Playing with Apache Hive and SOLR

As described in a previous post, Apache SOLR can perform very well to provide low latency analytics. Data logs can be pre-aggregated using Hive and then synced to SOLR. To this end, we developed a simple Storage Handler for SOLR so that data can be read and written to SOLR transparently using an external table.

We will show in this post how to install our SOLR storage handler and then run a simple example where we sync some data from Hive to SOLR.
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Using the Mahout Naive Bayes Classifier to automatically classify Twitter messages

mahout2Classification algorithms can be used to automatically classify documents, images, implement spam filters and in many other domains. In this tutorial we are going to use Mahout to classify tweets using the Naive Bayes Classifier. The algorithm works by using a training set which is a set of documents already associated to a category. Using this set, the classifier determines for each word, the probability that it makes a document belong to each of the considered categories. To compute the probability that a document belongs to a category, it multiplies together the individual probability of each of its word in this category.  The category with the highest probability is the one the document is most likely to belong to.

To get more details on how the Naive Bayes Classifier is implemented, you can look at the mahout wiki page.

This tutorial will give you a step-by-step description on how to create a training set, train the Naive Bayes classifier and then use it to classify new tweets.

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Playing with Apache Hive, MongoDB and the MTA

Apache Hive is a popular datawarehouse system for Hadoop that allows to run SQL queries on top of Hadoop by translating queries into Map/Reduce jobs. Due to the high latency incurred by Hadoop to execute Map/Reduce jobs, Hive cannot be used in applications that require fast access to data. One common technique is to use Hive to pre-aggregate data logs stored in HDFS and then sync the data to a Datawarehouse.

In this post we’re going to describe how to install Hive and then, as New York City straphangers, we’re going to load subway train movement data from the MTA in HDFS, execute Hive queries to aggregate the number of daily average train movements per line and store the result in MongoDB.
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