l'Ishwar incroyable Nataraj

I was fiddling around with Power Map and wanted to see how far I could go ahead with the mapping visualization....... I basically leveraged the Power Station data file available at: http://office.microsoft.com/en-us/excel-help/redir/XT104048055.aspx?CTT=5&origin=HA104091224 After downloading this excel file, I started to create Power Map with the basic idea of having 3 layers. 1. The first layer would take into account all the counties based on the Power Transmitted 2. The second layer would consist of all the companies based the Power distribution 3. The third layer considers the power distribution based on Plant Name.... Now this excel file contains roughly about 20,000 rows...... My machine configuration is basically a Windows 8 with an Intel i7 core and 16 GB of RAM. I was good up to step 1 in the steps described above but when I hit step 2 that's when I noticed that my memory just bloated like crazy...... I decided like a crazy person to try step 3 and that's

Big Data - the keyword given to solutions that can handle massive amount of data usually in the petabyte or greater amount. There are several big data solutions out there and all of them have their unique characteristics which can be useful in different scenarios. I was looking into Cloudera's versions of Hadoop like Impala, Sentry and HBase. All these vary based on the use case. For some of my clients I have leveraged Amazon Redshift, Cassandra (and hopefully soon Apache Hadoop). The architecture of these systems differ but the end goal is the storage and processing of vast amounts of data down to second or milli second based result generation. Focusing on this aspect I am going to give a more detailed insight on Redshift which is a node based peta byte scaled database as well as a high level overview of what I recently implemented. Note: The above diagram is from the Redshift Warehousing article ( http://docs.aws.amazon.com/redshift/latest/dg/c_high_level_system_architecture.