Operations | Monitoring | ITSM | DevOps | Cloud

After a mortgage payment, energy costs are typically the largest household expense. In my case it was an easy decision to install solar panels, but I wanted to perform in-depth analyses with historical data. Deploying monitoring sensors was straightforward; collecting and processing the raw data became the main challenge. Telegraf and InfluxDB are ideal choices for managing time series data. Although I had no prior experience, a Docker instance of Telegraf was onboarded in no time.

Developers are always trying to improve the reliability and performance of their software, while at the same time reducing their own costs when possible. One way to accomplish this is edge computing and it’s gaining rapid adoption across industries. According to Gartner, only 10% of data today is being created and processed outside of traditional data centers.

Smart energy IoT platforms are empowering consumers to track energy usage and even control spend based on their next bill’s forecast. Yet eSoftThings, a specialist in the Internet of Things (IoT) and artificial intelligence (AI), set out to push smart energy management even further, for both consumers and utility companies, through its IoT platform eSoftLink.

If you’re an InfluxDB user you might be considering migrating your workload to InfluxDB Cloud. You probably want to free yourself from the responsibilities associated with managing and serving your OSS account. Perhaps you are finding that you simply cannot scale your OSS instance vertically to meet your needs. Maybe you want to use all of the Flux functions that are available to you in InfluxDB Cloud.

The community has spoken and the demand was clear: “BRING BACK THE INTERACTIVE SHELL USED IN 1.X” So it’s back… It works with InfluxDB V2… and has some improvements. The interactive shell allowed users to write data and interactively query data using InfluxQL. For newer users, InfluxQL is the SQL-like query engine that was native to the first major version of InfluxDB.

As more businesses generate and process data at the edge, the need to share data from edge nodes to a centralized cloud location increases. Replicating data from the edge to the cloud ensures consistency across an entire application and creates an uninterrupted historical record that preserves the critical context of time. Edge Data Replication (EDR) is a feature available in InfluxDB designed to address this challenge.

As the world continues to face unparalleled uncertainties due to climate change, using energy efficiently is more important than ever. Time series data plays a critical role in helping organizations operate in a greener and more sustainable way. In Finland, EnerKey operates a platform that drives sustainability and energy management to unearth savings from consumption data.

InfluxDB is very powerful for working with time series data, but learning to use any new tool can be intimidating. The fear of a steep learning curve can delay or even prevent people from using new tools that would ultimately make things easier and more efficient. Fortunately, InfluxDB has over a dozen client libraries so you can work with InfluxDB using a language you already know.

Geo-temporal data has both location and time. It’s used for all sorts of applications, from tracking shipments, to predicting weather patterns, to building fitness apps. It’s powerful because it lets you know where and when events happen, so you can understand them better and connect them with other events.