Building a Scalable Time-Series Data Platform with VictoriaMetrics 1.83, Grafana, and Kubernetes - NextGenBeing Building a Scalable Time-Series Data Platform with VictoriaMetrics 1.83, Grafana, and Kubernetes - NextGenBeing
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Building a Scalable Time-Series Data Platform with VictoriaMetrics 1.83, Grafana, and Kubernetes

Learn how to build a scalable time-series data platform using VictoriaMetrics 1.83, Grafana, and Kubernetes to handle large amounts of data and provide fast analytics.

Growth & Distribution Premium Content 3 min read
NextGenBeing Founder

NextGenBeing Founder

Dec 17, 2025 • 29 views
Building a Scalable Time-Series Data Platform with VictoriaMetrics 1.83, Grafana, and Kubernetes
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Introduction to Time-Series Data Platforms

Last quarter, our team discovered that our existing time-series data platform couldn't handle the sudden spike in data from our IoT devices. We tried to optimize it, but it was clear that we needed a more scalable solution. That's when we decided to build a new platform using VictoriaMetrics 1.83, Grafana, and Kubernetes.

The Problem with Traditional Time-Series Databases

Traditional time-series databases like InfluxDB and OpenTSDB are great for small-scale applications, but they become bottlenecked when dealing with large amounts of data. We experienced this firsthand when our database started to slow down, causing delays in our analytics pipeline. We needed a database that could handle high ingestion rates and provide fast query performance.

Why VictoriaMetrics 1.83?

After researching various time-series databases, we chose VictoriaMetrics 1.83 for its high performance, scalability, and ease of use. VictoriaMetrics is a fast, scalable, and open-source time-series database that can handle large amounts of data. It's also highly configurable, which made it easy to tailor to our specific use case.

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Tags: Performance scalability time-series data

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