Are you looking to get started with InfluxDB on Google Cloud? We’ve pulled together our top resources to help you get the most out of your time series data whether that’s coming from your Google Cloud infrastructure or your own application. Read how customers like Wayfair and Vera C. Rubin Observatory use InfluxDB on Google Cloud to solve their time series data collection and processing challenges to power their multifaceted, complex real-world use cases.

Last week we released Telegraf 1.18 with a range of new plugins including Elastic Beats, directory monitoring, NFS, XML parsing and some aggregators and processors to help with your data ingestion. All of these packages were written in Go 1.16.2. This was one of our largest releases in a while and couldn’t have been done without the 70+ Telegraf community members who contributed to writing plugins, fixing bugs, reviewing code, and everything else to improve Telegraf!

Giraffe is InfluxData’s graphing library, built to use and graph the data coming from InfluxData’s time series database, InfluxDB. Yes, there are other graphing libraries available; but ours is the only one purpose-built to graph line protocol without having to convert it. Plus, we have lots of great features, like legends and colorization, without much configuration. So, how to get started?

Are you interested in performing time series forecasting or anomaly detection, but you don’t know where to start? If so, you’re not alone. There is an overwhelming variety of libraries, algorithms, and workflow recommendations for these tasks. As a Developer Advocate at InfluxDB, the leading time series database, I’ve researched time series data science methodologies and best practices for forecasting and anomaly detection.

With InfluxDB you can use Tasks to process data on a schedule. You can also use tasks to write custom alerts. However, sometimes your task will fail. In this TLDR, we’ll learn how to debug your task with the InfluxDB UI and the InfluxDB CLI.

If you’re familiar with Telegraf, you know that you can easily configure this lightweight collection agent with a single TOML configuration file to gather metrics from over 180 inputs and write data to a wide variety of different outputs and/or platforms. You might also know that Telegraf can act as a processor, aggregator, parser, and serializer.

Data platforms — or databases with sets of APIs for flexibly working with data — are quintessential backbones for those who rely heavily on being able to change how they obtain data and work with their data over time. A good data platform will provide you the necessary tools to glean the insights you need to solve tangible problems. That platform should also hopefully make it so you don’t have a bad time doing it!