Big Data Vs Big Data Analytics Vs Data Science

Understand the significant differences between Big Data vs Big Data Analytics vs Data Science through this tutorial:

Terms such as big data analytics, big data, and data scientist are trendy these days. These new fields are generating enough interest among engineers and IT professionals while choosing to make one’s career choices.

This tutorial distinguishes the differences between these buzzwords and helps in clarifying the confusion.

Big Data vs Big Data Analytics vs Data Science

Introduction To Big Data, Big Data Analytics, And Data Science

Let us see what each of the terms mean.

What Is Big Data

Big data relates to the large data sets, which are created from a variety of sources and with a lot of speed (a. k. a velocity). Any data set that has one of the attributes can be called Big Data. It is also about the data with veracity and value.

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Big Data is used for the analysis of insights that will help you with the business moves. Some real-world examples that will explain how big data is used are as follows:

  1. Big Data is used to find out consumer shopping habits.
  2. It can be used to monitor health conditions through data from wearables.
  3. The transportation industry uses fuel optimization tools where big data is used.
  4. It is used for predictive inventory ordering.
  5. It can help you with real-time data monitoring and cybersecurity protocols.

What Is Big Data Analytics

Big data analytics is the use of specialized software or platforms to draw conclusions or to find answers to specific questions based on correlations or relationships between data sets from different systems.

It helps businesses or organizations to discover patterns and derive insights by leveraging the strengths of IT, marketing, and data science skills.

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What Is Data Science

Data science is about extracting information and insights from structured or unstructured data based on interdisciplinary knowledge in mathematics, statistics, computer science, and machine learning.

What is Data Science

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Some real-life examples where Data Science is used are:

  1. Data Science makes a great help with identifying and predicting disease.
  2. It can be used with personalized healthcare recommendations.
  3. It makes a great help to optimize the shipping routes in real-time.
  4. Data Science can be used to automate digital ad placement.


We will take an example of the banking industry to explain the roles of Big Data professional, Data Scientist, and Data Analyst:

Data Science will help the banking industry with

  • Fraud detection and prevention
  • Risk management
  • Customer data analysis
  • Marketing and sales
  • And AI-driven Chatbots & virtual assistants.

Big Data will help the banking industry with:

  • To provide personalized banking solutions to their customers.
  • Boosting performance.
  • Performing effective customer feedback analysis
  • And with effective risk management.

Data Analysts will analyze work systems, information, procedures, and documents of the bank. Bank Data Analysts will assess the financial and management aspects of the bank and hence the cost & time can be determined for each function. This role also has the responsibility of reviewing a monthly audit of cost savings.

Big Data Vs Big Data Analytics Vs Data Science: Tabular Comparison

BasisBig DataBig Data AnalyticsData Science
DefinitionData generated from variety of sources in huge volume with high velocity is Big DataUse of specialized software and tools to analyze big data to conclude decision makingUse of multi disciplines to extract information and interpretable insights from structured and unstructured data
Skill setsSet up/Maintain Infrastructure
Data preprocessing
API development
Data pipeline implementation and monitoring
Analyze Data
Data Modeling
Data Engineering
Business Analytics
Business Intelligence
Machine Learning
Exploratory Analysis
Data Mining
Modeling Visualisation
Software Development
Application AreasFinancial Services, Retail, Health & Sports, performance optimization, Communication, etc.Healthcare, Travel, Gaming, Energy Management, etc. Internet Searches, Search Recommenders, Digital Advertisements, Image/Speech recognition, Fraud & risk detection, etc.
ToolsKafka & Storm
Cloud Computing
KNIME, SAS, Python
Power BI/Tableau
Apache Spark/Hive
Neural Networks frameworks
R or Python
Annual Salary range (USD)95000 to 16500075000 to 130000100000 to 185000

Roles Available

The roles available for Data Analyst can be Database Administrators, Operations, Data Architects, and Data Analysts. Data scientists can work as Data creatives, Data Developers, Data researchers, and some other roles as per their skill set.

There are various roles available in Big Data such as Data Analyst, Big Data Visualizer, Big Data Engineer, Data Strategist, etc.

Required Skill-Set

Let go through the skill-sets required to become a Data Scientist, Big Data professional, and Data Analyst

#1) Data Scientist

A data scientist requires the following skills:

  • Design, develop and analyze machine learning models.
  • Perform exploratory analysis to uncover missing and incorrect data.
  • Manipulate data and draw insights using a programming language such as R or Python.
  • Develop, train, and improve the performance of a neural network model.
  • Use distributed computing tools such as Apache Spark, Hive, etc.
  • Use Microsoft Excel, PowerBI, and Tableau for data visualization and presentation of the results of the assigned problem statement.
  • Collaborate with Data Analysts and Business Analysts by delivering deployable and learned machine learning models.
  • Apply knowledge of programming and data structure with general software development skills such as source code management, debugging, testing, etc.

