Congratulations to Hari Shreedharan, Cloudera software engineer and Apache Flume committer/PMC member, for the early release of his new O’Reilly Media book, Using Flume: Stream Data into HDFS and HBase. It’s the seventh Hadoop ecosystem book so far that was authored by a current or former Cloudera employee (but who’s counting?).
Why did you decide to write this book?
I have been working on Apache Flume for the past two years,
Why would any company be interested in searching through its vast trove of email? A better question is: Why wouldn’t everybody be interested?
Email has become the most widespread method of communication we have, so there is much value to be extracted by making all emails searchable and readily available for further analysis. Some common use cases that involve email analysis are fraud detection, customer sentiment and churn, lawsuit prevention, and that’s just the tip of the iceberg.
Hue 2.2 , the open source web-based interface that makes Apache Hadoop easier to use, lets you interact with Hadoop services from within your browser without having to go to a command-line interface. It features different applications like an Apache Hive editor and Apache Oozie dashboard and workflow builder.
This post is based on our “Analyzing Twitter Data with Hadoop” sample app and details how the same results can be achieved through Hue in a simpler way.
In this installment of “Meet the Engineer”, get to know Customer Operations Engineering Manager/Apache Sqoop committer Kathleen Ting (@kate_ting).
What do you do at Cloudera, and in what open-source projects are you involved?
I’m a support manager at Cloudera, and an Apache Sqoop committer and PMC member. I also contribute to the Apache Flume and Apache ZooKeeper mailing lists and organize and present at meetups, as well as speak at conferences,
The post below was originally published via blogs.apache.org and is republished below for your reading pleasure.
This is Part 1 in a series of articles about tuning the performance of Apache Flume, a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of event data.
To kick off this series, I’d like to start off discussing some important Flume concepts that come into play when tuning your Flume flows for maximum performance: the channel and the transaction batch size.