Author Archives: Amr Awadallah

Big Data’s New Use Cases: Transformation, Active Archive, and Exploration

Categories: Hadoop Impala Use Case

Now that Apache Hadoop is seven years old, use-case patterns for Big Data have emerged. In this post, I’m going to describe the three main ones (reflected in the post’s title) that we see across Cloudera’s growing customer base.

Transformation

Transformations (T, for short) are a fundamental part of BI systems: They are the process through which data is converted from a source format (which can be relational or otherwise) into a relational data model that can be queried via BI tools.

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Grouping Related Trends with Hadoop and Hive

Categories: Community General Hadoop Hive

(guest blog post by Pete Skomoroch)

In a previous post, I outlined how to build a basic trend tracking site called trendingtopics.org with Cloudera’s Distribution for Hadoop and Hive.  TrendingTopics uses Hadoop to identify the top articles trending on Wikipedia and displays related news stories and charts.  The data powering the site was pulled from an Amazon EBS Wikipedia Public Dataset containing 8 months of hourly pageview logfiles. 

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Tracking Trends with Hadoop and Hive on EC2

Categories: Community General Guest Hadoop


At Cloudera, we frequently work with leading Hadoop developers to produce guest blog posts of general interest to the community. We started a project with Pete Skomoroch a while back, and we were so impressed with his work, we’ve decided to bring Pete on as a regular guest blogger. Pete can show you how to do some pretty amazing things with Hadoop, Pig and Hive and has a particular bias towards Amazon EC2.

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Job Scheduling in Apache Hadoop

Categories: Hadoop MapReduce

(guest blog post by Matei Zaharia)

When Apache Hadoop started out, it was designed mainly for running large batch jobs such as web indexing and log mining. Users submitted jobs to a queue, and the cluster ran them in order. However, as organizations placed more data in their Hadoop clusters and developed more computations they wanted to run, another use case became attractive: sharing a MapReduce cluster between multiple users.

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