A quick conversation with most Chief Information Security Officers (CISOs) reveals they understand they need to modernize their security architecture and the correct answer is to adopt a machine learning and analytics platform as a fundamental and durable part of their data strategy. However, many CISOs fear deployment of an initial use case will be somewhat daunting. Cloudera has partnered along with Arcadia Data and StreamSets to make it easier than ever for CISOs to take the first step and deploy basic use cases leveraging data sources common to many environments.
sparklyr is a great opportunity for R users to leverage the distributed computation power of Apache Spark without a lot of additional learning. sparklyr acts as the backend of dplyr so that R users can write almost the same code for both local and distributed calculation over Spark SQL.
Since sparklyr v0.6, we can run R code across our Spark cluster with spark_apply().
This article shows how to build and publish a customized Docker image for usage as an engine in Cloudera Data Science Workbench. Such an image or engine customization gives you the benefit of being able to work with your favorite tool chain inside the web based application.
Cloudera Data Science Workbench (CDSW) enables data scientists to use their favorite tools such as R, Python, or Scala based libraries out of the box in an isolated secure sandbox environment.
As customers use Apache Hadoop clusters in ways other than through HUE and Hadoop Command Line Interface (CLI) and integrate it closely with the applications they develop, we often get asked how to access their secure Hadoop cluster from within the custom applications. Many customers use a service account in their application and access the cluster with a fixed service account. However, other customers would like to access as the end users who have authenticated to the application.
When most people think of Big Data, often they imagine loads of unstructured data. However, there is always some sort of structure or relationships within this data. Based on these relationships there are one or more representation schemes best suited to handle this type of data. A common pattern seen in the field is hierarchy/relationship representation. This form of representation is adept in handling scenarios like complex business models, chain of event or plans, chain of stock orders in banks,