AWS), Kubernetes, and Terraform, effectively managing EKS clusters and implementing infra... Read More

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5 yrs.
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Komiljon is an experienced DevOps & SRE Engineer with 3 years in the field, proficient in Kubernetes, Docker, AWS, and Terraform. He has played a crucial role in enhancing system reliability and deployment speed while impleme... Read More

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Middle
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40 h/w
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3 yrs.
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Victoriia S.

Victoriia is a skilled Flutter Developer with 4 years of experience in mobile application development. She specializes in frameworks such as Flutter, leveraging JavaScript, DART, and utilizes databases like MySQL and Firebase... Read More

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20 - 30 h/w
Experience
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Cortance 5-star rating on ClutchCortance 5-star rating on GoodFirms
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Cortance delivered a functional, stable system on time, receiving positive feedback from the end client. The team was responsive to feedback and quickly resolved issues, communicating via virtual meetings, emails, and messaging apps. Their proactive approach impressed the client.

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Cortance provided us with three AI/ML experienced backend developers who met our tech expectations and integrated into our team well. They picked up our workflows quickly and, because of their solid experience, helped us to sort all the Machine learning-related tasks. The hiring process was also fast and efficient, which helped us scale without delays.

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