One of the really interesting topics in business intelligence is the desire for organizations to take action from data. While most use dashboards as a source of information, it certainly doesn't prompt or drive them to act. This means you can’t close the loop on your insights and the decisions that they drive.

We are excited to release Deep Learning for Anomaly Detection, the latest applied machine learning research report from Cloudera Fast Forward Labs. Anomalies, often referred to as outliers, are data points or patterns in data that do not conform to a notion of normal behavior. Anomaly detection, then, is the task of finding those patterns in data that do not adhere to expected norms. The capability to recognize or detect anomalous behavior can provide highly useful insights across industries.

One of the first recorded medical devices was the stethoscope in 1816. Fast forward more than a century to 2019, where the world witnessed the creation of an award-winning multi-sensor, implantable cardiac device able to predict potential heart failure weeks in advance. The data and analytics streamed and analyzed from new connected devices are transforming healthcare as we know it. However, a real challenge in this environment is the sheer volume and scope of data that must be managed and protected.

With so much packed into the latest Yellowfin 9 release, we figured it would be great to let you know about some of the coolest features (which will really transform how you do analytics!) in this series of blog posts - Fresh Features. At Yellowfin, we’re super excited that you will be benefiting from the huge amount of work our development team have been doing behind the scenes to complete revamp Yellowfin’s look, feel, and functionality.

Let’s try to figure out what happens with the application when the source file is much bigger than the available memory. The memory in the below tests is limited to 900MB […]. Naively we could think that a file bigger than available memory will fail the processing with OOM memory error. And this supposition is true.

One of the core reasons that organizations invest in analytic solutions is because they want to get everyone in their organization on the same page. They want everyone to understand what's happening and why it's happening so that individuals know what they need to do to be successful and drive outcomes for the organization.

Like many other people, I used time over the recent holidays to clean out and organize my digital files. In that process, I finally trashed the speaking notes for a panel I participated in at SMA’s (Strategy Meets Action) first summit in 2012 when I worked at a large global insurer. During that session, a gentleman in the audience asked me what I thought about “big data” and its implications for Insurance.

The opportunity to create new economic, social and environmental value by unlocking the “good” in data is immense. While the problems we face as a society may be getting harder to solve, the advances we can make when we break down the silos between the physical and digital worlds are profound.

Once upon a time there was only one way to use Apache Spark but support for additional programming languages and APIs have been introduced in recent times. A novice can be confused by the different options that have become available since Spark 1.6 and intimidated by the idea of setting up a project to explore these APIs.