This case study is an instructive example of how performance analysis is a multi-faceted process that often leads one in surprising directions.
Apache Solr Near Real Time (NRT) Search allows Solr users to search documents indexed just seconds ago. It’s a critical feature in many real-time analytics applications. As Solr indexes more and more documents in near real time, end-user expectations for performance get higher and higher.
Apache Hadoop is a proven platform for long-term storage and archiving of structured and unstructured data. Related ecosystem tools, such as Apache Flume and Apache Sqoop, allow users to easily ingest structured and semi-structured data without requiring the creation of custom code. Unstructured data, however, is a more challenging subset of data that typically lends itself to batch-ingestion methods. Although such methods are suitable for many use cases,
Thanks to Karthik Vadla, Abhi Basu, and Monica Martinez-Canales of Intel Corp. for the following guest post about using CDH for cost-effective processing/indexing of DICOM (medical) images.
Medical imaging has rapidly become the best non-invasive method to evaluate a patient and determine whether a medical condition exists. Imaging is used to assist in the diagnosis of a condition and, in most cases, is the first step of the journey through the modern medical system.
Thanks to Jonathan Natkins, a field engineer from StreamSets, for the guest post below about using StreamSets Data Collector—open source, GUI-driven ingest technology for developing and operating data pipelines with a minimum of code—and Cloudera Search and HUE to build a real-time search environment.
As pressure mounts on data engineers to deliver more data from more sources in less time, StreamSets Data Collector can serve as a linchpin in the data management process,
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,