How to get started with metrics using Grafana: Prometheus, Grafana Mimir, Graphite, and beyond
How to get started with metrics using Grafana: Prometheus, Grafana Mimir, Graphite, and beyond
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How to get started with metrics using Grafana: Prometheus, Grafana Mimir, Graphite, and beyond
How to get started with metrics using Grafana: Prometheus, Grafana Mimir, Graphite, and beyond
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Prometheus has become the metrics management standard for modern and cloud native applications. However, enterprises that use Prometheus at scale face scalability, availability, legacy, and security challenges. Grafana Labs provides a simple and scalable solution for unifying your metrics across multiple systems, enabling both real-time and historical analysis in Grafana Cloud or self-hosted on your own infrastructure.
In this session, we’ll talk about some of the challenges users encounter when scaling their metrics systems, with a particular focus on Prometheus, Grafana Mimir, and Graphite. Then we’ll cover how Grafana helps organizations address these problems so they can easily store, query, and manage their metrics.
During this webinar, you’ll learn:
- The importance of metrics as part of your observability strategy
- The challenges that come up when scaling Prometheus metrics
- How to overcome the scaling challenge with different Grafana Labs offerings - Grafana Mimir, Grafana Cloud Metrics and Grafana Enterprise Metrics (GEM)
- How to choose between the different Grafana offerings
- How to get started with Grafana Cloud using your existing metrics system
Additional resources to explore:
- Register now: Grafana Cloud Adaptive Metrics: Reduce Prometheus high cardinality metrics
- Register now: Intro to Grafana Mimir: The open source time series database that scales to 1 billion metrics & beyond
- Webinar: How to control metrics growth in Prometheus and Kubernetes with Grafana Cloud
- Try now: Sign up for Grafana Cloud

Aengus Rooney
Senior Solutions Engineer at Grafana Labs
Aengus has been involved in all aspects of data systems, enterprise data, big data, and cloud data platforms, for over two decades. Originally programming large scale transactional databases, his career moved into parallel & distributed computing, advanced analytical systems, and observability. Aengus has been responsible for the technical strategy, architecture, design, and engineering for multiple enterprise scale data systems, using proprietary and open source technologies.