Analyzing Twitter Data with Apache Hadoop, Part 3: Querying Semi-structured Data with Apache Hive by Jon Natkins.
From the post:
This is the third article in a series about analyzing Twitter data using some of the components of the Apache Hadoop ecosystem that are available in CDH (Cloudera’s open-source distribution of Apache Hadoop and related projects). If you’re looking for an introduction to the application and a high-level view, check out the first article in the series.
In the previous article in this series, we saw how Flume can be utilized to ingest data into Hadoop. However, that data is useless without some way to analyze the data. Personally, I come from the relational world, and SQL is a language that I speak fluently. Apache Hive provides an interface that allows users to easily access data in Hadoop via SQL. Hive compiles SQL statements into MapReduce jobs, and then executes them across a Hadoop cluster.
In this article, we’ll learn more about Hive, its strengths and weaknesses, and why Hive is the right choice for analyzing tweets in this application.
I didn’t realize I had missed this part of the Hive series until I saw it mentioned in the Hue post.
Good introduction to Hive.
BTW, is Twitter data becoming the “hello world” of data mining?