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An Easy Guide to Understanding Big Data with Research Topics

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Today, almost the entire world is using Amazon. Every day millions of users buy something or the other from Amazon. Now imagine Amazon to be like one of those traditional general stores that sell everything because it is a traditional system. The owner of Amazon has to talk to each one of its customers and understand their needs to come up with good marketing strategies for the target audience. But in this case, they also have to cater to the million different needs of the million customers. Now, that would be an impossible task to achieve using the traditional marketing system. But, with digital marketing taking over, things have gotten easier.

Now, Amazon can easily go through its individual customer needs, define the pattern and come up with marketing strategies that cater to individual customers in a very one-to-one way. But are digital marketing techniques enough for such efficient work? No. Data plays a huge role in making digital marketing efficiently working.

Establishments like Amazon need a lot of information about a huge number of people. The data is referred to as big data. And the fun fact is that big data keeps growing with time. This generates more scope for research in the same field. But before you do one, it is better you brush up on your idea on big data.

Write My Research Paper on Big Data: This blog intends to do the same. A quick brief on big data will tell you about the most relevant research topics in the field. Let’s start with the definition.

What is Big Data?

 Big data is defined as more varied data, arriving at a faster rate and in larger volumes.

Simply put, big data is an accumulation of information that is extremely large in volume and is continuously expanding exponentially. No typical data management systems can effectively store or process this data because of its magnitude and complexity.

But this information is not enough to understand big data. You need to understand the three V’s to have a more detailed understanding.

The three V’s of big data are –

Volume –

For some organisations, this may be tens of terabytes of data. While for others, it may be hundreds of petabytes. With big data, you must handle high volumes of low-density, unstructured data, like information from Twitter feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment.

Velocity –

Velocity is the rapid rate at which information gets received and acted on. Normally, the fastest velocity of data streams straight into memory versus being recorded to disk. Certain internet-enabled smarter products function within or close to real-time and will demand real-time evaluation and response.

Variety –

Variety alludes to the wide range of data types that are accessible. In a relational database, traditional data kinds were organised and easily suited. Data is currently available in fresh unorganised formats thanks to the growth of big data. Text, audio, and video are examples of semi-structured and unstructured data types that require further pre-processing in order to create meaning and enable metadata.

This brings us to the next point of discussion, the types of big data.


Big data can be classified into three different types. They are –


Structured Data

‘Structured’ data refers to any data that can be retrieved, analysed and preserved in a fixed way. Over time, computer science expertise has grown more successful in creating methods for handling this type of data. But honestly, this is a bit problematic when the data gets bigger.

Here is an example of structured data.

Employee IDEmployee NameGenderDepartmentSalary In Lac
1234Ron WeasleyMaleMarketing650000
300Hermione GrangerFemaleFinance650000
400Harry PotterMaleAdmin500000
500Draco MalfoyMaleAdmin500000
600Luna LovegoodFemaleFinance550000

Unstructured Data

Unstructured data is any data whose shape or organisation is unknown. Unstructured data is enormous in quantity and presents a number of processing obstacles that must be overcome in order to extract value from it.

Google search outputs are an example of unstructured data.

Semi-Structured Data

Both types of data can be found in semi-structured data. Semi-structured data can appear to be structured, but it is not specified by a relational DBMS’s concept of a table, for example. An XML file containing data is an example of semi-structured data.

How Does Big Data Work?

 Big data analytics works in 4 steps

Collect Data

Every organisation has a different approach to data collection. Thanks to modern technologies, organisations can now collect data from various structured and unstructured sources.

Process Data

For analytical queries to yield reliable results, data must be appropriately organised after it has been gathered and stored, especially if the data is huge and unstructured. Data processing is becoming more difficult for organisations as data availability increases dramatically. Batch processing is one processing choice, and stream processing is the other.

Clean Data

All data, regardless of size, must be scrubbed to increase data quality and produce more robust results. Duplicate or unnecessary data must be removed or accounted for, and all data must be structured correctly.

Analyse Data

It takes time to transform huge amounts of data into a usable form. Advanced analytics techniques can transform huge data into significant insights once they are ready. Data mining, deep learning and productive analysis are a few methods of data analysis.

Benefits of Big Data

  • In order to modernise their obsolete mainframes by determining the underlying causes of errors and problems in real-time and out-dated code bases, many IT organisations are wholly dependent on big data. Open-source systems like Hadoop are replacing the traditional systems used by many organisations.
  • With the aid of big data technologies and the capacity to run different algorithms quicker, the data can be appraised frequently throughout the day. Unfinished or erroneous limits on credit or pricing information can result in a loss of overall customer service, revenue reduction, or service costs.
  • Big data can assist businesses in acting more quickly, enabling them to shift more quickly than their rivals.
  • Financial services companies are slicing and dicing their users into carefully calibrated categories using big data to mine data about customer interactions, which will help brands in creating more sophisticated and relevant offerings.
  • To identify patients who are most likely to request a second admission within a short period of discharge, hospitals analyse medical data and records. The hospital can avoid patients’ expensive hospital stays.

Not just this, there are many other benefits of big data as well, and now that the data is growing exponentially, the benefits are also walking on the higher side.

What are the Challenges of Big Data?

There are 6 challenges that come along with big data. They are –

No Proper Understanding –

Companies’ Big Data projects fail because of a lack of knowledge. All staff might not be familiar with the definition, sources, processing, and storage of data. Data experts could understand what is happening, but others might not.

Data Growing Rapidly –

The proper storage of all these enormous volumes of data is one of the biggest problems with big data. Companies’ data centres and databases are storing an ever-growing volume of data. Managing big data sets as they increase rapidly over time becomes increasingly challenging.

