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.
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