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Refcard #317

Advanced Time Series

As we enter the era of workflow automation, machine learning, and artificial intelligence, collecting and monitoring time series data is becoming more and more essential. This Refcard reviews use cases and case studies across industries and walks you through the process of collecting time series metrics from a host.

1,447

Brought to you by

InfluxData
Free .PDF for easy Reference

Written by

Daniella Pontes Sr. Manager Product Marketing, InfluxData
Refcard #317

Advanced Time Series

As we enter the era of workflow automation, machine learning, and artificial intelligence, collecting and monitoring time series data is becoming more and more essential. This Refcard reviews use cases and case studies across industries and walks you through the process of collecting time series metrics from a host.

1,447
Free .PDF for easy Reference

Written by

Daniella Pontes Sr. Manager Product Marketing, InfluxData

Brought to you by

InfluxData
Table of Contents

Introduction

What Is Time Series Data and Where Is It Taking Us?

Section 1

Introduction

More than four years ago, InfluxDB — an open source time series platform — was launched. In the years since, time series technology has become increasingly popular; according to DB-Engines, over the last 24 months, time series has been the fastest growing database category. This popularity is fueled by the "sensorification" of the physical world (i.e., IoT) and the rapidly increasing instrumentation requirements of the next generation of software. InfluxDB has millions of downloads, an expanding list of enterprise customers, and a growing community that is always finding new ways to build on the platform — and we are just scratching the surface.

As we enter the era of workflow automation, machine learning, and artificial intelligence, it is time for time series data.

Section 2

What Is Time Series Data and Where Is It Taking Us?

In a previous Refcard, we talked about how time series has been used broadly as a tool to understand change and behavior. For instance, we use time series to generate and observe economic indexes and market performance, environment degradation, growth rate of social media, etc. So, what's new? Why the new growing interest in understanding something that we have already used for so long?

Behind much of the interest in understanding time series better is the volume at which we are collecting time series data of all sorts. From the physical world, we have sensors in manufacturing and energy generating plants, as well as fleets of personal devices, all generating tons of data. From the virtual world, we have been instrumenting software metrics, events, and logs. With the containerization of applications, the number of collected measurements exploded. To make matters worse, the sampling is increasingly done at very fine intervals, all the way down to nanosecond granularity. Although this is an eye opener, volume alone doesn't fully explain the renewed focus on time series data. The fundamental question persists: why have we gotten into this "frenzy" mode of collecting time series data about everything to which we have access?

This is a preview of the Advanced Time Series Refcard. To read the entire Refcard, please download the PDF from the link above.

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