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Blogs | Virtual Ameerpet

Big Data Analytics – Hadoop


Development of a framework or exploring the deep roots of a technology has always been a good practice in IT industry to meet various the needs of the industry experts. With the traditional knowledge of programming and databases, all the way I have been involved in the management of structural data.
‘Something new’ was required when data changed its forms and moved from its traditional –structured format to unstructured that made the existing programing languages or databases to be re -organized and evolve with a wide larger forms to handle unstructured data too.

It is a part of big data. As we all are aware of the fact that Google, Facebook, twitter and many other social networking sites have been the main contributors from which big data has evolved. Hadoop has been the buzz word that speaks all about how to handle unstructured data and keep it safe in a clustered environment.

Ability to sustain failures at storage level, if any and have a backup ready is its major pro. Large Rackspace was always a point of option to keep my data backup. But all it comes to the latest approach is –everything is managed in cloud, in a virtual world.

In a Hadoop architecture, data is organized in a Cluster and is designed so as to handle distributed processing of huge data sets. It has the ability to scale up to thousands of machines as per requirement. Instead of relying on the high end hardware systems, these clusters have the ability to perceive and handle failures at application layer itself.

Hadoop suits various market requirements, say for example, to perform an accurate portfolio evaluation or risk analysisthat could not give accurate results with the database engine, whereas hadoop does. Consider an Online retail that needs to deliver better search results to its customers, wherein its search results can be shown with the things we want the customers to buy. Hadoop comes in place again. Get in touch with our experts at ( to have more insight into its architecture and concepts.

Blogs | Virtual Ameerpet

Hadoop for distributed data-processing applications


As companies have more data collected from the consumer’s interaction with their businesses, Hadoop open source storage system is emerging as a key technology for breaking up large chunks of data into smaller pieces. Hadoop is used to manage data that the businesses generate through many sources like–social media websites and other digital technologies in a large scale distributed batch processing infrastructure.

Its design helps in efficiently handling these large amounts of data either in a single machine or that scaled up to a set of machines. The underlying facts that Hadoop changes the economics and the dynamics of large scale computing lies in its main characteristics like scalability, Cost Effectiveness, Flexibility and Fault Tolerant.

Its effective fault tolerant mechanism redirects the work to another location of data in case of any loss in a data node and enables continuous processing. Being schema less and ability to imbibe any type of data either structured or unstructured shows that Hadoop is flexible enough to handle data from multiple sources. This data can amount in arbitrary ways that helps in deeper analysis.

As discussed earlier, a Hadoop system can scale up from a single machine to multiple machines, its distributed system has the ability to add new nodes as needed without any impact on the data formats and it’s loading. This would also enable parallel computing to commodity servers that leads to a decreased cost and making it more affordable to model all the data.

Having the entire organizations data in Hadoop makes it manage the data by breaking it up into pieces and spread it across various servers. It is now the known fact that there would not be one particular place where we say that all the data resides in, but Hadoop has a track of it in data nodes and clusters. One need to run an indexing job and process the code to each of the servers in a cluster such that each server operates on its own bits of data.

Now we have the results in a whole combined format where MapReduce come in place – that we MAP all the operations to the servers and then REDUCE the results back to a one whole result set. Get in touch with our experts at ( to learn more about Hadoop job processing and its architecture.

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