Cloudera Enterprise 5.5 (comprising CDH 5.5, Cloudera Manager 5.5, and Cloudera Navigator 2.4) has been released.
Cloudera is excited to bring you news of Cloudera Enterprise 5.5. Our persistent emphasis on quality is especially pronounced in this release, with more than 500 issues identified and triaged during its development.
A highlight of this release is the inclusion of Cloudera Navigator Optimizer (available in limited beta for select Cloudera Enterprise customers;
Impala is designed to deliver insight on data in Apache Hadoop in real time. As data often lands in Hadoop continuously in certain use cases (such as time-series analysis, real-time fraud detection, real-time risk detection, and so on), it’s desirable for Impala to query this new “fast” data with minimal delay and without interrupting running queries.
In this blog post, you will learn an approach for continuous loading of data into Impala via HDFS,
Learn the details about using Impala alongside Kudu.
Kudu (currently in beta), the new storage layer for the Apache Hadoop ecosystem, is tightly integrated with Impala, allowing you to insert, query, update, and delete data from Kudu tablets using Impala’s SQL syntax, as an alternative to using the Kudu APIs to build a custom Kudu application. In addition, you can use JDBC or ODBC to connect existing or new applications written in any language,
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 former Cloudera intern Jose Cambronero for the post below about his summer project, which involved contributions to MLlib in Apache Spark.
Data can come in many shapes and forms, and can be described in many ways. Statistics like the mean and standard deviation of a sample provide descriptions of some of its important qualities. Less commonly used statistics such as skewness and kurtosis provide additional perspective into the data’s profile.