There are a few additional criteria that allow information to be classified as "big data," even though big data is most often defined by its large volume. "Big data" refers to massive, voluminous data sets that are typically extremely complex, mostly unstructured, and derived from various data sources. Another distinguishing feature of this data is that it cannot be managed by traditional analytical tools due to its size, complexity, and unstructured nature. Any software or utility that can analyze and process large amounts of data that may be complex or even in real-time and cannot be managed by conventional data processing tools is referred to as "big data technology." Boost your Skills by learning: Digital Marketing.
Table of Content
Big Data Technologies and Tools
Use Cases for Big Data Technology
The big data Three V's:
Big data can also be defined by the three "Vs" of volume, velocity, and variety.
Volume: Extremely large amounts of data, It might be several terabytes for some businesses, petabytes for others, and exabytes for still others.
Velocity: This describes the rapid rate at which big data is created or gathered, as well as the speed at which some fundamental processing or any action on the data is carried out. The velocity of data collected from sensors on appliances in IoT-driven smart products may be so high that the data moves directly to memory, necessitating real-time or nearly real-time actions to be taken on the data.
Variety: Historically, data had been in very fixed formats that could be incorporated into relational database structures. Big data, however, includes a variety of data types, including unstructured and semi-structured data, which must be processed and analyzed using unique tools and technologies.
Out of the three main categories, big data can be classified as either structured, unstructured, or semi-structured.
Structured data: This kind of data can be processed in its original form without being converted to a digital format or going through any other format changes in order to be used for analysis.
Unstructured data: This describes a more ad hoc data type that does not have any predefined formatting and therefore requires some preprocessing in order to be used for analysis. This type of data may need to be converted into structured data, which could take resources and time.
Semi-structured data: As the name suggests, this type of data has a certain amount of innate hierarchy or categorization but still lacks the distinguishing characteristics of structured data and cannot be stored in relational databases.
Big Data Technologies and Tools
Pre-processing and analysis of big data involve a number of steps in order to derive useful and worthwhile insights from the data. Following that, the analysis assists in revealing hidden patterns in the data, new trends, or even customer preferences, all of which can be helpful in supporting data-driven decisions. Additionally, cloud computing and AI/ML help automate some processes and reduce manual labor.
Data acquisition, which entails locating and then gathering big data, is the first step.
The following action is data storage. Newer storage techniques have emerged as a result of the traditional DBMS systems' inability to handle the volume, velocity, and variety of big data. Today, distributed storage and MPP are the most widely used foundations for big data storage solutions.
The growth of big data has also led to an evolution of databases. The preferred format for big data storage in NoSQL databases—a flexible and cloud-friendly way to store and handle unstructured data—is JSON (JavaScript Object Notation).
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Use Cases for Big Data Technology
Big data technology is being used in a variety of ways by various sectors and industries outside of the technology sector.
Product manufacturers like Proctor & Gamble are gathering and analyzing vast amounts of data from numerous sources, including social media, focus groups held in various locations, and test marketing initiatives. Using big data technology, they are creating predictive models that link the commercial success of new and old products to their data attributes.
Big data technology has made predictive maintenance of mechanical and remote devices possible in a variety of heavy industries, including oil and gas, engineering, and construction, which use machinery. These organizations are able to reduce maintenance costs and prevent production downtimes by using data analytics to predict the likelihood of potential issues or mechanical failures before they actually occur.
Big data is being used in the sports industry to analyze team and player performance using statistical game data analysis, as well as to understand viewership trends for major sporting events like Wimbledon and FIFA.
Big data analytics is also widely used in the financial sector, with exchange commissions using it to stop illegal stock market trading and to lower fraudulent financial transactions in the banking sector.
Big data is used in the healthcare industry to locate and track the global spread of virus infections. The data from national census surveys on the state of healthcare in their countries is also analyzed by health ministries.
As we move into an era of IoT and connected devices, big data analytics will become more important. The biggest impact of big data technology may be felt in the area of operational effectiveness. Driving innovation using predictive analytics and data-driven insights into consumer trends and preferences is the other area of significant impact. The big data and analytics sector is also proving to be one of the biggest job creators in the modern economy with its lightning-fast growth. The majority of businesses are now expanding their teams with knowledgeable internal data analysts and scientists who can harness the power of big data in a methodical and scientific way to support business growth. With big data, the possibilities are truly endless.