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RapidMiner is a leading data science platform that unites data prep, machine learning & predictive model deployment. RapidMiner provides data mining and machine learning procedures including data loading and transformation (ETL), data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment. RapidMiner is written in the Java programming language.
Rapid Miner assignment is designed to help you to learn and use a data mining tool. This simple drag-and- drop interface makes it easy to build robust data mining models. Rapid Miner is at the particular forefront of Hadoop. It also triggers predictive analytics and visual analytics to data researchers and analysts.
To complete the assignments consisting of use of RapidMiner is a very challenging task for students. The rapid miner software itself is very complex, it’s not easy for the beginners to learn the rapidminer.The free edition of the RapidMiner has limited resources which are not sufficient to perform reliable analytics.
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RapidMiner is a software package that allows data mining, text mining and predictive analytics. The program allows the user to enter raw data, including databases and text, which is then automatically and intelligently analysed on a large scale. RapidMiner includes a free trial to assess its capabilities.
The people who are looking to extract value from massive data, they use Rapidminer Hadoop. The primary element of the actual Rapid Miner is the Predictive Analytics System and extends predictive analytics. The Rapid Miner system deals with unstructured data such as text files, web traffic logs, and pictures, etc.
The rapid Minor system could very easily control the volume related to large data without creating a single line of code. We provide the students with their Hadoop Rapid Miner assignments. Rapid Miner is a software application which interacts with other tasks to store and retrieve information.
RapidMiner offers flexible approaches to remove any limitations in data set size. The most often used is the in-memory engine, where data is loaded completely into memory and is analyzed there. This and other engines are outlined below.
The natural storage mechanism of RapidMiner is in-memory data storage, highly optimized for data access usually performed for analytical tasks.
The advantage of Hadoop is that it offers both a distributed storage engine as well as a possibility to use a Hadoop cluster for a distributed analytical engine for big data analytics.
All the storage types above can be combined with loop-based workflows where data processing / modeling is performed on partitions of the data and the results are combined afterwards. It depends on the data set as well as the analysis if, for example, a loop-based approach using the in-memory approach is faster.
Hadoop is open-source, which means it is free to use. Since it is an open-source project the source-code is available online for anyone to understand it or make some modifications as per their industry requirement.
Hadoop is a highly scalable model. A large amount of data is divided into multiple inexpensive machines in a cluster which is processed parallely. The number of these machines or nodes can be increased or decreased as per the enterprise’s requirements.
Hadoop uses commodity hardware(inexpensive systems) which can be crashed at any moment. In Hadoop data is replicated on various DataNodes in a Hadoop cluster which ensures the availability of data if somehow any of your systems got crashed. You can read all of the data from a single machine if this machine faces a technical issue. Data can also be read from other nodes in a Hadoop cluster because the data is copied or replicated by default. By default, Hadoop makes 3 copies of each file block and stores it into different nodes.
Fault tolerance provides High Availability in the Hadoop cluster. High Availability means the availability of data on the Hadoop cluster. Due to fault tolerance in case if any of the DataNode goes down the same data can be retrieved from any other node where the data is replicated.
Hadoop is open-source and uses cost-effective commodity hardware which provides a cost-efficient model, unlike traditional Relational databases that require expensive hardware and high-end processors to deal with Big Data.
Hadoop is designed in such a way that it can deal with any kind of dataset like structured(MySql Data), Semi-Structured(XML, JSON), Un-structured (Images and Videos) very efficiently. This means it can easily process any kind of data independent of its structure which makes it highly flexible. It is very much useful for enterprises as they can process large datasets easily, so the businesses can use Hadoop to analyze valuable insights of data from sources like social media, email, etc. With this flexibility, Hadoop can be used with log processing, Data Warehousing, Fraud detection, etc.
Hadoop is easy to use since the developers need not worry about any of the processing work since it is managed by the Hadoop itself. The Hadoop ecosystem is also very large and comes up with lots of tools like Hive, Pig, Spark, HBase, Mahout, etc.
The concept of Data Locality is used to make Hadoop processing fast. In the data locality concept, the computation logic is moved near data rather than moving the data to the computation logic.
Hadoop uses a distributed file system to manage its storage i.e. HDFS(Hadoop Distributed File System). In DFS(Distributed File System) a large size file is broken into small size file blocks then distributed among the Nodes available in a Hadoop cluster, as this massive number of file blocks are processed parallely which makes Hadoop faster, because of which it provides a High-level performance as compared to the traditional DataBase Management Systems.
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