Operations | Monitoring | ITSM | DevOps | Cloud

MQTT is a messaging protocol used widely in the IoT space. Its publish/subscribe model is ideal for IoT because multiple devices can send data to a single MQTT broker, which MQTT clients can then access. The MQTT integration for InfluxDB’s native collector feature is a fast and easy way to create cloud-to-cloud data pipelines. Here’s the lowdown on using the Native MQTT collector. Who: Users and companies that rely on MQTT to provide time series data for their applications.

We love to write and ship code to help developers bring their ideas and projects to life. That’s why we’re constantly working on improving our product in sync with developer needs to ensure their happiness and accelerate Time To Awesome. This month is special. We have many features that we think you will love when onboarding or continuing to use InfluxDB. We launched Native Data Collectors this month.

This article was originally published in The New Stack and is reposted here with permission. You might need to perform anomaly detection or forecasting if you’re working with time-series data. The first step before working on your time series is finding the right data store. To effectively detect or forecast your data, you will require a data store that can handle a large volume of data at a high ingest rate. Therefore, you might want to look at using a purpose-built time-series database.

Cloud adoption has been on an upward trajectory for over a decade with no signs of slowing down. As widescale migration becomes the norm, organizations are realizing cloud financial management — also referred to as FinOps — is critical to creating long term value in the cloud. Building a culture of financial discipline requires visibility and a strategy for measuring success along the way.

This article was written by Thamatam Vijay Kumar. Scroll down for author bio and photo. Modern-day websites are filled with dashboards featuring enriched charts, line graphs, radar as well as multigraphs. The world is fascinated with such charts and graphs, which deliver much value to millennial web applications. There are many such chart libraries which provide interactive visualization and deliver data insights for users. The charts plot the lines using data points.

A wide variety of industrial processes rely on water, and before it can be used, it needs to be treated to remove dissolved substances. Minerals have to be filtered out so they don’t form scales on equipment as water is heated and cooled, and bacteria needs to be removed in cases involving human health. Ekopak is a Belgian company working to make water treatment more sustainable by using less water and energy where possible.

In this tutorial we’ll learn how to use Python to get time series data from the OpenWeatherMap API and convert it to a Pandas DataFrame. Next we’ll write that data to InfluxDB, a time-series data platform, with the InfluxDB Python Client. We’ll convert the JSON response from our API call to a Pandas DataFrame because I find that that’s the easiest way to write data to InfluxDB.

Achieving cost savings is one of the main drivers for cloud adoption. But for most companies, controlling cloud spend is much more challenging than anticipated. In a recent survey, 94% of IT decision makers report they are overspending in the cloud. Our own survey on cloud costs revealed 90% of executives say better cloud cost management and cost reduction is a top priority.