In this guide, learn how to use Cloudera Search with Basis Technology’s Rosette® to perform fuzzy name searches in multiple languages and scripts.
Our thanks to Basis Technology team (Jeanne Le Garrec, Hannah MacKenzie-Margulies and Brian Sawyer) for supporting writing this how-to blog.
Cloudera Search, powered by Apache Solr brings full-text, interactive search, and scalable indexing to Apache Hadoop by marrying SolrCloud with HDFS, Apache HBase,
This case study is an instructive example of how performance analysis is a multi-faceted process that often leads one in surprising directions.
Apache Solr Near Real Time (NRT) Search allows Solr users to search documents indexed just seconds ago. It’s a critical feature in many real-time analytics applications. As Solr indexes more and more documents in near real time, end-user expectations for performance get higher and higher.
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,
[Update: A new package for Apache Phoenix 4.7.0 on CDH 5.7 was released in June 2016.]
New Cloudera Labs packages for Apache Phoenix 4.5.2 (which includes Apache Spark integration) is now available for CDH 5.4.x and CDH 5.5.x.
Earlier this year, Cloudera announced the inclusion of Apache Phoenix in Cloudera Labs.
To recap: Phoenix adds SQL to Apache HBase,
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.