#2) Big Data Engineer

A big data engineer requires the following skills:

  • Set up and maintain big data infrastructure and pipelines in and out of databases/data lakes, IoT devices, and data warehouses.
  • Clean up or pre-process data for machine learning or various types of analytics.
  • Monitor data infrastructure or systems in a pipeline with the help of scripting, programming, software, or platforms.
  • Use big data processing frameworks such as Hadoop & Spark.
  • Use streaming platforms such as Kafka & Storm.
  • Use cloud platforms such as AWS, GCP, Azure, etc.
  • Apply knowledge of programming, SQL, and scripting to automate multiple tasks and develop API to save effort and time.
  • Collaborate with business users and other stakeholders to understand data requirements.

#3) Data Analyst

A data analyst is required to have the following skills:

  • Analyze, model, and interpret data.
  • Use tools and technologies such as Spreadsheets, RapidMiner, KNIME, SAS, Python, Jupyter, SQL.
  • Perform feature selection and engineering.
  • Articulate conclusions to business, and stakeholders using data visualization tools.
  • Analyze data for accuracy and replace missing values.
  • Collaborate with data scientists and data engineers.

Salaries Of Data Scientist, Big Data Professional, And Data Analyst

Salaries of professionals called data scientists, big data engineers, and data analysts are given below.

#1) Data Scientist Salary

Data science is one of the hottest jobs of this century. Median annual salaries of a data scientist in the US for different experience level are:

  • Freshers or Early Career: 100000 USD
  • Mid-level data scientist: 140, 000 USD
  • Data scientist in Managerial Role: Above 185000 USD

Learn more =>> Differences between Data Analyst vs Data Scientist

#2) Big Data Engineer Salary

Big Data Engineers are among the top five highest-paid engineers in the USA. The average annual salary of a big data engineer on different experience levels is given below:

  • New Data Engineers: 95000 USD
  • Senior Data Engineers: 135000 USD
  • Data Engineers with Management Role: Above 160000 USD

#3) Data Analyst Salary

Data analyst’s contributions are very important towards organizational goals and therefore are highly paid.  The average annual salary of data analysts are given below:

  • Early career data analysts: 70000 USD
  • Mid-level data analysts: 110000 USD
  • Managers: 130000 USD

Frequently Asked Questions

Q #1) Does Big Data come under Data Science?

Answer: Data science is not limited to any particular type of data. It is a term that encompasses other disciplines such as mathematics, statistics, and machine learning & AI. Any data can be part of data science projects. Therefore, big data is in a way a smaller subset and within Data Science.

Q #2) Big Data Vs Data Science, which is better as a career?

Answer: Both Big Data and Data Science are good career options and are fulfilling. Big Data requires the use of specialized tools and technologies and an engineer needs to have skills similar to system administrators or DevOps engineers. Data Science requires one to have knowledge of multiple subjects along with methods of training, evaluating, and improving machine learning models.

Both big data and data science are important for taking out actionable insights from data.

Q #3) Is Tableau good for Big Data Analytics?

Answer: Tableau is good for analytics. It is an end to end platform for big data analytics. It works as a self-service portal and can be used to analyze big data in a governed environment. Users can make use of Tableau workbooks and can share insights with the help of dashboards with other users.

Q #4) Does Big Data require coding?

Answer: Yes, mastering the skills in big data requires coding; however, the level of knowledge required for coding is not as deep as that of a programmer. Data Analysts use coding to enhance and customize existing reports in the software and the tools that they use. Big data engineers use coding to automate various tasks or to integrate multiple tools.

Data scientists use coding to create machine learning models in tools such as R Studio or Jupyter.

Q #5) Is Big Data difficult to learn?

Answer: It is relatively easy to learn big data. The reputed technologies used for big data are open source. Moreover, it requires engineers to collaborate and exchange knowledge in an interpretable and transparent way, and open-source support the same. The efficient use of methods and tools requires one to practice as much as possible to gain mastery.


This tutorial explains the difference between big data vs data science vs big data analytics and compares all three terms in a tabular format. Moreover, the work roles of a data scientist, data analyst, and big data engineer are explained with a brief glimpse of their annual average salaries in the USA.