Tag Archives: Flume

How-to: Do Data Quality Checks using Apache Spark DataFrames

Categories: How-to Spark

Apache Spark’s ability to support data quality checks via DataFrames is progressing rapidly. This post explains the state of the art and future possibilities.

Apache Hadoop and Apache Spark make Big Data accessible and usable so we can easily find value, but that data has to be correct, first. This post will focus on this problem and how to solve it with Apache Spark 1.3 and Apache Spark 1.4 using DataFrames.

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Strata + Hadoop World NYC 2015 Content Preview

Categories: Community Events Hadoop

The Strata + Hadoop World NYC 2015 (Sept. 29-Oct. 3) agenda was published in the last few days. Congratulations to all accepted presenters!

In this post, I just want to provide a concise digest of the tutorials and sessions that will involve Cloudera or Intel engineers and/or interesting use cases. There are many worthy sessions from which to choose, so we hope this list will influence your decisions about where to spend your time during the week!

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Deploying Apache Kafka: A Practical FAQ

Categories: CDH Kafka

This post contains answers to common questions about deploying and configuring Apache Kafka as part of a Cloudera-powered enterprise data hub.

Cloudera added support for Apache Kafka, the open standard for streaming data, in February 2015 after its brief incubation period in Cloudera Labs. Apache Kafka now is an integrated part of CDH, manageable via Cloudera Manager, and we are witnessing rapid adoption of Kafka across our customer base.

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Inside Apache HBase’s New Support for MOBs

Categories: HBase

Learn about the design decisions behind HBase’s new support for MOBs.

Apache HBase is a distributed, scalable, performant, consistent key value database that can store a variety of binary data types. It excels at storing many relatively small values (<10K), and providing low-latency reads and writes.

However, there is a growing demand for storing documents, images, and other moderate objects (MOBs)  in HBase while maintaining low latency for reads and writes.

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Architectural Patterns for Near Real-Time Data Processing with Apache Hadoop

Categories: Data Ingestion Flume Hadoop HBase Kafka Spark

Evaluating which streaming architectural pattern is the best match to your use case is a precondition for a successful production deployment.

The Apache Hadoop ecosystem has become a preferred platform for enterprises seeking to process and understand large-scale data in real time. Technologies like Apache Kafka, Apache Flume, Apache Spark, Apache Storm, and Apache Samza are increasingly pushing the envelope on what is possible. It is often tempting to bucket large-scale streaming use cases together but in reality they tend to break down into a few different architectural patterns,

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