The Metrics as a Service (MaaS) Model with InfluxDB
Metrics as a Service combines centralization resource efficiency and maximum value extraction from data, but also and most importantly, it lowers the barrier to start using time series data to drive more accurate planning and decision making. In traditional approaches to monitoring metrics collection, much effort and overhead is placed into setting up and running the monitoring solution, with limited value extracted from the data collected. With a service model approach, we invert the pyramid, facilitating teams to adopt data processing frameworks to extract more and valuable information quickly and at minimal incremental cost.
Open source is fundamental to data collection, especially if the open source project is supported by an active and motivated community. Most likely, there will already be a set of available libraries and collector agents for common systems and applications usually found in a production environment. But, open source goes beyond reducing barriers to adoption with a ready-to-use menu of libraries and agent plugins. Custom instrumentation will also benefit from full code access and transparency, and a community support to overcome issues.
InfluxDB was purpose-built to provide an open source time series platform that operates with high performance at scale, allowing for centralized ingestion, analysis, visualization, alert and storage of all monitoring data and respective metadata collected across the organization. InfluxDB is ecosystem- and cloud-friendly, keeping your data asset “fluid” for maximum value extraction. And most importantly, it keeps pace with the speed and analytical needs of modern environments enabling more accurate predictive modeling, intelligent alerting and real-time actions.
Using InfluxDB for MaaS enables all teams across your organization to access time series data from infrastructure monitoring, container and orchestration monitoring (including Docker, Kubernetes, and Jenkins), network monitoring, multi-tier monitoring (including error rate monitoring), application performance monitoring, cloud monitoring, and business functionalities services monitoring.