Data is a raw materialized form of facts, figures, symbols, and characters that can be processed with the help of a computer. It can be a picture, audio note, text, search history, location, etc.
Big data is a small data set collection that grows exponentially with time. It is so complex and extensive that no traditional and commercial data management software can handle or process it effectively. Big data is nothing but data with colossal size and quantity.
Example of Big Data
- The New York Stock Exchange generates about one Terabyte of new data daily.
- The giant social media site Facebook generates almost 500+TeraByte of data daily regarding photos, videos, comments, chat, etc.
- A single jet can create up to 10 Terabytes of data. And there are hundreds of jet flies in a single day.
Types of Big Data
There are three types of Big Data
1. Structured Data
2. Unstructured Data
3. Semi-Conductor Data
Any data that uses an expected format and has a defined structure. Structured data is usually stored in a well-defined pattern, such as a database or excel sheet, that can be read easily. It is generally tabular with columns and rows that define its attributes. We can use SQL to manage structured databases.
Example: Data Stored in DBMS or in any excel table which is in an organized form.
Unstructured Data do not have any particular form or structure, and they cannot be stored on Database management systems. We cannot process any unstructured data until and unless we don’t transfer its form from amorphous to structure.
Examples: Text files, videos, music, voice notes, etc.
Semi-Structured data do not have any particular structure like unstructured data in Database Management Systems. Still, they contain some organizational properties, for instance, markers and tags, which are helpful in semantic elements, making it easy for analysis.
Example: XML or JSON Document
Characteristics of Big Data
Some 5Vs define big data and its characteristics which include:
Volume is one of the most vital factors in considering a data’s state. It refers to the speed of data creation in a single day. It defines whether the data can be regarded as big data.
Big data velocity deals with the pace at which data flows from sources like business processes, machines, and networks. The flow of data is gigantic and nonstop. Real-time data helps companies and business owners to make decisions at the right time according to the situation and trends and take full advantage of it. This Big data mainly generates on Google search engines, Gmail, YouTube, Facebook, Linked In, Tiktok, etc.
Veracity refers to analyzing the quality of data. High-veracity data is considered the most valuable and useful data that contribute in meaningful ways to different organizations and companies. On the other side, low veracity data refers to the least valuable data, which could be more helpful for most organizations. Hence, it is known as noise.
Data in high volume, high velocity, and high veracity need to be processed with the help of the most advance and latest analytical algorithms to extract fruitful information from it.
Data with information is valuable. Big data is a waste if we are collecting and storing it; this technique will not work until we start to form the data into its beneficial form, giving us accurate and helpful results.
Variety makes big data significant. Big data is generated from different sources; it can come from search engine history, your check-in location, videos watched, uploading photos or videos on any social media account, and even from your phone when it’s idle. The data would be either structured, unstructured, or semi-structured.
Technologies of Big data
From Google Assistant to Self-driving cars and beyond, AI is developing at a much higher speed than it was ever before; being an interdisciplinary branch of science, it takes many approaches as Augmented reality, machine learning, and deep learning into account to make a remarkable shift in almost every tech industry.
The excellent aspect of AI is the power to intellectualize and make decisions that can provide a persuasive likelihood of achieving a definite goal. AI is evolving consistently to produce benefits in various industries. For example, AI can be used for drug treatment, healing patients, and conducting surgery in OT.
R is a programming language and an open-source project. Free software is mainly utilized for statistical computing, visualization, a unified developing environment like an eclipse, and visual studio communication assistant.
Experts believe that it is the most glorified language among all other languages in the IT industry. It is widely implemented for designing statistical software and mainly in data analytics.
The science of predictive analysis generates upcoming happenings with the help of primary data. It is a subpart of big data analytics. It uses machine learning technologies, data mining and statistical modelling, and some mathematical models to forecast future happenings.
With built-in features for streaming, SQL, machine learning, and graph processing support. Apache Spark earns cited as the fastest and most common generator for big data transformation. It supports significant languages of big data consisting of Python, R, Scala, and java.
The Hadoop ecosystem consists of a platform used to assist in resolving the challenges and issues surrounding big data. The Hadoop ecosystem comprises both Apache open-source projects and other wide variety of commercial tools and Solutions.
It is a highly secure ecosystem and an outstanding choice for various big data implementations in banking, finance, insurance, healthcare, retailing, etc.
BlockChain is assigned technology that consists of digital bitcoin currency, which is unique in that once the data has been written in blockchain, it can never be deleted or changed and will remain the same.