MQTT is becoming the standard protocol for applications that operate in environments where network connectivity is intermittent or unreliable, reducing bandwidth usage is a priority, or where hardware resources are limited. In this post you will learn about some specific use cases where businesses are seeing value from making MQTT part of their tech stack.

Databases are often the biggest bottleneck when it comes to application performance. Over the years a number of new database designs have emerged to help with not only basic scalability and performance but also to help improve developer productivity and make building certain types of applications easier. That isn’t to say these new databases are magical — there are always trade-offs being made and certain things are sacrificed for gains in other areas.

In an ideal world, developers would be able to release new products and features from development environments into production extremely fast while also not having to stress about breaking prod. Achieving this combination of development speed while also maintaining software reliability requires having the right toolchain and automation in place.

When it comes to IoT data, MQTT is a superstar. With so many IoT devices generating data out in the world, developers need ways to access it. After all, data lies at the heart of every application. But data doesn’t just magically manifest itself into your datastore, and building the right data pipeline can make or break an application. Data collection is not a one-size-fits-all problem to solve.

MQTT is a machine-to-machine communication protocol. Devices publish messages to a broker under specific topics, and other devices subscribe to those topics to receive information. It’s popular because it doesn’t take up a lot of bandwidth, so IoT devices with limited network connectivity can use it. MQTT works because of brokers. Each device sending and receiving data can communicate with potentially millions of other devices while only connecting to one broker.

The Internet of Things (IoT) describes devices with sensors and computational ability which let them collect, exchange, and act on data. IoT is a broad category that includes uses from smart home thermostats to industrial manufacturing equipment. Sensor data is time series data, and IoT is a common use case for InfluxDB because it can handle the huge amounts of data IoT sensors create.

This article was originally published in The New Stack and is reposted here with permission. Hundreds of billions of sensors produce vast amounts of time series data every day. The sheer volume of data that companies collect makes it challenging to analyze and glean insights. Machine learning drastically accelerates time series data analysis so that companies can understand and act on their time series data to drive significant innovation and improvements.

An enterprise IT company hosted a large industry event that drew attendees from all around the globe, including key technology leaders. Organizers knew that their IT offerings needed to be top notch to ensure attendees were happy when it came to event experience. The event application allowed attendees to browse and register for sessions at the event. So, organizers needed to be able to identify issues in real-time and fix them quickly.

Learn how to deploy InfluxDB Cloud’s Native Collectors with Kepware and The Things Network. Did you hear about the new feature that just dropped to InfluxDB Cloud? Native Collectors! Starting with MQTT. There will be plenty of content to get you started with Native Collectors. So this blog series covers connecting two popular IoT-based platforms to InfluxDB Cloud using native Collectors. One Enterprise use case and one industrial use case.