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,
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,
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.
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.
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.