Cloudera Data Science Workbench provides data scientists with secure access to enterprise data with Python, R, and Scala. In the previous article, we introduced how to use your favorite Python libraries on an Apache Spark cluster with PySpark. In Python world, data scientists often want to use Python libraries, such as XGBoost, which includes C/C++ extension. This post shows how to solve this problem creating a conda recipe with C extension.
Cloudera Search (that is Apache Solr integrated with the Apache Hadoop eco-system) now supports (as of C5.9) a backup and disaster recovery capability for Solr collections.
In this post we will cover the basics of the backup and disaster recovery capability in Solr and hence in Cloudera Search. In the next post we will cover the design of the Solr snapshots functionality and its integration with the Hadoop ecosystem as well as public cloud platforms (e.g.
As companies strive to implement modern solutions based on deep learning frameworks, there is a need to deploy it on existing hardware infrastructure in a scalable and distributed manner comes to the fore. Recognizing this need, Cloudera’s and Intel’s Big Data Technologies engineering teams jointly detail Intel’s BigDL Apache Spark deep learning library on the latest release of Cloudera’s Data Science Workbench. This collaborative effort allows customers to build new deep learning applications with BigDL Spark Library by leveraging their existing homogeneous compute capacity of Xeon servers running Cloudera’s Enterprise without having to invest in expensive GPU farms and bringing up parallel frameworks such as TensorFlow or Caffe.
Cloudera Data Science Workbench provides freedom for data scientists. It gives them the flexibility to work with their favorite libraries using isolated environments with a container for each project.
In JVM world such as Java or Scala, using your favorite packages on a Spark cluster is easy. Each application manages preferred packages using fat JARs, and it brings independent environments with the Spark cluster. Many data scientists prefer Python to Scala for data science,
Organizations analyze logs for a variety of reasons. Some typical use cases include predicting server failures, analyzing customer behavior, and fighting cybercrime. However, one of the most overlooked use cases is to help companies write better software. In this digital age, most companies write applications, be it for its employees or external users. The cost of faulty software can be severe, ranging from customer churn to a complete firm’s demise, as was the case with Knight Capital in 2012.