WELCOME TO BIGDATATRENDZ      WELCOME TO CAMO      Architectural Patterns for Near Real-Time Data Processing with Apache Hadoop      Working with Apache Spark: Or, How I Learned to Stop Worrying and Love the Shuffle     

Video Bar


Sunday, 28 September 2014

The Early Release Books Keep Coming: This Time, Hadoop Security

source: Thanks to CLOUDERA for technical updates

Hadoop Security is the latest book from Cloudera engineers in the Hadoop ecosystem books canon.
We are thrilled to announce the availability of the early release of Hadoop Security, a new book about security in the Apache Hadoop ecosystem published by O’Reilly Media. The early release contains two chapters on System Architecture and Securing Data Ingest and is available in O’Reilly’s catalog and in Safari Books.
Hadoop security
The goal of the book is to serve the experienced security architect that has been tasked with integrating Hadoop into a larger enterprise security context. System and application administrators also benefit from a thorough treatment of the risks inherent in deploying Hadoop in production and the associated how and why of Hadoop security.
As Hadoop continues to mature and become ever more widely adopted, material must become specialized for the security architects tasked with ensuring new applications meet corporate and regulatory policies. While it is up to operations staff to deploy and maintain the system, they won’t be responsible for determining what policies their systems must adhere to. Hadoop is mature enough that dedicated security professionals need a reference to navigate the complexities of security on such a massive scale. Additionally, security professionals must be able to keep up with the array of activity in the Hadoop security landscape as exemplified by new projects like Apache Sentry (incubating) and cross-project initiatives such asProject Rhino.

Getting Started with Big Data Architecture

What does a “Big Data engineer” do, and what does “Big Data architecture” look like? In this post, you’ll get answers to both questions.
Apache Hadoop has come a long way in its relatively short lifespan. From its beginnings as a reliable storage pool with integrated batch processing using the scalable, parallelizable (though inherently sequential) MapReduce framework, we have witnessed the recent additions of real-time (interactive) components like Impala for interactive SQL queries and integration with Apache Solr as a search engine for free-form text exploration.
Getting started is now also a lot easier: Just install CDH, and all the Hadoop ecosystem components are at your disposal. But after installation, where do you go from there? What is a good first use case? How do you ask those “bigger questions”?
Having worked with more customers running Hadoop in production than any other vendor, Cloudera’s field technical services team has seen more than its fair share of these use cases. Although they obviously vary by industry and application, there is a common theme: the presence of Big Data architecture.
In this post, you’ll get a whirlwind tour of that architecture based on what we’ve seen at customer sites over the past couple of years, and get some tips/initial advice about building your own as the foundation for an enterprise data hub.

Big Data Architecture

Introduction to HDFS Erasure Coding in Apache Hadoop

Thanks to blog contributors from Cloudera Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50% compar...