Category Archives: HBase

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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YCSB, the Open Standard for NoSQL Benchmarking, Joins Cloudera Labs

Categories: Cloudera Labs HBase Performance

YCSB, the open standard for comparative performance evaluation of data stores, is now available to CDH users for their Apache HBase deployments via new packages from Cloudera Labs.

Many factors go into deciding which data store should be used for production applications, including basic features, data model, and the performance characteristics for a given type of workload. It’s critical to have the ability to compare multiple data stores intelligently and objectively so that you can make sound architectural decisions.

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Apache Spark Comes to Apache HBase with HBase-Spark Module

Categories: Cloudera Labs Community HBase Spark

The SparkOnHBase project in Cloudera Labs was recently merged into the Apache HBase trunk. In this post, learn the project’s history and what the future looks like for the new HBase-Spark module.

SparkOnHBase was first pushed to Github on July 2014, just six months after Spark Summit 2013 and five months after Apache Spark first shipped in CDH. That conference was a big turning point for me,

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Inside Santander’s Near Real-Time Data Ingest Architecture

Categories: Flume HBase Kafka

Learn about the near real-time data ingest architecture for transforming and enriching data streams using Apache Flume, Apache Kafka, and RocksDB at Santander UK.

Cloudera Professional Services has been working with Santander UK to build a near real-time (NRT) transactional analytics system on Apache Hadoop. The objective is to capture, transform, enrich, count, and store a transaction within a few seconds of a card purchase taking place. The system receives the bank’s retail customer card transactions and calculates the associated trend information aggregated by account holder and over a number of dimensions and taxonomies.

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Designing Fraud-Detection Architecture That Works Like Your Brain Does

Categories: Flume HBase Kafka Spark Use Case

To design effective fraud-detection architecture, look no further than the human brain (with some help from Spark Streaming and Apache Kafka).

At its core, fraud detection is about detection whether people are behaving “as they should,” otherwise known as catching anomalies in a stream of events. This goal is reflected in diverse applications such as detecting credit-card fraud, flagging patients who are doctor shopping to obtain a supply of prescription drugs,

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