What is big data analytics? Fast answers from diverse data sets

There’s data, and then there’s big data. So, what’s the difference? Big data defined A clear big data definition can be difficult to pin down because big data can cover a multitude of use cases. But in general the term refers to sets of data that are so large in volume and so complex that… Continue reading What is big data analytics? Fast answers from diverse data sets

Semi-supervised learning explained

In his 2017 Amazon shareholder letter, Jeff Bezos wrote something interesting about Alexa, Amazon’s voice-driven intelligent assistant: In the U.S., U.K., and Germany, we’ve improved Alexa’s spoken language understanding by more than 25% over the last 12 months through enhancements in Alexa’s machine learning components and the use of semi-supervised learning techniques. (These semi-supervised learning… Continue reading Semi-supervised learning explained

How Qubole addresses Apache Spark challenges

Traditional relational databases have been highly effective at handling large sets of structured data. That’s because structured data conforms nicely to a fixed schema model of neat columns and rows that can be manipulated using SQL commands to establish relationships and obtain results. Then big data came along. To read this article in full, please… Continue reading How Qubole addresses Apache Spark challenges

Deep learning frameworks: PyTorch vs. TensorFlow

Not every regression or classification problem needs to be solved with deep learning. For that matter, not every regression or classification problem needs to be solved with machine learning. After all, many data sets can be modeled analytically or with simple statistical procedures. To read this article in full, please click here (Insider Story) from… Continue reading Deep learning frameworks: PyTorch vs. TensorFlow

How to do real-time analytics across historical and live data

Today’s analytical requirements are putting unprecedented pressures on existing data infrastructures. Performing real-time analytics across operational and stored data is typically critical to success but always challenging to implement. To read this article in full, please click here (Insider Story) from InfoWorld Big Data https://ift.tt/31CzJff via IFTTT

HPE plus MapR: Too much Hadoop, not enough cloud

Cloud killed the fortunes of the Hadoop trinity—Cloudera, Hortonworks, and MapR—and that same cloud likely won’t rain success down on HPE, which recently acquired the business assets of MapR. While the deal promises to marry “MapR’s technology, intellectual property, and domain expertise in artificial intelligence and machine learning (AI/ML) and analytics data management” with HPE’s… Continue reading HPE plus MapR: Too much Hadoop, not enough cloud