Big Data
Big Data is a collection of data that is huge in volume (may be in Petabytes), yet growing exponentially with time. It is data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Sources of Big Data
Some of the sources of big data are:
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Social Media: Twitter, Facebook, LinkedIn, Google generate huge amounts of data per day. Stats say that 500+ TB of data is generated into the database of Facebook daily, mainly in terms of images, audio, videos, messages, etc.
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Stock Exchange: For example, New York Stock Exchange generates one TB of new data per day.
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Jet Engines: A single jet engine can generate 10+ terabytes of data in 30 minutes of flight time. With many thousands of flights per day, the generation of data reaches up to many Petabytes.
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E-commerce Sites: Sites like Amazon, Flipkart, Alibaba generate huge amounts of logs from which users’ buying trends can be traced.
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Weather Stations: All the weather stations and satellites give very large data which is stored and manipulated to forecast weather.
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Telecom Companies: Telecom companies study user trends and accordingly publish their plans, storing data of millions of users.
Types of Big Data
There are three types of Big Data:
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Structured: Any data that can be stored, accessed, and processed in the form of a fixed format is termed as ‘structured’ data. Data stored in a relational database management system (RDBMS) is one example. Structured Query Language (SQL) is often used to manage such data.
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Unstructured: Any data with unknown form or structure is classified as unstructured data. A typical example is a heterogeneous data source containing text files, images, videos, etc. The output returned by Google search is unstructured data.
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Semi-structured: Semi-structured data can contain both forms of data. It may appear structured but is not defined by a table in a relational DBMS. An example is data represented in an XML file.
Characteristics of Big Data
Big data can be described by the following characteristics:
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Volume: The name Big Data itself relates to an enormous size. Whether data can be considered Big Data depends on its volume.
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Variety: Variety refers to heterogeneous sources and data types, both structured and unstructured. This includes emails, photos, videos, monitoring devices, PDFs, audio, etc., posing storage and analysis challenges.
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Velocity: Velocity refers to the speed of generation of data and how quickly it can be processed. The volume of data is estimated to double every 2 years.
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Variability: Variability refers to inconsistency in data, which can hamper effective handling and management.
Benefits of Big Data Processing
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Business Intelligence: Businesses can utilize external data from search engines and social media to fine-tune their strategies.
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Improved Customer Service: Big Data and natural language processing replace traditional feedback systems to better evaluate consumer responses.
Applications of Big Data
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Smarter Healthcare: Organizations can predict patient conditions using petabytes of patient data.
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Telecom: Big Data reduces data packet loss and improves seamless connections.
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Retail: Understanding consumer behavior, e.g., Amazon’s recommendation engine, improves customer experience.
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Traffic Control: Sensors and data help manage urban traffic congestion.
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Manufacturing: Big Data analysis improves product quality, efficiency, and reduces defects.
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Search Quality: Google uses generated data to continuously improve its search algorithms.
Conclusion
Big Data has become an essential resource in today’s digital world, driving decisions, improving services, and optimizing operations across industries. Its ability to process vast and complex datasets allows organizations to gain valuable insights, enhance efficiency, and respond quickly to changing trends. As technology continues to evolve, Big Data will remain a cornerstone of innovation and intelligent decision-making in multiple sectors.