Types of Big Data Technologies

Among the technologies that are used to handle, process, and analyse Big Data, the most effective and popular innovations have been in the fields of distributed and parallel processing, Hadoop, In-Memory Computing (IMC), Big Data cloud, etc. Hadoop, which is an open-source platform, has been by far the most popular technology associated with Big Data storage and processing different types of data.

Hadoop is commonly used by data-driven organisations to extract maximum output from their normal data-usage practices at a rapid pace. Besides Hadoop, the other techniques used for Big Data processing are cloud computing and IMC.

These technologies help organisations to analyse data in different ways under varying circumstances. Cloud computing helps businesses to save cost and better manage their resources by allowing them to use resources as a service on the basis of specific requirements and paying for only those services that have actually been used.

IMC helps you to organise and complete your tasks faster by carrying out all the computational activities from the main memory itself. You can use all these techniques, as per specific requirements, for analysing Big Data.

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Types of Big Data Technologies

Hadoop

Traditional technologies have proved incapable to handle the huge amounts of data generated in organisations or to fulfil the processing requirements of such data. Therefore, a need was felt to combine a number of technologies and products into a system that can overcome the challenges faced by traditional processing systems in handling Big Data.

One of the technologies designed to process Big Data (which is a combination of both structured and unstructured data available in huge volumes) is known as Hadoop. Hadoop is an open-source platform that provides the analytical technologies and computational power required to work with such large volumes of data.

Earlier, distributed environments were used to process high volumes of data. However, multiple nodes in such an environment may not always cooperate with each other through a communication system, leaving a lot of scope for errors. Hadoop platform provides an improved programming model, which is used to create and run distributed systems quickly and efficiently.

The following are some of the important features of Hadoop:

• Hadoop performs well with several nodes without requiring shared memory or disks among them. Hence, the efficiency-related issues in context of storage and access to data get automatically solved.
  
  
• Hadoop follows the client–server architecture in which the server works as a master and is responsible for data distribution among clients that are commodity machines and work as slaves to carry out all the computational tasks. The master node also performs the tasks of job controlling, disk management, and work allocation.
  
  
• The data stored across various nodes can be tracked in Hadoop. It helps in accessing and retrieving data, as and when required.
  
  
• Hadoop improves data processing by running computing tasks on all available processors that are working in parallel. The performance of Hadoop remains up to the mark both in the case of complex computational questions and of large and varied data.

Hadoop keeps multiple copies of data (data replicas) to improve resilience that helps in maintaining consistency, especially in case of server failure.

Cloud Computing and Big Data

One of the vital issues that organisations face with the storage and management of Big Data is the huge amount of investment to get the required hardware setup and software packages. Some of these resources may be over-utilised or underutilised with varying requirements over time. We can overcome these challenges by providing a set of computing resources that can be shared through cloud computing.

These shared resources comprise applications, storage solutions, computational units, networking solutions, development and deployment platforms, business processes, etc. The cloud computing environment saves costs related to infrastructure in an organisation by providing a framework that can be optimised and expanded horizontally. In order to operate in the real world, cloud implementation requires common standardised processes and their automation.

In-Memory Computing Technology for Big Data

We learned that distributed computing can help us meet requirements of storage and processing power for Big Data analytics. Another way to improve the computational speed and power of processing data is to use IMC. The representation of data in the form of rows and columns makes data processing easier and faster.

Today, however, the data being generated is largely unstructured. The volume of data being generated today must be processed at a very high speed because the data is growing at a very fast rate. IMC is used to facilitate high-speed data processing. For example, IMC can help in tracking and monitoring consumers’ activities and behaviours, which allow organisations to take timely actions for improving customer services and, thus, customer satisfaction.

We all know that data is stored on external devices known as secondary storage space. This data had to be accessed from an external source whenever an operation or any kind of modification on the data was required. External sources are accessed through Input/Output (I/O) channels that transfer data temporarily from secondary storage space to primary storage for processing purposes.

The process of accessing external devices used to consume too much time during which the CPU could not be used for any other operation. The advantage of using external devices for data storage is that secondary storage is economical as compared to primary storage.

Hive

Hive is a mechanism through which we can access the data stored in Hadoop Distributed File System (HDFS). Hive provides a Structured Query Language (SQL) interface, HiveQL, or the Hive Query Language.

This interface translates the given query into a MapReduce code. HiveQL enables users to perform tasks using the MapReduce concept but without explicitly writing the code in terms of the map and reduce functions.

The data stored in HDFS can be accessed through HiveQL, which contains the features of SQL but runs on the MapReduce framework. It should be noted that Hive is not a complete database and is not meant to be used in Online Transactional Processing Systems, such as online ticketing, bank transactions, etc.

PIG

Pig was designed and developed for performing a long series of data operations. The Pig platform is specially designed for handling many kinds of data, be it structured, semi-structured, or unstructured. Its aim, as a research project was to provide a simple way to use Hadoop and focus on examining large datasets.

Pig became an Apache project in 2007. By 2009, other companies started using pig, making it a toplevel Apache project in 2010. Pig can be divided into three categories: ETL (Extract, Transform, and Load), research, and interactive data processing. Pig consists of a scripting language, known as Pig Latin, and a Pig Latin compiler.

