Tag Archives: apache hadoop

Sustained Innovation in Apache Spark: DataFrames, Spark SQL, and MLlib

Categories: CDH Spark

Cloudera has announced support for Spark SQL/DataFrame API and MLlib. This post explains their benefits for app developers, data analysts, data engineers, and data scientists.

In July 2015, Cloudera re-affirmed its position since 2013: that Apache Spark is on course to replace MapReduce as the default general-purpose data processing engine for Apache Hadoop. Thanks to initiatives like the One Platform Initiative,

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Cloudera Enterprise 5.5 is Now Generally Available

Categories: CDH Cloudera Manager

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;

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How-to: Ingest and Query “Fast Data” with Impala (Without Kudu)

Categories: Hadoop How-to Impala Kudu

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,

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How-to: Build a Complex Event Processing App on Apache Spark and Drools

Categories: HBase How-to Kafka Spark Use Case

Combining CDH with a business execution engine can serve as a solid foundation for complex event processing on big data.

Event processing involves tracking and analyzing streams of data from events to support better insight and decision making. With the recent explosion in data volume and diversity of data sources, this goal can be quite challenging for architects to achieve.

Complex event processing (CEP) is a type of event processing that combines data from multiple sources to identify patterns and complex relationships across various events.

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