Skip to main content

Hadoop Mashups - Serving HDFS data in Yahoo Pipes, IFTTT recipe, Zapier zap

By Sachin Ghai


“Hadoop  is backend…it is for batch…it is for Google, Facebook … how does a web developer come in to picture…”
Lets dispel some perception… and demonstrate an architectural flow for pulling data from HDFS right into a Yahoo Pipe, IFTTT recipe or Zapier zap.

1- Setup Hadoop, HDFS with data coming in the nodes

2- Write the MapReduce algorithms to process the data you want

3- Setup Hive or HBase or Voldemort or your preferred DB/warehouse option depending on your interest.

4- Setup Struts/Spring based application in Eclipse/RAD/your preferred IDE with supported web server like WAS 7.0/JBoss

5- Write a program to fetch the data from Hive (or other variants mentioned in step 3 above), format it, render as RSS response and provide servlet to serve it back as response

.
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
       xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
       xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd">
    <bean id="hadoopFeedGenerationAction" class="com.hadoop.rss.action.hadoopFeedGenerationAction"
          init-method="checkInitialization" scope="prototype">
        <property name="generator" ref="hadoopFeedGenerator"/>      
    </bean>
    <bean id="hadoopItemDAO" class="com.hadoop.rss.dao.hadoopItemDAOImpl" init-method="checkInitialization">
        <property name="jdbcTemplate" ref="jdbcTemplate"/>
    </bean>
    <bean id="hadoopFeedFormatter" class="com.hadoop.rss.formatter.hadoopFeedFormatterImpl"
          init-method="checkInitialization">
        <property name="description" value="channel desc"/>
        <property name="link" value="www.test.com"/>
        <property name="title" value="channel title"/>
        <property name="contentTemplate">
            <value><![CDATA[<table>
<tr><td>Request Id</td><td>%requestId%</td></tr>
<tr><td>Title</td><td>%requestTitle%</td></tr>
<tr><td>Type Code</td><td>%requestTypeCode%</td></tr>
<tr><td>Requester Web Id</td><td>%requesterWebId%</td></tr>
<tr><td>Step start time</td><td>%stepStartTime%</td></tr>
<tr><td>Status</td><td>%status%</td></tr>
<tr><td>Link to item</td><td>%requestLink%</td></tr>
</table>]]></value>
        </property>       
        <property name="rssFeedGenerator" ref="rssFeedGenerator"/>
        <property name="itemTitle" value="item title"/>
    </bean>


Sample xml file excerpt for RSS generation – use case: transaction data fetch

6- Deploy and host the .war on a public server

7- Mash up RSS feed in the pipe or recipe or zap.

 







Use case examples:
1-      Need a live index of words in documents uploaded by users
2-      Weather data served right from the Hadoop crunchers to AJAX renderers

Contact Sachin Ghai for further details on this Hadoop hack.

Labels:

Comments

Post a Comment

Popular articles

5 online tools in data visualization playground

By Sachin Ghai
While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs.
To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground.
1)      Many Eyes: Many Eyes is a data visualization experiment by IBM Researchandthe IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Map, Tag/Word cloud and ge…
6 comments
Read more

Data deduplication tactics with HDFS and MapReduce

By Sachin Ghai
As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…
2 comments
Read more

In-memory data model with Apache Gora

By Sachin Ghai
Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…
4 comments
Read more