Cloudera Director 1.5 is now available; this post describes what’s inside, including a new open source plugin interface.
Cloudera Director is the manifestation of Cloudera’s commitment to providing a simple and reliable way to deploy, scale, and manage Apache Hadoop in the cloud of your choice. With Cloudera Director 1.5, we continue the story of enabling production-ready clusters and big data applications by focusing on the following themes.
Our thanks to Karthik Vadla and Abhi Basu, Big Data Solutions engineers at Intel, for permission to re-publish the following (which was originally available here).
Data science is not a new discipline. However, with the growth of big data and adoption of big data technologies, the request for better quality data has grown exponentially. Today data science is applied to every facet of life—product validation through fault prediction,
Thrift client authentication and doAs impersonation, introduced in HBase 1.0, provides more flexibility for your HBase installation.
In the two-part blog series “How-to: Use the HBase Thrift Interface” (Part 1 and Part 2), Jesse Anderson explained the Thrift interface in detail, and demonstrated how to use it. He didn’t cover running Thrift in a secure Apache HBase cluster, however, because there was no difference in the client configuration with the HBase releases available at that time.
Learn about the architecture of Ibis, the roadmaps for Ibis and Impala, and how to get started and contribute.
We created Ibis, a new Python data analysis framework now incubating in Cloudera Labs, with the goal of enabling data scientists and data engineers to be as productive working with big data as they are working with small and medium data today. In doing so, we will enable Python to become a true first-class language for Apache Hadoop,
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,