Cloudera Engineering Blog · Flume Posts
This is the second article in a series about analyzing Twitter data using some of the components of the Hadoop ecosystem available in CDH, Cloudera’s open-source distribution of Apache Hadoop and related projects. In the first article, you learned how to pull CDH components together into a single cohesive application, but to really appreciate the flexibility of each of these components, we need to dive deeper.
Every story has a beginning, and every data pipeline has a source. So, to build Hadoop applications, we need to get data from a source into HDFS.
The post below was originally published via blogs.apache.org and is republished below for your reading pleasure.
This blog post is about Apache Flume’s File Channel. Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
We’re getting really close to Strata Conference + Hadoop World 2012 (just over a month away), schedule planning-wise. So you may want to consider adding the tutorials, sessions, and keynotes below to your calendar! (Start times are always subject to change of course.)
The ones listed below are led or co-led by Clouderans, but there is certainly a wide range of attractive choices beyond what you see here. We just want to ensure that you put these particular ones high on your consideration list.
Social media has gained immense popularity with marketing teams, and Twitter is an effective tool for a company to get people excited about its products. Twitter makes it easy to engage users and communicate directly with them, and in turn, users can provide word-of-mouth marketing for companies by discussing the products. Given limited resources, and knowing we may not be able to talk to everyone we want to target directly, marketing departments can be more efficient by being selective about whom we reach out to.
In this post, we’ll learn how we can use Apache Flume, Apache HDFS, Apache Oozie, and Apache Hive to design an end-to-end data pipeline that will enable us to analyze Twitter data. This will be the first post in a series. The posts to follow to will describe, in more depth, how each component is involved and how the custom code operates. All the code and instructions necessary to reproduce this pipeline is available on the Cloudera Github.
Who is Influential?
We are happy to announce the general availability of CDH3 update 5. This update is a maintenance release of CDH3 platform and provides a considerable amount of bug-fixes and stability enhancements. Alongside these fixes, we have also included a few new features, most notable of which are the following:
Apache Flume is a scalable, reliable, fault-tolerant, distributed system designed to collect, transfer, and store massive amounts of event data into HDFS. Apache Flume recently graduated from the Apache Incubator as a Top Level Project at Apache. Flume is designed to send data over multiple hops from the initial source(s) to the final destination(s). Click here for details of the basic architecture of Flume. In this article, we will discuss in detail some new components in Flume 1.x (also known as Flume NG), which is currently on the trunk branch, techniques and components that can be be used to route the data, configuration validation, and finally support for serializing events.
In the past several months, contributors have been busy adding several new sources, sinks and channels to Flume. Flume now supports Syslog as a source, where sources have been added to support Syslog over TCP and UDP.
This blog was originally posted on the Apache Blog: https://blogs.apache.org/flume/entry/apache_flume_hackathon. Apache Flume is currently undergoing incubation at The Apache Software Foundation. More information on this project can be found at http://incubator.apache.org/flume.
This blog was originally posted on the Apache Blog: https://blogs.apache.org/flume/entry/flume_ng_architecture
Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. Flume is currently undergoing incubation at The Apache Software Foundation. More information on this project can be found at http://incubator.apache.org/flume. Flume NG is work related to new major revision of Flume and is the subject of this post.
The Development track at Hadoop World is a technical deep dive dedicated to discussion about Apache Hadoop and application development for Apache Hadoop. You will hear committers, contributors and expert users from various Hadoop projects discuss the finer points of building applications with Hadoop and the related ecosystem. The sessions will touch on foundational topics such as HDFS, HBase, Pig, Hive, Flume and other related technologies. In addition, speakers will address key development areas including tools, performance, bringing the stack together and testing the stack. Sessions in this track are for developers of all levels who want to learn more about upcoming features and enhancements, new tools, advanced techniques and best practices.
On Monday, we held our second Flume Office Hours at Cloudera HQ in Palo Alto. The intent was to meet informally, to talk about what’s new, to answer questions, and to get feedback from the community to help prioritize features for future releases.
Below is the slide deck from Flume Office Hours: