Companies are investing heavily in the cloud for the operational and financial benefits. But without a robust cloud cost management strategy in place, the complexity of cloud services and billing can to overspending and unnecessary cloud waste. Being able to accurately predict future cloud spend is one way to more optimize cloud spend and inform budgets.
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.
Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week we’re exploring the ‘hidden’ environmental impact of our data-driven world.
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.
Cloud spend — which research shows makes up 51% of IT budgets — is a prime candidate for company cost savings initiatives with the potential to make a huge difference in gross margins. It’s also an area that has grown dramatically in the last few years due to digital transformation and a rise in cloud demand during the pandemic.
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.
