Big Data Analytics Tools

Table of Contents:

Analytic tools are used widely to analyze the data in meaningful form among the sets of large volumes of data. Now in the market, there are many tools evolving every day to analyze the data in all forms to extract the value from the available data. It reduces the cost and time of any company as the past data can be used to take as many decisions as possible in critical situations. It is turning out to be a must for all companies because of the productivity it gives at a minimal cost. As most of the tools are open source, users can download them for free and change the modules as per the requirements of the organization.

1.10.1 Hadoop

It is a software library framework, where processing of distributed systems will be done on clusters (Vavilapalli et al. 2013)- It can be extended from one to multiple machines in any environment. It is one of the most commonly used tools in big data processing in many companies for extracting the data; the hardware requirements are less as the data processing is mostly done on the cloud.

1.10.1.1 Features of Hadoop

Many features that are associated with Hadoop; few major features are listed here.

1.10.1.1.1 HDFS

It is one of the distributed file systems that run on any hardware but gives high performance and throughput by using the MapReduce algorithm. The Hadoop File System (HDFS) stores data across multiple machines by replicating the data in all other servers, in case if any data fails in primary servers. It contains two nodes:

■ Name node

■ Data node.

Features of HDFS

■ Fault tolerance

■ High reliability

■ High replication

■ Scalability

■ Distributed storage.

1.10.1.1.2 MapReduce

It contains two tasks: map and reduce. Map gets a set of data and changes it into set of data in which the elements are broken down into Key-Value pairs. Then the reduce task takes input from the map and splits those tuples into smaller tuples and maps (Bhandarkar 2010).

Map - Splitting and Mapping Reduce - Shuffling and Reducing

Features of MapReduce

■ Local data processing

■ In-built redundancy

■ Independent language

■ MapReduce execution framework

■ Inter-process communication.

1.10.2 Apache Spark

It is an open-source and cluster computing framework. It is designed for fast computation and works on the concept of MapReduce. It has memory cluster computing which increases the processing speed of application.

Features of Spark

■ Speed

■ Advanced analytics

■ Swift processing

■ Dynamic in nature

■ In-memory computation

■ Reusability

■ Fault tolerance

■ Supports multiple languages like Java, R, Scala, and Python.

1.10.3 Apache Storm

It is a free and open source real-time distributed real-time computation system written in Java and Clojure. It is leading to real-time data analytics.

Features of Storm

■ Robust and user friendly

■ Real-time stream processing

■ Fault tolerance

■ Flexible

■ Reliable

■ Operational intelligence.

1.10.4 NoSQL Databases

In 1970s, Flat File Systems were used to store the data, but the problem is there is no standardization in the storage. It is a non-relational database or non-SQL database. It works on the mechanism apart from the tabular relations model for storing data. Preferably it is used to store real-time web applications and big data (Han et al. 2011).

Databases can be classified into three types:

  • 1. RDBMS
  • 2. Online analytical processing (OLAP)
  • 3. NoSQL.

Features of NoSQL

■ Performance is high.

■ Used as a familiar query language.

■ Less downtime.

■ Scalability is easy.

■ Flexible.

1.10.5 Cassandra

Here the data will be stored on many servers more than one replication factor so that the data will be available at all points of time without any downtime (Lakshman and Malik 2010).

Features of Cassandra

■ Fast writing

■ Replication

■ Schema-free

■ Transaction support

■ Application programming interface

■ Schema-free

■ Flexible data storage.

1.10.6 RapidMiner

It is an environment for data mining and machine learning (ML). It can be applied in both research and real-world data mining tasks (Hofmann et al. 2013)-

Applications

■ Multimedia mining

■ Text mining

■ Feature engineering

■ Datastream mining

■ Distributed data mining.

Conclusion

In this chapter, components of IoT devices, such as sensors, cloud servers, IoT gateway and physical devices, which are used in transmission of the data from the environment to the network, have been discussed. This chapter discusses big data analytics where data are generated continuously. Here the challenges in the generation of big data are classified, and different patterns of IoT data have been categorized. Media, business, and IoT are the major sources of big data generation. Big data system components such as data acquisition, data retention, data processing, data transport, data leverage are explained. Predictive, prescriptive, diagnostic, and descriptive analytics are used to take an optimal decision. Tools are also used in storing the data according to the format.

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