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,
Bet you didn’t know this: In some cases, Solr offers lightning-fast response times for business-style queries.
If you were to ask well informed technical people about use cases for Solr, the most likely response would be that Solr (in combination with Apache Lucene) is an open source text search engine: one can use Solr to index documents, and after indexing, these same documents can be easily searched using free-form queries in much the same way as you would query Google.
This new Cloudera Labs project promises to deliver the great Python user experience and ecosystem at Hadoop scale.
Across the user community, you will find general agreement that the Apache Hadoop stack has progressed dramatically in just the past few years. For example, Search and Impala have moved Hadoop beyond batch processing, while developers are seeing significant productivity gains and additional use cases by transitioning from MapReduce to Apache Spark.
Thanks to such advances in the ecosystem,
The best data protection strategy is to remove sensitive information from everyplace it’s not needed.
Have you ever wondered what sort of “sensitive” information might wind up in Apache Hadoop log files? For example, if you’re storing credit card numbers inside HDFS, might they ever “leak” into a log file outside of HDFS? What about SQL queries? If you have a query like select * from table where creditcard = ‘1234-5678-9012-3456’,
Cloudera Search combines the speed of Apache Solr with the scalability of CDH. Our newest training course covers this exciting technology in depth, from indexing to user interfaces, and is ideal for developers, analysts, and engineers who want to learn how to effectively search both structured and unstructured data at scale.
Despite being nearly 10 years old, Apache Hadoop already has an interesting history. Some of you may know that it was inspired by the Google File System and MapReduce papers,