Confusion With Tool Selection –

When choosing the appropriate technology for Big Data analysis and storage, companies frequently experience confusion. And sometimes, because of not having proper knowledge, they end up choosing something not very efficient. This then messes up the entire project.

Lack of Data Professionals –

Companies want knowledgeable data specialists to manage these cutting-edge technologies, including Big Data solutions. These experts, who are skilled in using the tools and making sense of huge data sets, will consist of data scientists, data analysts, and data engineers.

Data Security –

One of the major difficulties of big data is keeping these enormous sets of data secure. Companies frequently put data security to later stages because they are so busy understanding, storing, and analysing their data sets. However, this is a bad idea because unprotected data repositories might serve as a haven for malevolent hackers.

 Integration of Data –

Now that you have a crystal clear idea of big data, it is time you look for some topics for your research writing study on big data. Only one rule is applicable here; you have to choose as per the relevance and its importance in the modern time.

For your reference, there are a few topics listed below. Have a look.


 40 Big Data Research Topics

Data Mining Research Topics for Students

  1. Spectral clustering in parallel inside a distributed system
  2. How can linear and nonlinear regression analyses’ efficacy be increased?
  3. Give a description of asymmetric spectral clustering.
  4. Describe Association Rule Learning in the Context of data mining.
  5. Information-based clustering: What is it?
  6. Talk about the MATLAB spectral clustering package.
  7. Describe how dependency modelling performs.
  8. Spectral clustering with self-rotation
  9. Talk about how well representative-based clustering performs.
  10. Discuss data clustering K-Means techniques.

Basic Big Data Research Topics

  1. Asymmetric spectral clustering should be explained.
  2. Big data analytics and its clients.
  3. The Internet of Things.
  4. Give a definition of distributed semantic analysis.
  5. Describe how artificial intelligence is relevant now.
  6. How to answer questions in a semantic way.
  7. What is the procedure for graph analytics?
  8. Establish what organised machine learning is.
  9. Relevance of agile data science.
  10. What use does augmented reality serve?

Data Management Project Topics

  1. Discuss how a business is affected by data quality.
  2. How to ensure correct management results in efficient data protection?
  3. How to use data management to advance scientific outreach and research
  4. Market analysis, reference model, and data catalogue
  5. Describe the most effective data management techniques for current businesses.
  6. AI and new technologies for data management
  7. How to gather and manage external data
  8. Why is data retention crucial, and what does it entail?
  9. What is data valuation? Why is data valuation important in data management?
  10. Describe how the fundamentals of data management are used.

 Unique Big Data Research Topics

  1. Data Fabric technology advancement
  2. Uncertainty in the management of massive data
  3. Evolution of Vector Similarity Search
  4. How much external data be sourced and managed?
  5. How can stream data be processed in big data?
  6. A functional understanding of the big data ecosystem
  7. Hadoop programming and the Map-Reduce architecture
  8. How can big data improve organisational operations and its ability to compete in the current market?
  9. Big data analytics and business intelligence
  10. Research that is reproducible: Data management and analysis.

 Trending Big Data Research Topic

  1. Deploying Spark clusters automatically
  2. Utilising big data to identify market supply conditions
  3. Using big data to enhance an organization’s supply chain management
  4. MapReduce as a big data architect
  5. Discuss the best big data tools available.
  6. Explain the usefulness of probabilistic classification in data mining.
  7. Describe the implementation procedure for attribute-access control and role-based access control.
  8. Determine the method or algorithm used to authenticate users and data owners.
  9. Examine how subject-oriented data mining can be used to reduce terrorism.
  10. Data streaming and big data- What is the process?

Next up, we have addressed the common queries students have about big data. Here you go!

Most Popular Questions Searched By Students:

What are the issues of big data research?

Answer: Big data research has the following problems –

  • Storage
  • Processing
  • Finding and fixing problems with data quality
  • Big Data System Scaling
  • Making a decision on and evaluating big data technologies
  • Environments for Big Data
  • Instantaneous Insights

What are the most important research topics in the Big Data field?

Answer: Here are a few important big data research topics.

  • Educating oneself on data mining
  • List some of the most innovative concepts in big data management.
  • How to stop unauthorised data access
  • How to create role-based access control or attribute access

What are the major problems big-data applications solve?

Answer: Problems that big data can solve are –

  • Predictive analysis
  • Streamlining of business processes
  • Better ways of market research
  • Centralization of data access

What is the most significant problem/challenge faced by the age of big data?

Answer: Lack of knowledge is the most significant problem or challenge, as it may be referred to in the age of big data.

What are the three Vs of Big data ?

Answer: The three Vs of Big data are –




How is Hadoop related to big data?

Answer: Hadoop is basically a framework or rather say open source frame work that allows storing and analysing not just unstructured but as well as complex data.

What are some of the data management tools used with Edge Nodes in Hadoop?

Answer: Oozie, Ambari, Pig and Flume are the most common data management tools that work with Edge Nodes in Hadoop.

Name a few big data processing techniques. 

Answer: Big Data Processing Techniques include

  • Big Data Stream Processing 
  • Big Data Batch Processing
  • Real-Time Big Data Processing

Hi, I am Mark, a Literature writer by profession. Fueled by a lifelong passion for Literature, story, and creative expression, I went on to get a PhD in creative writing. Over all these years, my passion has helped me manage a publication of my write ups in prominent websites and e-magazines. I have also been working part-time as a writing expert for for 5+ years now. It’s fun to guide students on academic write ups and bag those top grades like a pro. Apart from my professional life, I am a big-time foodie and travel enthusiast in my personal life. So, when I am not working, I am probably travelling places to try regional delicacies and sharing my experiences with people through my blog. 

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