Using Apache Impala (incubating) on top of Apache Kudu (incubating) has significant performance benefits
Apache Kudu (incubating) is the newest addition to the set of storage engines that integrate with the Apache Hadoop ecosystem. The promise of Kudu is to deliver high-scan performance, targeting analytical workloads, while allowing users to concurrently insert, update, and delete records. With these properties, Kudu becomes a viable alternative to existing combinations of HDFS and/or Apache HBase to achieve similar results with less complicated ETL pipelines,
Vodafone UK’s new SIEM system relies on Apache Flume and Apache Kafka to ingest nearly 1 million events per second. In this post, learn about the architecture and performance-tuning techniques and that got it there.
SIEM platforms provide a useful tool for identifying indicators of compromise across disparate infrastructure. The catch is, they’re only as accurate as the fidelity of the data involved, which is why Apache Hadoop is becoming such a valuable platform for that use case.
I recently had a chat with Benjamin Bengfort, a data scientist finishing his PhD at the University of Maryland, and Jenny Kim, a software engineer at Cloudera, about their forthcoming O’Reilly Media book (now in Early Access), Data Analytics with Hadoop: An Introduction for Data Scientists.
Why did you decide to write this book?
Ben: The content was originally part of a class that Jenny and I were teaching together.
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,
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.