The Apache HBase Medium Object Storage (MOB) feature was introduced by HBASE-11339. This feature improves low latency read and write access for moderately-sized values (ideally from 100K to 10MB based on our testing results), making it well-suited for storing documents, images, and other moderately-sized objects . The Apache HBase MOB feature achieves this improvement by separating IO paths for file references and MOB objects, applying different compaction policies to MOBs and thus reducing write amplification created by HBase’s compactions.
Starting in Cloudera Enterprise 5.5, Cloudera Navigator offers interactive visual analytics that help answer important questions about the data that’s in your CDH clusters.
The new analytics system in Cloudera Navigator shows the distribution of data along various metadata dimensions and supports interactive filtering and grouping with a simple point-and-click interface. This new functionality a great complement to Cloudera Navigator’s search capabilities and is integrated with Navigator’s policy engine, so you can easily understand the impact of data management policies before applying them to your data.
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;
The Impala project has already passed several important milestones on the way to its status as the leader and open standard for BI and SQL analytics on modern big data architecture. Today’s milestone is the submission of proposals for Impala and Kudu to join the Apache Software Foundation (ASF) Incubator.
[Update: Read the text of the Impala and Kudu proposals here and here, respectively.]
Since its initial release nearly five years ago,
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,