For some businesses, windows of opportunity can be measured in seconds. To react immediately and intelligently to these opportunities, the data from disparate systems and services needs to be available for processing in milliseconds. Whether the organization needs real-time analytics to buy and sell equities, debug code before whole systems go offline, adjust prices based on customer behavior or conduct A/B testing inside a MMORPG, processing, analyzing and acting on the time-series data in real-time is the problem to solve.
InfluxData helps companies get insight into what matters to them, in real-time, by helping them effectively manage their time-series data.
At InfluxData, our interviews with customers and users have surfaced many cases already in wide use, for example:
There aren’t many out-of-box real-time analysis solutions that can serve so many diverse industries and niches. And more importantly, that don’t require the heavy lifting of a mini supercomputer or a complex, sprawling system that needs to implemented and maintained at a very high cost. Legacy analytics products were based on historical analysis and some queries can take hours to run. This clearly doesn’t meet the needs and pace of today’s business climate.
Some legacy products could in theory be extended, for example enhance a network packet capture tool to detect anomalies in traffic patterns. But it wouldn’t be any less than rewriting the core solution in order for it to scale and compute real-time analysis on the data.
As businesses do more of real time tracking and smart selling, many of them are building their own data management solutions.
Technically real time processing can be achieved in a few different ways including:
All approaches are valid, but present their own sets of challenges:
MPP implementations are very complex with multiple moving parts and need a lot of compute machines chained together over a shared network. In-memory processing is definitely getting popular with RAM cost decreasing steadily. In-database analytics and On-chip processing are a bit risky to build without deep subject matter expertise.
InfluxData helps companies get insight into what matters to them, in real-time, by helping them effectively manage their time-series data with targeted features like :
Real-time analysis means having to collect data from disparate systems, applications, datasources, services and infrastructure components. InfluxData’s Telegraf collector supports 30+ inputs and 10+ outputs and can be easily extended to support your sources of data. Telegraf makes collecting data in a format InfluxDB can consume, simple. Here’s why:
However, the InfluxData platform is extensible by design so you can easily integrate other collection agents like collectd, in conjunction with Telegraf.
Learn more about Telegraf
The most popular data type in any real-time analytics system is going to be in a time-series format. InfluxDB is designed from the ground up to handle just time-series data and to do it better than any other database. InfluxDB is the “I” in the TICK stack. More specifically, InfluxDB is an open source database written in Go to handle time-series data with high availability and high performance requirements. InfluxDB installs in minutes without external dependencies, yet is flexible and scalable enough for complex deployments. Here’s why InfluxDB is the best choice for storing a custom monitoring solution’s time-series data:
Learn more about InfluxDB
If you don’t already have a dashboarding or graphing UI in place, InfluxData provides Chronograf. It’s the “C” in the TICK stack. Chronograf is a downloadable binary you install behind your firewall to collaboratively, yet securely, perform ad-hoc visualizations on your time-series data. Features include:
Another visualization UI choice that offers tight integration with InfluxDB is the open source Grafana project. Either choice makes connecting to and visualizing time-series data, simple.
Inevitably, you are going to want to either alert on or in some way process the time-series data in your real-time analytics system. You’ll want to do this either before it gets written to InfluxDB or when it is retrieved. To address this need, the InfluxData platform ships with the open source Kapacitor project. Kapacitor is the “K” in the the TICK stack. It’s an alerting and data processing engine specifically designed for time-series data. It lets you define your own custom pipeline to aggregate, select, transform or otherwise process data and then store it back in InfluxDB or trigger an event. Features include:
Learn more about Kapacitor
InfluxData is used for real-time analytics by startups and large enterprises alike. Visit our Testimonials page for a comprehensive list.