All About Big Data Processing and‬ ‭ Distribution Systems‬

‭ Introduction‬

‭ What is big data?‬

‭Big Data refers to a huge amount of information that regular computers or tools struggle to manage. This data comes‬ from many sources like social media, websites, mobile apps and many more. By analysing this data, we can discover‬ patterns, trends and insights that help in decision making.‬

‭ 5 Vs of Big Data‬

‭ Volume, Velocity, Variety, Veracity, and Value are the five Vs of Big Data. These Vs explain the challenges and‬ opportunities that come with a huge amount of information.‬

‭ Let’s talk about each of them separately :

‭ 1. Volume:‬‭ Volume is the quantity of data produced and stored each second.

It also helps us to identify whether the given data is big data or not.‬

‭ For example:‬

‭ E-commerce stores like Amazon generate a vast amount of data daily through purchases like phone numbers, bank‬ account details etc.‬

‭ 2. Velocity:‬‭ Velocity describes how fast data is generated‬‭ and how quickly data is moved and processed.‬

‭ For example :‬

‭ Streaming platforms like Netflix, which process a large amount of data from users in real time and suggest content‬ accordingly.‬

‭ 3. Variety:‬‭ Variety is the term used to describe the different types of data, such as semi-structured, unstructured, and‬ structured data.‬

‭ For examples;‬

– Employee database which is a structured data type.‬

– Social media conversation which is an unstructured data type.‬

– XML files, which is a semi structured data type as it is a combination of structured and unstructured data type.‬

‭ 4. Veracity:‬‭ Veracity describes the quality of data. It is important for ensuring that data is trustworthy and accurate.‬

‭ For example:‬

‭ In marketing campaigns, accurate customer data (like phone number) is essential for avoiding reading out to the wrong‬‭ audience.‬

‭ 5. Value:‬‭ Value is defined as the capacity to extract meaningful information from the raw data.‬

‭ For example :‬

‭ Social media platforms like YouTube and Instagram recommend content based on past user behaviour.‬

‭ How Big Data is Processed:‬

‭ What is Big Data processing?‬

‭ Big Data processing is a method of handling large amounts of information and analysing it. Since the data is too large‬ for regular computers or tools, we use specialised tools and techniques for processing it.‬

‭ There are four stages of Big Data Processing :‬

‭ Data Storage → Data Mining → Data Analytics → Data Visualisation‬‭

Data Storage:‬‭ This is a foundational step, which stores big data in systems like Hadoop, NoSQL, etc.‬

‭Data Mining:‬‭ In this stage, meaningful information and insight are extracted from raw data.‬

‭Data Analytics:‬‭ This stage focuses on analysing trends‬‭ and making decisions.‬

‭Data Visualisation:‬‭ This final stage involves presenting‬‭ data in the form of charts/graphs, which makes it easier to‬ understand.‬

‭Types of Data Processing:‬‭ Batch and Real-time Processing‬

‭Batch Processing:‬‭ It is a process of processing a‬‭ large amount of data at a specific time.‬

‭ It mainly focuses on data integrity and data reliability.‬

‭ For example, checking all exam papers together after the exam is finished.‬

‭ Hadoop and MapReduce are frequently used tools in Batch processing.‬

‭Real-time processing:‬‭ It is a process of analysing data as it arrives, providing immediate insights and authorising quick‬ response.‬

‭ It is mainly focused on providing a timely and consistent response.‬

‭For example, a UPI transaction in which every transaction detail is analysed immediately.‬

‭Apache Spark and Kafka are frequently used tools in Real-time processing.‬

‭Tools used in Big Data processing:‬

‭Some popular tools for handling and analysing Big Data include Apache Hadoop, Apache Spark, Hadoop Distributed ‭File System (HDFS), MapReduce, Apache Kafka and many more.‬

‭Let’s talk about each of them separately:‬

‭Apache Hadoop:‬‭ Large volumes of data can be processed and stored using this open-source system. It‬ works well for Batch processing.‬

