Category Archives: Hadoop

Progress Report: Bringing Erasure Coding to Apache Hadoop

Categories: Hadoop HDFS Performance

Get an update on the progress of the effort to bring erasure coding to HDFS, including a report about fresh performance benchmark testing results.

About a year ago, the Apache Hadoop community began the HDFS-EC project to build native erasure coding support inside HDFS (currently targeted for the 2.9/3.0 release). Since then, we have designed and implemented basic functionalities in the first phase of the project under HDFS-7285,

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The Top 10 Cloudera Engineering Blog Posts of 2015

Categories: Community General Hadoop

Which topics interested attracted the most community interest in 2015? Find out below.

It’s our annual custom to bring you a list of this blog’s most popular posts of the year. (See the 2013 and 2014 versions.) Why? Because this list reflects interests across the ecosystem; it’s one of the best passive surveys we have, actually.

As usual, when drawing conclusions, be sure to account for data skew.

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DistCp Performance Improvements in Apache Hadoop

Categories: CDH Hadoop HDFS Performance Tools

Recent improvements to Apache Hadoop’s native backup utility, which are now shipping in CDH, make that process much faster.

DistCp is a popular tool in Apache Hadoop for periodically backing up data across and within clusters. (Each run of DistCp in the backup process is referred to as a backup cycle.) Its popularity has grown in popularity despite relatively slow performance.

In this post, we’ll provide a quick introduction to DistCp.

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How-to: Ingest and Query “Fast Data” with Impala (Without Kudu)

Categories: Hadoop How-to Impala Kudu

Impala is designed to deliver insight on data in Apache Hadoop in real time. As data often lands in Hadoop continuously in certain use cases (such as time-series analysis, real-time fraud detection, real-time risk detection, and so on), it’s desirable for Impala to query this new “fast” data with minimal delay and without interrupting running queries.

In this blog post, you will learn an approach for continuous loading of data into Impala via HDFS,

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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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