This post from the HUE team about using HUE (the open source web GUI for Apache Hadoop), Apache Spark, and SQL for analytics was initially published in the HUE project’s blog.
Apache Spark is getting popular and HUE contributors are working on making it accessible to even more users. Specifically, by creating a Web interface that allows anyone with a browser to type some Spark code and execute it.
Thanks to Michal Malohlava, Amy Wang, and Avni Wadhwa of H20.ai for providing the following guest post about building ML apps using Sparkling Water and Apache Spark on CDH.
The Sparkling Water project is nearing its one-year anniversary, which means Michal Malohlava, our main contributor, has been very busy for the better part of this past year. The Sparkling Water project combines H2O machine-learning algorithms with the execution power of Apache Spark.
This new open source complement to HDFS and Apache HBase is designed to fill gaps in Hadoop’s storage layer that have given rise to stitched-together, hybrid architectures.
The set of data storage and processing technologies that define the Apache Hadoop ecosystem are expansive and ever-improving, covering a very diverse set of customer use cases used in mission-critical enterprise applications. At Cloudera, we’re constantly pushing the boundaries of what’s possible with Hadoop—making it faster,
This new core security layer provides a unified data access path for all Hadoop ecosystem components, while improving performance.
We’re thrilled to announce the beta availability of RecordService, a distributed, scalable, data access service for unified access control and enforcement in Apache Hadoop. RecordService is Apache Licensed open source that we intend to transition to the Apache Software Foundation. In this post, we’ll explain the motivation, system architecture,
Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50% compared to replication while maintaining the same durability guarantees. This post explains how it works.
HDFS by default replicates each block three times. Replication provides a simple and robust form of redundancy to shield against most failure scenarios. It also eases scheduling compute tasks on locally stored data blocks by providing multiple replicas of each block to choose from.