Big data workloads can be complex and challenging to monitor, especially when they are running on Amazon EMR on Amazon EC2 clusters. This blog post will discuss how FINRA established real-time operational observability for their Amazon EMR big data workloads using Prometheus and Grafana.
Challenges of Monitoring Big Data Workloads
There are several challenges associated with monitoring big data workloads on Amazon EMR. These challenges include:
- Distributed nature of EMR clusters: EMR clusters are distributed, which means that the data you need to monitor is spread across multiple nodes.
- Variety of data sources: Big data workloads can involve a variety of data sources, such as logs, metrics, and traces.
- Need for real-time insights: In order to troubleshoot problems and optimize performance, you need to be able to see data in real-time.
FINRA’s Observability Framework
To address these challenges, FINRA built an observability framework that provides operational metrics insights for big data processing workloads on Amazon EMR on Amazon EC2 clusters. The framework includes the following components:
- Data ingestion layer: The data ingestion layer is responsible for collecting metrics from EMR clusters. FINRA uses a custom-built script that is stored in Amazon Simple Storage Service (Amazon S3) to collect metrics.
- Prometheus: Prometheus is an open-source monitoring tool that collects and stores metrics data.
- Grafana: Grafana is an open-source visualization tool that allows you to create dashboards to view and analyze metrics data.
Benefits of the Observability Framework
FINRA’s observability framework has provided several benefits, including:
- Improved visibility into EMR cluster health: The framework provides real-time insights into the health of EMR clusters, which helps FINRA to identify and troubleshoot problems quickly.
- Enhanced operational efficiency: The framework helps FINRA to optimize the performance of their EMR clusters.
- Reduced costs: By improving the efficiency of their EMR clusters, FINRA can reduce their costs.
Conclusion
By using Prometheus and Grafana, FINRA has been able to establish real-time operational observability for their Amazon EMR big data workloads on Amazon EC2. This has given them improved visibility into the health of their clusters, enhanced operational efficiency, and reduced costs.
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