An Introduction to Big Data Analytics – Advantages, Challenges and Applications

This blog discusses the core concepts of big data, its significance, types, and properties. The blog will also discuss applications, working methods, and cutting-edge technologies in addition to these topics. Big data is extremely large data that is used to capture extremely large amounts of data that grow exponentially over time.

What is Big Data?

The enormous increase in data volume gives rise to the idea of big data. This makes organising and sorting the data challenging. The term “big data” is typically used to refer to a mixed dataset that is extremely large and combines both structured and unstructured data. It is a process that entails gathering, analysing, and preparing this enormous amount of data gathered from many sources. Some examples of big data are Facebook, the Exchange-stock market, search engines like Google, and airway data.

The three Vs—Volume, Velocity, and Variety—are typically used to describe the properties of big data.

• The primary identifying factor for categorising Big Data is volume. Top social media platforms often get enormous amounts of data measured in terabytes or kilobytes. Using traditional methods to monitor such data becomes quite challenging. In files, records, and tables, specific data are gathered.
• Velocity is the second. It refers to the speed at which data is ingested and processed. Every day, around 2.5 quintillion bytes of data are received. So it is impossible to take action using conventional techniques.
• Variety is the third. It alludes to the distinctive sources used to gather the data. From the data’s structure to its category, there may be variation. Among the various categories are text, video, and artificially created visuals. Veracity, worth, and variety are some further common traits.

Types of Big Data

• Structured: The term “structured data” is disclaimed to refer to any data that can be easily accessed, stored, and processed. In this, the format of the data being stored is already known. The values of a certain table kept in a database are an example of such data.
• Unstructured: Unstructured data includes any data whose source is ambiguous and unformatted. Here, information comes from unbiased sources that combine text, video, and audio records. All the search results returned from a search engine site are an example of data.
• Semi-structured: Semi-structured data combines both structured and unstructured information. Despite being defined, this data is not kept in a relational database.

Challenges in Big Data

• Data storage: The primary challenge is data storage and arrangement due to the quick development in the data size in recent times.
• Data refining: The procedure’s most difficult and tedious part is refining the data. It is demanding to clean up such a large volume of data. It is important to curate and make it understandable and useful to make it relevant.
• Keeping up: Big data technology is evolving at a rapid rate. A few years ago, Apache Hadoop was the talk of the town, followed by Apache Spark, and now a hybrid of the two is available.
• Cybersecurity risks: Large data also carries an increased risk of a security compromise. Businesses with a lot of data are increasingly the focus of cybercrimes.

Applications of Big Data

• Weather forecasting: Numerous mobile applications are utilised to make weather predictions. Using barometers, ambient thermometers, and hygrometers can increase the accuracy of this forecasting. This has many uses, including researching the effects of global warming and preparing for crisis management measures.
• Advertising: The current marketing strategy no longer bases price changes on consumer reactions. To ascertain the types of adverts that consumers are interested in, several data points, surveys, traffic patterns, purchasing trends, eye movement patterns, and movie preferences are all considered.
• Personal Grooming: Big Data is being used to enhance individual growth and wellness. Fitbits, sleep tracking, calorie counting, and exercise tracking are all activities that aid in gaining insight into personal development.
• Health Industry: Another area that generates a tonne of data is this one. The likelihood of making an inappropriate diagnosis can significantly decrease if a proper analysis is conducted using this data. Based on thorough investigation and analysis of prior results, medications are given out. Several health bands have also been introduced to the market, increasing consumer awareness of their health.
• Transportation: Big data can be used in the transportation industry in a variety of applications, including route planning, traffic control, and congestion management. Big data is used to manage all the real-time routes and traffic concerns that taxis use. These characteristics have favourable effects, such as reducing pollutants, saving time, and improving safety controls.

Working Mechanism of Big Data

There have been numerous design proposals to manage the data effectively. Among them, Hadoop was one. To successfully process enormous amounts of data, it is now one of the greatest open-source technologies made accessible under the Apache licence.

HDFS (Hadoop Distributed File System) and MapReduce Engine are the two primary parts of Hadoop.

Data Access Components

• Pig: Pig is a programme used to analyse huge data collections. Their parallelization-enabled structure allows for greater efficiency when handling large amounts of data. This also has the crucial characteristic of concealing essential data during processing.
• Hive: It’s a data warehouse built on top of Hadoop. It processes and queries the data using the HiveQL language. This language is comparable to SQL but performs queries more quickly because of indexing.

In terms of an introduction to big data, its functioning and applications, and issues and challenges in big data, I believe this blog is pretty informative. It is essential at this time. Data processing has become an agonising chore due to the continuous growth in data volume. Big data has applications in the weather broadcasting, transportation, banking, and health industries. One of the well-known ideas to get over the drawbacks of managing massive data is Hadoop.