Data is a critical aspect of every business or organization. In fact, it is capable of influencing an organization’s plan of action and can also be utilized for the success and growth of a project. This article is an introduction to Big Data & Hadoop and includes all the basics that you need to know.
What is Big Data?
Big Data is basically a term used to describe a large volume of complex, diverse and fast-changing data which is acquired from new data sources as well as the analysis of it. The complexity and diversity of these data sets makes it extremely difficult to store and manage it efficiently with traditional data processing and management tools. Big Data is classified into two main types, namely structured data and unstructured data. SQL databases are examples of structured data while document files as well as data from search engine results are examples of unstructured data or data whose form is unknown. As organizations strive to make their mark in a highly competitive market, there is need for more effective ways to manage data which involves handling huge amounts of data which is no mean task. It is therefore important to incorporate Big Data development strategies that are capable of transforming your business.
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Characteristics of Big Data
Followings are the key characteristics of Big Data:
The term Big Data describes data which is enormous in size. The value of data largely depends upon its size. Volume is a major characteristic that determines whether data is big or not.
This refers to the heterogeneous nature of data whether structured or unstructured. In the past, most applications considered databases and spreadsheets as the only sources of data, but today, data is available in many forms such as photos, emails, videos, audio, PDFs, monitoring devices among others. However, this variety also poses a few challenges that include storage, mining as well as analysis.
This term generally refers to how fast data is generated or the speed of data generation and processing to meet the demands which also determines the potential in that data. The velocity of Big Data involves the speed at which data from sources such as business processes, mobile devices, social media sites, application logs and sensors flows in. This flow of data is not only massive in nature but is also continuous.
This is the inconsistency which data can show at times, thus affecting the effective management and handling of data.
Importance of Big Data Development
A business or organization’s ability to process and develop Big Data comes with several benefits that include:
- Improved customer service
- Early identification of business risks if any
- Ability of a business to utilize outside intelligence in decision making
- Better operational efficiency
Companies can access data from search engines and other social sources of data such as Facebook and Twitter to fine tune their business strategies. Customer feedback has also been improved thanks to new systems that have adopted Big Data technologies. These technologies can greatly improve the efficiency of a business.
Risk Associated with Big Data
Some of the biggest risks of big data include:
- Lack of privacy
- Cyber security threats
- Over relying on data which can be risky sometimes
- Projects in Hadoop & Big Data – Learn by Building Apps
- Cloud Computing Applications – Big Data & Applications in Cloud by University of Illinois
- Taming Big Data with MapReduce and Hadoop – Hands On
- Big Data – Capstone Project by University of California San Diego
- Big Data Analytics by University of Adelaide
Big Data and Hadoop – Features and Ecosystem
Hadoop is simply a framework that uses a simple programming model to allow for the distributed processing of huge chunks of data sets across several commodity computers. This framework can effectively store and analyze data available in different machines quickly and in a cost effective way. Hadoop uses the MapReduce concept that allows it to divide a query into small portions before processing them in parallel. Some key features of Hadoop include:
- Flexibility in data processing
- Scalability (new nodes can be added onto the platform on need basis)
- Fault tolerance (Availability of data backup)
- Extremely fast data processing
- A robust ecosystem
- Cost effective (Reduces the cost of storage)
The Hadoop ecosystem comes with great projects such as Hive, MapReduce, Apache Pig, HBase, HCatalog, Zookeeper among many other new tools that are designed for better data processing.
Hadoop Architecture and Building Blocks
Hadoop is composed of four main building blocks, namely Hadoop Common, Hadoop Distributed File system, MapReduce and Yet Another Resource Negotiator (YARN). The Hadoop Distributed File System provides reliable data access and storage across the various nodes of a Hadoop cluster while the Hadoop Common supports other Hadoop modules. Yarn assigns storage, memory and CPU to all applications running on the Hadoop cluster. YARN is Hadoop’s core architectural center that enables multiple data processing engines like reel-time streaming and interactive SQL to handle data that is stored in a single platform.
Big Data Analytics
Many organizations are using Big Data analytics while making operational and strategic decisions. By examining Big Data sets, an organization can discover hidden patterns, customer preferences, market trends, unknown correlations and any other useful information that can help in designing more effective marketing campaigns as well as improved customer service.
This is an overview of Big Data and related concepts/technologies. More related articles with tons of information will be shared later.