‭Apache Spark:‬‭ It is another powerful tool that is‬‭ used for real-time big data processing, and it is even faster than‬ Hadoop. It can handle a large amount of data and can give instant insights.‬

‭MapReduce:‬‭ It is a program used inside Hadoop.It is employed to process data across several machines in parallel. It‬ breaks data into different pieces and then brings them together to give a result.‬

‭Apache Kafka:‬‭ It is used in real-time processing when‬‭ data needs to move quickly, like in messaging apps or stock‬ market systems.‬

‭How Big Data is Used in the Real World‬

‭In today’s world, there is a lot of data, and this big data is used by all big companies. Companies used this data to find‬ out the interests of their customers, use this data for marketing campaigns and use it for many more things which help‬ in their growth.‬

‭ Let’s discuss some industries and examples :‬

‭E-commerce stores (like Amazon, Flipkart):‬‭ These companies‬‭ use data to analyse customers’ browsing history,‬ purchases, and reviews. And they recommend products based on this analysis.‬

‭Entertainment apps (like Netflix, Amazon Prime):‬‭ These‬‭ apps recommend movies and shows based on analysing‬ customer data like their search history, previously watched.‬‭

Transportation apps (like Ola, Uber):‬‭ These apps track customers’ live location and suggest estimated fare and time‬ using real-time processing.‬

‭Challenges Faced in Big Data Processing‬

Now, as we have seen the benefits and real-life use of Big Data, it’s time to face the challenges that come with Big‬ Data. Let us discuss some common challenges that come with Big Data :‬

‭Data volume:‬‭ Managing and storing these large amounts‬‭ of data is one of the most common challenges because we‬ need huge storage and high-speed processing power.‬

‭Data quality:‬‭ Extracting meaningful data from raw‬‭ data is also a common big challenge because these raw data are‬ messy, incomplete, inaccurate or duplicate.‬

‭Security and privacy:‬‭ Big data is one of the most‬‭ important targets of cybercriminals because it contains personal and‬ business information. And it is very difficult to keep it secure from hackers, leaks or misuse.‬

Scalability:‬‭ These data are increasing. In order to manage them and preserve them, the system must be scalable. But‬ the real issue is that a lot of tools and businesses are not ready to handle this growth.‬

‭Data analysis:‬‭ To analyse these big raw data and find‬‭ important or meaningful insights, we need special analytical‬ techniques and tools.‬

‭ Future Trends and Innovations in Big Data‬

‭According to a report, the expected CAGR growth of the big data market is about 12.7% between 2023 and 2028. This‬ shows how fast the Big Data market is growing. With the rise of new technology, the future of big data is evolving rapidly‬ and it has great scope for the future.‬

‭The future of AI with Big Data:‬

‭Nowadays, one of the major trends is the use of Artificial Intelligence (AI). AI is used to make smart predictions, and It‬ enables big data to be automatically learned by systems.. For example, platforms like Instagram start using AI for‬ analysing customers’ behaviour and recommending content accordingly.‬

‭Future of Big Data in cloud computing:‬

‭Another growing trend is the use of cloud-based storage platforms like AWS or Google Cloud, which make it easier to‬ manage larger amounts of data.‬

‭Demand for Data scientists and CDOs:‬

‭The demand for data scientists and CDOs is increasing day by day as big data is growing rapidly. According to a BLS‬ report, data scientists’ employment rate is estimated to grow by 35% in the period from 2022 to 2032 because data‬ scientists play an important role in massive data collection through their analytical and programming skills.‬ On the other hand, CDOs play an important role in managing data to ensure data quality. Thus, the future of Big Data‬ involves CDOs processing more efficient data.‬

‭Conclusion‬

‭Big data has become a very important part of today’s digital world. Big data is improving our daily lives in the‬ background, whether we’re watching films on YouTube or purchasing on Flipkart.. In this article, we have learned‬ everything about Big Data, like what Big Data is, how it is processed, what tools are used and some real-life examples.‬ We have also discussed the challenges that come with a large amount of data and also looked at future trends that are‬ making Big Data smarter and more efficient.‬