IDG Contributor Network: Dawn of intelligent applications

Data remains a foundational element of computing. Recently, Hadoop and big data have been a central part of data progression, allowing you to capture data at scale. But companies now look to the expanding use of cloud computing and machine learning to create more intelligent applications. This new generation of applications use all the data… Continue reading IDG Contributor Network: Dawn of intelligent applications

IDG Contributor Network: Dataops: agile infrastructure for data-driven organizations

About a decade ago, the software engineering industry reinvented itself with the development and codification of so-called devops practices. Devops, a compound of “development” and “operations,” refers to a set of core practices and processes that aim to decrease time to market by thoughtfully orchestrating the tight integration between software developers and IT operations, emphasizing… Continue reading IDG Contributor Network: Dataops: agile infrastructure for data-driven organizations

IDG Contributor Network: AI: the challenge of data

In the last few years, AI has made breathtaking strides driven by developments in machine learning, such as deep learning. Deep learning is part of the broader field of machine learning that is concerned with giving computers the ability to learn without being programmed. Deep learning has had some incredible successes. Arguably, the modern era… Continue reading IDG Contributor Network: AI: the challenge of data

IDG Contributor Network: In 2018, can cloud, big data, and AI stand more turmoil?

The amount of new technologies in 2017 has been overwhelming: The cloud was adopted faster than analysts projected and brought several new tools with it; AI was introduced into just about all areas of our lives; IoT and edge computing emerged; and a slew of cloud-native technologies came into fruition, such as Kubernetes, serverless, and… Continue reading IDG Contributor Network: In 2018, can cloud, big data, and AI stand more turmoil?

Julia vs. Python: Julia language rises for data science

Of the many use cases Python covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient. But for the developers behind the Julia language — aimed specifically at “scientific computing, machine learning,… Continue reading Julia vs. Python: Julia language rises for data science

In the rush to big data, we forgot about search

I ready David Linthicum’s post ”Data integration is the one thing the cloud makes worse” with great interest. A huge reason that I decided my next job would be for a search company was because of this very problem. (That’s why I now work for LucidWorks, which produces Solr– and Spark-based search tools.) While working… Continue reading In the rush to big data, we forgot about search

IDG Contributor Network: The clash of big data and the cloud

Recently, I visited a few conferences and I noticed a somewhat hidden theme. While a lot of attention was being paid to moving to a (hybrid) cloud-based architecture and what you need for that (such as cloud management platforms), a few presentations showed an interesting overall development that everybody acknowledges but that does not get… Continue reading IDG Contributor Network: The clash of big data and the cloud