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,
This new open source complement to HDFS and Apache HBase is designed to fill gaps in Hadoop’s storage layer that have given rise to stitched-together, hybrid architectures.
The set of data storage and processing technologies that define the Apache Hadoop ecosystem are expansive and ever-improving, covering a very diverse set of customer use cases used in mission-critical enterprise applications. At Cloudera, we’re constantly pushing the boundaries of what’s possible with Hadoop—making it faster,
YCSB, the open standard for comparative performance evaluation of data stores, is now available to CDH users for their Apache HBase deployments via new packages from Cloudera Labs.
Many factors go into deciding which data store should be used for production applications, including basic features, data model, and the performance characteristics for a given type of workload. It’s critical to have the ability to compare multiple data stores intelligently and objectively so that you can make sound architectural decisions.
The SparkOnHBase project in Cloudera Labs was recently merged into the Apache HBase trunk. In this post, learn the project’s history and what the future looks like for the new HBase-Spark module.
SparkOnHBase was first pushed to Github on July 2014, just six months after Spark Summit 2013 and five months after Apache Spark first shipped in CDH. That conference was a big turning point for me,
Learn about the near real-time data ingest architecture for transforming and enriching data streams using Apache Flume, Apache Kafka, and RocksDB at Santander UK.
Cloudera Professional Services has been working with Santander UK to build a near real-time (NRT) transactional analytics system on Apache Hadoop. The objective is to capture, transform, enrich, count, and store a transaction within a few seconds of a card purchase taking place. The system receives the bank’s retail customer card transactions and calculates the associated trend information aggregated by account holder and over a number of dimensions and taxonomies.