Functions-as-a-Service (FaaS) bring to the cloud a set of properties that are central to the serverless computing promise: little to no concern about infrastructure operations, auto provisioning and auto scaling, and pay-per-use with zero cost for idle time. While these benefits are driving the growth of FaaS, developers are quickly realizing they need a better programming […]
In this post, I’ll share technical details and code samples to help you to create your very own Fitness App solution. If you want to further customize it or add specialized features, you can also go ahead and connect it to other services and APIs (like we did with the location mapping API).
InfluxData is proud to announce that its InfluxCloud managed service for time-series data is now available on Bluemix. Rooted in open source, the InfluxData platform is built specifically for metrics, events, and other time-based data. In other words, a true modern time-series platform.
Interest in containers has surged in the DevOps community since IBM co-founded the Cloud Native Computing Foundation in 2015. Innovation in open cloud technology creates challenges with interoperability and integration. The Foundation is run by developers, for developers to minimize those challenges by promoting cloud native applications and services.
With cases of both type I and type II diabetes rising, Medtronic recognized the need to create a new generation of glucose monitoring solutions that would give people the tools to manage their diabetes more easily, in combination with routine support from healthcare professionals. Find out how they are working with IBM Watson to help.
In recent years, technologies that enable organizations to capture, process, ingest and analyze large volumes of data at high velocity have become increasingly important.
If you were to make a list of the most-hyped topics in enterprise technology right now, it’s likely that the Internet of Things (IoT), big data and event-driven applications would be near the top of it. In many cases, these types of solutions share a common factor: the need to capture, analyze and act on fast-moving, high-volume streams of data—whether that data is generated by IoT sensors, user activity on the web, or traditional transactional data such as trading on financial markets.
According to the recent Bloor Research Report, the Internet of Things is driving wider industry adoption and propelling streaming analytics into the mainstream. There is a rapid increase in vendor and market activity in the Industrial Internet of Things (IIoT) and M2M, with significant deployments of streaming analytics in sectors such as healthcare, smart cities, smart energy, industrial automation, oil and gas, logistics and transportation. The characteristics of streaming analytics are particularly suited to the processing of sensor data: the combination of time-based and location-based data analysis in real-time over short time windows, the ability to filter, aggregate and transform live data, and to do so across a range of platforms from small edge appliances to distributed, fault-tolerant cloud clusters. Sensor data volumes have already reached a level where streaming analytics is a necessity, not an option.