blog@Yantrajaal

As a former DBA I love the fact that SQL has a life of its own and the way it is still used after it was first conceived of in the 1980s as the natural way to query Relational Database Management Systems. A vast amount of data in the world is still available in SQL compliant RDBMS tables and today when business analytics and data science seems to be overshadowing SQL, I was delighted to find that SQL can still play a very important role in the implementation of complex data mining applications. This post explains how this evergreen tool is still very, very relevant in data mining. Many data mining tools like Rattle , RapidMiner are used to create "models" for Classification / Decision Trees and Heirarchical Clustering  but then the models have to put into production by using them to score large datasets. This is where SQL can play a very powerful role. The models created in the data mining tools need to be exported as PMML documents and then converted to SQL using any of...

The challenge of combining the statistical power of R and the "Big Data" capabilities of Hadoop is something that has always fascinated me. Over a year ago, I had finally broken free from from the stupidity of the WordCount ( and various other counting ) programs and tried to solve a real like retail problem with linear regression using R and Hadoop. This is documented in my blog post Forecasting Retail Sales -- Linear Regression with R and Hadoop . In this case however I had used the Hadoop streaming API to call to separate R programs. Subsequently I had come across the Hortonworks HDP platform that dramatically simplified the process of installing and running Hadoop. This is explained in my blog post Big Data for the Non Geek, where in addition to installing Hadoop, I have also explained how to overcome the challenges of installing the RHadoop packages on top Hadoop on the Hortonworks platform. Hortonworks has a nice example of how to run an rHadoop program on the HDP...

[Note]   -- Hadoop, IMHO, is history. Rather than waste time with all this, suggest you check up my blog post on  Spark with Python . Hadoop is a conceptual delight and an architectural marvel. Anybody who understands the immense challenge of crunching through a humungous amount of data will appreciate the way it transparently distributes the workload across multiple computers and marvel at the elegance with which it does so. image from nextgendistribution.com Thirty years after my first tryst with data -- as relational database management systems that I had come across at the University of Texas at Dallas -- my introduction to Hadoop was an eye opener into a whole new world of data processing. Last summer, I managed to Demystify Map Reduce and Hadoop by installing it on Ubuntu and running a few Java programs but frankly I was more comfortable with Pig and Hive that allowed a non-Java person -- or pre-Java dinosaur -- like me to perform meaningful tasks with Map-R...

My effort to learn Hadoop and Map Reduce resulted in the presentation. If you like it and find it useful do leave a comment.