Cloudera Enterprise 5.9 includes the latest release of Hue (3.11), the web UI that makes Apache Hadoop easier to use.
As part of Cloudera’s continuing investments in user experience and productivity, Cloudera Enterprise 5.9 includes a new release of Hue. Hue continues its focus on SQL and also now makes your interaction with the Cloud easier (Amazon S3 specifically in this first version). We’ll provide a summary of the main improvements in the following part of this blog post.
Cloudera Enterprise 5.5 (comprising CDH 5.5, Cloudera Manager 5.5, and Cloudera Navigator 2.4) has been released.
Cloudera is excited to bring you news of Cloudera Enterprise 5.5. Our persistent emphasis on quality is especially pronounced in this release, with more than 500 issues identified and triaged during its development.
A highlight of this release is the inclusion of Cloudera Navigator Optimizer (available in limited beta for select Cloudera Enterprise customers;
Combining CDH with a business execution engine can serve as a solid foundation for complex event processing on big data.
Event processing involves tracking and analyzing streams of data from events to support better insight and decision making. With the recent explosion in data volume and diversity of data sources, this goal can be quite challenging for architects to achieve.
Complex event processing (CEP) is a type of event processing that combines data from multiple sources to identify patterns and complex relationships across various events.
This post from the HUE team about using HUE (the open source web GUI for Apache Hadoop), Apache Spark, and SQL for analytics was initially published in the HUE project’s blog.
Apache Spark is getting popular and HUE contributors are working on making it accessible to even more users. Specifically, by creating a Web interface that allows anyone with a browser to type some Spark code and execute it.
Learn how to use OCR tools, Apache Spark, and other Apache Hadoop components to process PDF images at scale.
Optical character recognition (OCR) technologies have advanced significantly over the last 20 years. However, during that time, there has been little or no effort to marry OCR with distributed architectures such as Apache Hadoop to process large numbers of images in near-real time.
In this post, you will learn how to use standard open source tools along with Hadoop components such as Apache Spark,