Cloudera Developer Blog · Data Collection Posts
Note: This post was originally published at blogs.apache.org in a slightly different form.
Apache Sqoop is a tool for doing highly efficient data transfers between relational databases and the Apache Hadoop ecosystem. One significant benefit of Sqoop is that it’s easy to use and can work with a variety of systems inside as well as outside of that ecosystem. Thus, with one tool, you can import or export data from all databases supporting the JDBC interface with the same command-line arguments exposed by Sqoop. Furthermore, Sqoop was designed to be modular, allowing you to plug in specialized additions to optimize transfers for particular database systems.
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.
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?
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/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.
This blog was originally posted on the Apache Blog: https://blogs.apache.org/sqoop/entry/apache_sqoop_overview
Using Hadoop for analytics and data processing requires loading data into clusters and processing it in conjunction with other data that often resides in production databases across the enterprise. Loading bulk data into Hadoop from production systems or accessing it from map reduce applications running on large clusters can be a challenging task. Users must consider details like ensuring consistency of data, the consumption of production system resources, data preparation for provisioning downstream pipeline. Transferring data using scripts is inefficient and time consuming. Directly accessing data residing on external systems from within the map reduce applications complicates applications and exposes the production system to the risk of excessive load originating from cluster nodes.
Pero works on research and development in new technologies for online advertising at Aol Advertising R&D in Palo Alto. Over the past 4 years he has been the Chief Architect of R&D distributed ecosystem comprising more than thousand nodes in multiple data centers. He also led large-scale contextual analysis, segmentation and machine learning efforts at AOL, Yahoo and Cadence Design Systems and published patents and research papers in these areas.
A critical premise for success of online advertising networks is to successfully collect, organize, analyze and use large volumes of data for decision making. Given the nature of their online orientation and dynamics, it is critical that these processes be automated to the largest extent possible.
The user-data connection is driving NoSQL database-Hadoop pairing
Flume is a flexible, scalable, and reliable system for collecting streaming data. The Flume User Guide describes how to configure Flume, and the new Flume Cookbook contains instructions (called recipes) for common Flume use cases. In this post, we present a recipe that describes the common use case of using a Flume node collect Apache 2 web servers logs in order to deliver them to HDFS.
Using Flume Agents for Apache 2.x Web Server Logging
The past month has been exciting and productive for the community using and developing Cloudera’s Flume! This young system is a core part of Cloudera’s Distribution for Hadoop (CDH) that is responsible for streaming data ingest. There has been a great influx of interest and many contributions, and in this post we will provide a quick summary of this month’s new developments. First, we’re happy to announce the availability of Flume v0.9.1 and we will describe some of its updates. Second, we’ll talk about some of the exciting new integration features coming down the pipeline. Finally we will briefly mention some community growth statistics, as well as some recent and upcoming talks about Flume.