“In 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity,” according to Gartner. It will do so largely by learning how to make better predictions over time and supplementing people’s ability to complete tasks in more natural ways
The modern data landscape demands more than one type of database. That’s IBM has rolled out JSON-document-based databases in Db2 and Cloudant, as well as partnered with select database providers to offer developer-focused database services through the IBM Compose platform.
Thanks to the democratization of data, rising numbers of businesses are making highly insightful decisions that are producing beneficial real-world results. Catch a few highlights from a podcast that takes a deep dive into how open source analytics processing engines and high volumes of data are
The really cool thing about big data and multiple data sources for today’s advanced, web-based applications is that a variety of open source databases can provide specialized support for different application components. If you’re involved in application development, discover why achieving polyglot
Spark’s momentum is building, and it is rapidly emerging as the central technology in analytics ecosystems within organizations. See why Spark’s technical advancements around iterative processing combined with its easy overall environment and tool set for developers make it a true operating system
On the heels of several key announcements to broaden the IBM Cloud Data Services portfolio, see how a wide range of technologies can be implemented in a cloud-based, data warehouse architecture to support operational and analytical workloads.
Cloud-based open source databases offer developers agility, but quite often no single database can meet all their needs. See how adopting a multi-database strategy empowers developers to build web and mobile applications quickly and economically without the cost and distractions that come with
In the big data scheme of things, you can talk about the "3 Vs," the "4 Vs," or as many "Vs" as your fevered imagination can spin out. The "3 Vs" point to the "big" dimension of the big data phenomenon, but when you shift the focus from "big" data to "all" data, the fourth V becomes a cleaner fit.