The benefits of Pig programming language are:

• Ease of coding: Using Pig Latin, we can write complex programs. The code is simple and easy to understand and maintain. It takes complex tasks involving interrelated data transformations as data flow sequence and explicitly encodes them.
  
  
• Optimisation: Pig Latin encodes tasks in such a way that they can be easily optimised for execution. This allows users to concentrate on the data processing aspects without bothering about efficiency.
  
  
• Extensibility: Pig Latin is designed in such a way that it allows us to create our own custom functions. These can be used for performing special tasks. Custom functions are also called userdefined functions.

Tableau

Tableau products are the most widely used software tools for data visualisation. There are various types of Tableau products available in the market. Some of the commonly known products include Tableau Desktop, Tableau Server, Tableau Online, Tableau Reader, and Tableau Public.

One of the most popular applications of Tableau software tools is Tableau Desktop, which provides various facilities to create visualisations. The breakthrough approach used to build the Tableau Desktop tool takes pictures of data and converts them into optimised database queries, which help users in spotting patterns, identifying trends, and deriving logical conclusions and insights.

While working with the Tableau Desktop, the data analyst need not write any code; all the insights can, instead, be discovered by just connecting to the data and following the thoughts that strike the mind naturally. You can easily connect to your data, which is either in the memory or on the server.

Tableau Desktop allows you to directly retrieve data from the server or load it in the Tableau data engine from a disk. The speed of Tableau Desktop is as fast as the thoughts of human beings, and everything can be done with drag-and-drop technology.

Tableau Desktop provides options for data sharing in the form of dashboards, which can be used to reflect relationships by highlighting and filtering data. Dashboards can also help you create story line in a guided manner for explaining the insights obtained from data. Moreover, you can use Tableau variants, such as the Tableau Online and Tableau Server tools for sharing content.

The important features of Tableau software include the following:

• Single-click data analytics in visual form
• In-depth statistical analysis
• Management of metadata
• In-built, top class data analytic practices
• In-build data engine
• Big Data analytics
• Quick and accurate data discovery
• Business dashboards creation
• Various types of data visualisation
• Social media analytics, including Facebook and Twitter
• Easy and quick integration of R
• Business intelligence through mobile
• Analysis of time series data
• Analysis of data from surveys

R Language

R is a cross-platform programming language as well as a software environment for statistical computing and graphics. Generally, it is used by statisticians and data miners for developing statistical software and doing data analysis.

It is also believed that R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. R is a GNU project, which is freely available under the GNU General Public License and its pre-compiled binary versions are provided for various operating systems.

Python

Python is a high-level, open-source, interpreted language that is ideal for object-oriented programming. Python has a lot of features for dealing with arithmetic, statistics and scientific functions.

Python is opensource software, which means anybody can freely download it from www.python.org and use it to develop programs. Its source code can be accessed and modified as required in the projects.

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Other Tools and Technologies to Handle Big Data

Some other important tools and technologies that are used for handling the big data are as follows:

• MapReduce: Originally developed by Google, the MapReduce website describe it as “a programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes.” It’s used by Hadoop, as well as many other data processing applications. Operating System: OS Independent.
  
  
• GridGain: GridGrain offers an alternative to Hadoop’s MapReduce that is compatible with the Hadoop Distributed File System. It offers in-memory processing for a fast analysis of real-time data. You can download the open source version from GitHub or purchase a commercially supported version from the link above. Operating System: Windows, Linux, OS X.
  
  
• HPCC: Developed by LexisNexis Risk Solutions, HPCC is short for “high performance computing cluster.” It claims to offer superior performance to Hadoop. Both free community versions and paid enterprise versions are available. Operating System: Linux.
  
  
• Storm: Now owned by Twitter, Storm offers distributed real-time computation capabilities and is often described as the “Hadoop of real-time.” It is highly scalable, robust, fault-tolerant, and works with nearly all programming languages. Operating System: Linux.
  
  
• Cassandra: Originally developed by Facebook, this NoSQL database is now managed by the Apache Foundation. It’s used by many organisations with large, active datasets, including Netflix, Twitter, Urban Airship, Constant Contact, Reddit, Cisco and Digg. Commercial support and services are available through third-party vendors. Operating System: OS Independent. ‰‰
  
  
• Hbase: Another Apache project, HBase is the non-relational data store for Hadoop. Features include linear and modular scalability, strictly consistent reads and writes, automatic failover support and much more. Operating System: OS Independent.
  
  
• MongoDB: MongoDB was designed to support humongous databases. It’s a NoSQL database with document-oriented storage, full index support, replication and high availability, and more. Commercial support is available through 10gen. Operating system: Windows, Linux, OS X, Solaris.
  
  
• Neo4j: The “world’s leading graph database,” Neo4j boosts performance improvements up to 1000x or more versus relational databases. Interested organisations can purchase advanced or enterprise versions from Neo Technology. Operating System: Windows, Linux.
  
  
• CouchDB: Designed for the Web, CouchDB stores data in JSON documents that you can access via the Web or query using JavaScript. It offers distributed scaling with fault-tolerant storage. Operating system: Windows, Linux, OS X, Android.