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The current Analytics tools and models that are available in the market are very costly, unable to handle Big Data and less secure. The traditional Analytics systems takes a long time to come up with results, so it is not beneficial to use for Real Time Analytics. So, the proposed work resolves all these problems by combining the Apache Open Source platform which solves the issues of Real Time Analytics using HADOOP. It also provides scalability and reduced cost over analytics by using open Source Software. The work proposes to combine the Apache Open Source Modules and configure them to get the required result. This system also provide solution for speedy data downloading on HDFS by using source and sink (data ingestion) mechanism. The Hadoop is flexible and scalable architecture. The proposed work is based upon the phenomenon of combination of open source software along with commodity hardware that will increase the profit of IT Industry.

Firewalls have become the most essential part that is deployed on the Internet for protecting the private networks. Optimizing firewall rules is very critical for enhancing the performance of a network. The early work on firewall optimization focuses on either intra-firewall or inter-firewall optimization within one administrative domain where the privacy of firewall policies is not a concern. The main technical challenge to focused on is that firewall policies cannot be disclosed across domains because a firewall policy contains secret information and even potential security holes, which can be easily utilized by attackers. Proposed work involves first the protocol for preserving firewall policies and optimizing them. Particularly, for any two adjacent firewalls that belongs to two different administrative domains, this protocol can identify the rules in each firewall that can be removed because of the other firewall. This process of optimization involves computation between the two firewalls without any party disclosing their policies to the other. The communication cost is reduced by the optimization process. This protocol sustains no extra online packet processing overhead, and also the offline processing time is less.

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