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Data Mining with Weka -Installation

Weka - Data mining Tool W eka is a tool for big data and data mining. It is used to various classification, experiments, and analysis over large data sets. Installation Guide -weka  You can download Weka from   here   and follow the normal installation procedure. After completion you will get following window, here you can begin your classification or experiment on different data sets with Weka.
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Twitter Strom - Analysis of Distributed System

Analysis of Distributed System -Twitter Strom


Storm is a real time distributed stream data processing system with fault-tolerant capabilities. Storm is used on today’s one of the most popular social networking site Twitter to run various critical computations at large scale, and in real-time.

Storm has simple distribute system architecture and built with various features like fault tolerance, latency, efficient and easy human intervention etc. and can be used on very complex distribute stream data processing. It uses very simple topology for various functions like (tweet) word count  in one of the most popular social networking site Twitter.

Strom uses Nimbus and Zookeeper for internal architecture which handles supervision and worker message flow architecture activities.

Furthermore, a popular method for generating Storm topologies at Twitter is by using Summingbird. Summingbird is a general stream processing abstraction, which provides a separate logical planner that can map to a variety of stream processing and batch processing systems.

Storm is a critical infrastructure at Twitter that powers many of the real-time data-driven decisions that are made at Twitter. The use of Storm at social networking site Twitter is expanding rapidly, and raises a number of potentially interesting directions for future work. 

These include automatically optimizing the topology (intra-bolt parallelism and the packaging of tasks in executors) statically, and re-optimizing dynamically at runtime, without incurring a big performance impact. In addition, It wants to improve the visualization tools, improve the reliability of certain parts (e.g. move the state stored in local disk on Nimbus to a more fault tolerant system like HDFS), provide a better integration of Storm with Hadoop, and potentially use Storm to monitor, react, and adapt itself to improve the configuration of running topologies.

Another interesting direction of upcoming work of Strom might be support a declarative query paradigm for Storm that still allows easy extensiblility.
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