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What exactly is the Data Age? Well, there is no single definition of what this means - but my interpretation is that it refers to the fact that data can now be used as a foundation for decision making in every department of every business. And with the volume of data generated forecast to continue to grow exponentially up until 2025 according to IDC, the possibilities for using data to drive informed decision making are only going to increase.

In our previous two blogs, we provided an overview of the architecture and design of the Elasticsearch Go client and explored how to configure and customize the client. In doing so, we pointed to a number of examples available in the GitHub repository. The goal of these examples is to provide executable "scripts" for common operations, so it's a good idea to look there whenever you're trying to solve a specific problem with the client.

For public IPs, it is possible to create tables that will specify which city specific ranges of IPs belong to. However, a big portion of the internet is different. There are company private networks with IP addresses of the form 10.0.0.0/8, 172.16.0.0/12 or 192.168.0.0/16 scattered in every country in the world. These IP addresses tend to have no real information for the geographic locations.

Over the last decade, data collection has become a commodity. Consequently, there has been a tremendous deluge of data in every area of industry. This trend is captured by recent research, which points to growing volume of raw data and growth of market segments fueled by that data growth.

In this post, we learn how to use the reduce(), findColumn(), and findRecord() Flux functions to perform custom aggregations with InfluxDB. This TL;DR assumes that you have either registered for an InfluxDB Cloud account – registering for a free account is the easiest way to get started with InfluxDB – or installed InfluxDB 2.0 OSS. In order to easily demonstrate how these functions work, let’s use the array.from() function to build an ad hoc table to use in the query.

If you’re new to the Dashboards Beta app on Splunkbase and you’re trying to get started with building beautiful dashboards, this "Dashboards Beta" blog series is a great place to start. The Splunk Dashboards app (beta) brings a new dashboard framework, intended to combine the best of Simple XML and Glass Tables, and provide a friendlier experience for creating and editing dashboards.

It’s back to school season, and oftentimes, that means people are purchasing TI-84 calculators for their kids. But did you know that Texas Instruments makes so much more than calculators? 😁 Michael Hinkle, a Probe Engineering and Manufacturing Supervisor at Texas Instruments, recently presented on “How Texas Instruments Uses InfluxDB to Upload Product Standards and to Improve Efficiencies”.

As work from home has ballooned in 2020, virtual methods for communicating with colleagues have become more critical than ever. Same goes for all the useful productivity and collaboration tools at our disposal. The emerging downside is the difficulty of finding needed information among so many tools. Compounding the problem is the tendency for info to get siloed off by department.

InfluxDB clustering is available as part of InfluxData’s commercial product offered in both managed and self-managed capacities.

This tutorial will walk you through sending StatsD metrics to Telegraf. StatsD is a simple protocol for sending application metrics via UDP. These metrics can be sent to a Telegraf instance, where they are aggregated and periodically flushed to InfluxDB or other output sinks that you have configured. At the time of writing, we have 37 different output plugins supported.