Is Big Data!!! that big? – No Big Deal, Let’s Dig this Deeper Now

Is Big Data!!! that big? – No Big Deal, Let’s Dig this Deeper Now

Introduction:

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. It is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Data in its raw form has no value. Data needs to be processed in order to be of value. However, herein lies the inherent problem of big data.

Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. Even previously there was huge data which were being stored in databases, but because of the varied nature of this Data, the traditional relational database systems are incapable of handling this Data. Big Data is much more than a collection of datasets with different formats, it is an important asset which can be used to obtain enumerable benefits. Big Data in a way just means all data. And there is quite some data nowadays.

The sheer volume of data we can tap into is dazzling and, looking at the growth rates of the digital data universe, it just makes you dizzy.

Types of Big Data:

• Structured: Any data that can be stored, accessed and processed in the form of fixed-format is termed as structured data. Data stored in a relational database management system (RDBMS) is one example of structured data. It is easy to process structured data as it has a fixed schema. Structured Query Language (SQL) is often used to manage such kind of Data
• Unstructured: The data which have unknown form and cannot be stored in RDBMS and cannot be analyzed unless it is transformed into a structured format is called as unstructured data. Text Files and multimedia contents like images, audios, videos are an example of unstructured data
• Semi-structured: Semi-structured data can contain both forms of data... Partially organized data which does not have a fixed format. Ex: XML, JSON

Characteristics of Big Data:

• Volume: The quantity of generated and stored data. The amount of data matters. With big data, we will have to process high volumes of low-density, unstructured data. Size of data plays a very crucial role in determining value out of data. Hence, 'Volume' is one characteristic which needs to be considered while dealing with Big Data. The complete volumes of data and information that gets created whereby we mainly talk about infrastructure, processing, and management of big data, be it in a selective way. These datasets can be orders of magnitudes of larger than traditional datasets, which demands more thought at each stage of the processing and storage life cycle
• Velocity: Velocity is about where analysis, action and also fast capture, processing and understanding happen and where we also look at the speed and mechanisms at which large amounts of data can be processed for increasingly near-time or real-time outcomes, often leading to the need for fast data. Velocity is defined as the pace at which different sources generate the data every day. This flow of data is massive and continuous.

• Variety: Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media. Variety is about the many types, being structured, unstructured and everything in between. Data comes in different forms. It can be structured, semi-structured or unstructured. Hence, there is a variety of data which is getting generated every day
• Value: The ultimate challenge of big data is delivering value. As said, we add value to that as it’s about the goal, the outcome, the prioritization and the overall value and relevance created in Big Data applications. It is all well and good to have access to big data but unless we can turn it into the value it is useless
• Veracity: It involves the accuracy of the data. In other words, how much can you trust the data you’re using? Therefore, the veracity of your data is essential. This refers to the quality of the collected data
• Variability: Variation in the data leads to wide variation in quality. It refers to the inconsistency which can be shown by the data at times, thus restricting the process of being able to handle and manage the data effectively

Prerequisites:

• Data: The quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media
• Visualization: Visualization is the creation of complex graphs that tell the data scientist’s story, transforming the data into information, information into insight, insight into knowledge, and knowledge into advantage
• ETL: Build and schedule pipelines for recurring data transformations. ETL stands for extract, transform, and load. It refers to the process of taking raw data and preparing it for the system's use
• Interactive Analytics: Enable data teams and less technical users to analyze less structured or raw data that otherwise can’t fit in a data warehouse

• Data lake: Data lake is a term for a large repository of collected data in a relatively raw state. This is frequently used to refer to the data collected in a big data system which might be unstructured and frequently changing
• Data mining: Data mining is a broad term for the practice of trying to find patterns in large sets of data. It is the process of trying to refine a mass of data into a more understandable and cohesive set of information. Once the data is stored in the data management system
• Data warehouse: Data warehouses are large, ordered repositories of data that can be used for analysis and reporting. In contrast to a data lake, a data warehouse is composed of data that has been cleaned, integrated with other sources, and is generally well-ordered
• Hadoop: Hadoop is an Apache project that was the early open-source success in big data. It consists of a distributed filesystem called HDFS, with a cluster management and resource scheduler on top called YARN (Yet Another Resource Negotiator). The open-source framework that is widely used to store a large amount of data and run various applications on a cluster of commodity hardware. It has become a key technology to be used in big data because of the constant increase in the variety and volume of data and its distributed computing model provides faster access to data.
• Predictive Analytics: Predictive analytics uses data, statistical algorithms and machine learning techniques to identify future outcomes based on historical data. It’s all about providing the best future outcomes so that organizations can feel confident in their current business decisions

Advantages:

• Product Development, Customer Experience, and Fraud Compliance
• Machine Learning, Operational Efficiency, and Drive Innovation 

Disadvantages:

• Data Quality, Discovery, Storage, Analytics, Security, and Lack of Talent

                         

 

Applications:

• Entertainment, Insurance, Driver-less Cars, Automobile, and Government
• Data Management, Analytics, Video, Audio, Healthcare, and Education
• Media, Insurance, IoT, Information Technology, Retail, and Science
• Sports, Technology, Smarter Healthcare, Telecom, Retail, and Traffic control
• Manufacturing, Fintech, Robotics, Meteorology, and Medicine

Developer-Take-A-Ways!

• https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
• https://www.udemy.com/topic/big-data/
• https://academy.microsoft.com/en-us/professional-program/tracks/big-data/
• https://intellipaat.com/all-courses/big-data/
• https://www.coursera.org/specializations/big-data
• https://www.edx.org/micromasters/big-data

Conclusion:

Big Data is a big game-changer. Many organizations are using more analytics to drive strategic actions and offer better customer experience. Big data is a broad, rapidly evolving topic. Big Data analysis has a definite business value.

Combining Big Data with high powered analytics will help your business in accomplishing complex tasks smoothly and without any hassle

I’m going to share a bunch of tools for developers at the Developer Take-A-Ways Section of the story, but feel free to comment, share or send me any other interesting videos or links you might have found. ✌ It’s a massive opportunity to work on. I hope you found this article useful.

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