Category Archives: HBase

How-to: Create and Use a Custom Formatter in the Apache HBase Shell

Categories: Avro HBase How-to Tools

Learn how improve Apache HBase usability by creating a custom formatter for viewing binary data types in the HBase shell.

Cloudera customers are looking to store complex data types in Apache HBase to provide fast retrieval of complex information such as banking transactions, web analytics records, and related metadata associated with those records. Serialization formats such as Apache Avro, Thrift, and Protocol Buffers greatly assist in meeting this goal,

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New Apache Phoenix 4.5.2 Package from Cloudera Labs

Categories: Cloudera Labs HBase

(Ed. Note [Dec. 4 2015]: The post below has been edited to reflect the new release of Phoenix 4.5.2 for CDH 5.5.x.)

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,

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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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How-to: Index Scanned PDFs at Scale Using Fewer Than 50 Lines of Code

Categories: HBase How-to Search Spark

Learn how to use OCR tools, Apache Spark, and other Apache Hadoop components to process PDF images at scale.

Optical character recognition (OCR) technologies have advanced significantly over the last 20 years. However, during that time, there has been little or no effort to marry OCR with distributed architectures such as Apache Hadoop to process large numbers of images in near-real time.

In this post, you will learn how to use standard open source tools along with Hadoop components such as Apache Spark,

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Kudu: New Apache Hadoop Storage for Fast Analytics on Fast Data

Categories: Hadoop HBase HDFS Impala Kudu Performance Spark

This new open source complement to HDFS and Apache HBase is designed to fill gaps in Hadoop’s storage layer that have given rise to stitched-together, hybrid architectures.

The set of data storage and processing technologies that define the Apache Hadoop ecosystem are expansive and ever-improving, covering a very diverse set of customer use cases used in mission-critical enterprise applications. At Cloudera, we’re constantly pushing the boundaries of what’s possible with Hadoop—making it faster,

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