Metrics as a Service with InfluxDB

Fastest route to data-driven organization: scalable, intuitive, central platform for metrics, events and data analytics.
Application performance monitoring with InfluxDB

Learn why InfluxDB — trusted by hundreds of organizations in nearly every industry across a wide range of use cases — has become the preferred platform for centralized monitoring of metrics, events and key business indicators.

Why Metrics as a Service?

Metrics as a Service is a concept that combines centralization resource efficiency, overhead offload and maximum value extraction from the data collected organization-wide. All data converging to one platform, available in one pane, allows much richer visualization and analytics across multiple data sources and data types.

Key advantages of MaaS:

  • Removes silos’ inefficiencies
  • Reduce operational workload while providing agile response to multiple audiences with diverse monitoring requirements
  • Enable data enrichment, correlation and complex analytics for more insightful information
  • Supports real-time stream processing, alerting and anomaly detection
  • Removes barriers to adopting data-driven culture

What is the InfluxData Metrics as a Service with InfluxDB solution?

Metrics as a Service with InfluxDB gives you functionalities to sustain centralized growth, availability and durability of the data:

  • High availability clustering
  • Horizontal scalability
  • High ingest/query performance
  • Storage efficiency
  • Single pane for holistic visualization of key performance indicators.

Furthermore, InfluxDB provides extended functionalities and workflow automation for an easy-to-use, one-stop shop for metrics:

  • Real-time stream processing for effective alerting, optimizations at ingest as well as anomaly detection when integrated with Machine Learning frameworks
  • Powerful query and scripting engine that performs complex analytics over multiple data sources, enabling early detection of issues and fast root-cause diagnosis
  • Automation of data lifecycle management with retention policies, automated rollups, and eviction

Why use the InfluxDB platform for Metrics as a Service?

  • Detect anomalies and act while it still matters
  • Handle growing workload of targeted monitoring requirements
  • Provide fastest time to an awesome data-driven culture

There are 200+ Telegraf plugins and other Community Data Sources integrations using open source client libraries (Java, Javascript, Go, Python, C++, C#).

Find more information about application monitoring.

Featured customers

Playtech

“Why InfluxDB? For Playtech, it was very important to have observability, to understand system behavior to predict possible outages and problems in the very early stages.”

Aleksandr Tavgen, technical architect, Playtech

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Playtech

“Why InfluxDB? For Playtech, it was very important to have observability, to understand system behavior to predict possible outages and problems in the very early stages.”

Aleksandr Tavgen, technical architect, Playtech

Read case study

WayfairWayfair

“We recently introduced InfluxDB as our first-class time series database system, where we had the opportunity to work directly with InfluxData to ensure we were on a path that is scalable, robust, and in line with the future direction of their platform.”

Mike Bell, engineer, Wayfair

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WayfairWayfair

“We recently introduced InfluxDB as our first-class time series database system, where we had the opportunity to work directly with InfluxData to ensure we were on a path that is scalable, robust, and in line with the future direction of their platform.”

Mike Bell, Engineer, Wayfair

Read case study

hulu logo - customers studyHulu

The Hulu System Engineering team realized that in order to successfully implement a centralized service-oriented model for their metrics pipeline, they needed a time series platform that could support various consumers of monitoring data, yet a HA solution, clustered for failover, with zero downtime.

Read case study

hulu logo - customers studyHulu

The Hulu System Engineering team realized that in order to successfully implement a centralized service-oriented model for their metrics pipeline, they needed a time series platform that could support various consumers of monitoring data, yet a HA solution, clustered for failover, with zero downtime.

Read case study

CapitalOne success storyCapital One

“InfluxDB is a high-speed read and write database. So think of it. The data is being written in real-time, you can read in real-time, and when you’re reading it, you can apply your machine learning model. So, in real-time, you can forecast, and you can detect anomalies.”

Rajeev Tomer, senior manager of data engineering, Capital One

CapitalOne success storyCapital One

“InfluxDB is a high-speed read and write database. So think of it. The data is being written in real-time, you can read in real-time, and when you’re reading it, you can apply your machine learning model. So, in real-time, you can forecast, and you can detect anomalies.”

Rajeev Tomer, senior manager of data engineering, Capital One

Read more about MaaS with InfluxDB implementations made available by the InfluxDB community:

Metrics as a Service

In this webinar, the members of IT staff, Saravanan Krisharaju, Rajeev, and Karl will share how they built a fault-tolerant solution for metrics and events based on InfluxDB Enterprise, and how they leveraged InfluxDB fast data write and read to integrate their Machine Learning processing to detect anomalies in real-time.
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