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,
A new installment in the series about the tangled ball of thread that is YARN
In Part 1 of this series, we covered the fundamentals of clusters of YARN. In Part 2, you’ll learn about other components than can run on a cluster and how they affect YARN cluster configuration.
Ideal YARN Allocation
As shown in the previous post, a YARN cluster can be configured to use up all the resources on the cluster.
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.
Proper configuration of your Python environment is a critical pre-condition for using Apache Spark’s Python API.
One of the most enticing aspects of Apache Spark for data scientists is the API it provides in non-JVM languages for Python (via PySpark) and for R (via SparkR). There are a few reasons that these language bindings have generated a lot of excitement: Most data scientists think writing Java or Scala is a drag,
In this multipart series, fully explore the tangled ball of thread that is YARN.
YARN (Yet Another Resource Negotiator) is the resource management layer for the Apache Hadoop ecosystem. YARN has been available for several releases, but many users still have fundamental questions about what YARN is, what it’s for, and how it works. This new series of blog posts is designed with the following goals in mind:
- Provide a basic understanding of the components that make up YARN
- Illustrate how a MapReduce job fits into the YARN model of computation.