Big Data

Top 12 Big Data Skills You Must Have In 2024 and Beyond

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With the exponential data added daily into the warehouses, conventional analysis procedures are insufficient. The value from the data can be gained only when you know how to use it. Big data is characterized by its features like volume, variety, velocity, value, and veracity.

This diverse data can be processed by experts knowing the field. There are many skills required for big data analytics, which the industry is looking for today. Big data requires a big skill set. Let’s look at some top big data skills in demand for big data developers.  

What is Big Data?

Big data comprises structured, semi-structured, and unstructured data that organizations gather, allowing for information mining and its application in machine learning projects, predictive modeling, and other sophisticated analytics undertakings.

Within organizational data management architectures, big data processing and storage systems have become standard elements, often accompanied by tools that facilitate big data analytics applications.

Also read: Understanding Structured Data in Big Data

Why is big data important?

Businesses leverage big data within their systems to enhance operations, deliver enhanced customer service, design personalized marketing initiatives, and undertake other strategic measures that can ultimately bolster their revenue and profits.

Companies that effectively harness big data hold a potential competitive edge over their counterparts who fail to do so, as they gain the ability to make quicker and more well-informed business decisions.

For instance, big data furnishes valuable insights about customers, which companies can utilize to refine their marketing, advertising, and promotional strategies, resulting in improved customer engagement and higher conversion rates.

By analyzing both historical and real-time data, businesses can gauge the changing preferences of consumers or corporate buyers, thereby becoming more adaptive and responsive to customer desires and requirements.

Here are additional instances of organizations utilizing big data:

  1. In the energy sector, big data assists oil and gas companies identify promising drilling sites and monitor pipeline activities. Similarly, utilities employ it to monitor and optimize electrical grids.
  2. Financial services firms employ big data systems to manage risk and analyze real-time market data.
  3. Governments also utilize big data for emergency response, crime prevention, and implementing smart city initiatives.

Top 12 Big Data Skills to Learn

big data skills

As the field of technology and data science constantly evolves, the skills required for big data analytics may continue to expand.  Let’s look at the 12 most important skills a big data engineer must know to give a boost to their career:

1. Analytical Skills

Are you an individual having the curiosity to find the hidden patterns in the available data? Or you analyze the data to derive some valuable information from it. If you keenly observe the facts and figures, you have checked one of the big data requirements.

Analytical skills are among the very vital big data developer skills. They are a set of problem-solving skills that allow you to parse datasets and information to derive unique solutions to problems.

Critical analysis helps identify seasonal trends, customer behavior, probable downfalls, and forecasting sale models to ensure the smooth functioning of the organization.

Also read: A Beginners’ Guide to Data Analytics in IoT

Big data analytics allows big data developers to dig out the data for hidden patterns, numbers, facts, and information that can generate valuable insights.

Recruiters look for experts with sound analytical skills who can observe complex problems with different perceptions and build an understandable theory from it.

An inquisitive big data professional adds to the organization’s productivity by using their research, logic, and observations. The analytics procedures include the multi-channel communication approach, reflective analysis, creating models, making charts and graphs, and drafting reports. 

Also read: Top Free and Paid Data Analytics Tools

2. Data Visualization Skills

data visualization skills

Not everyone can understand the hidden patterns in the data. The actionable insights must be converted into clear formats that tech and non-tech people can use in the business. Data visualization is a big data skill wherein the data and information is represented pictorially or graphically.

To become an industry-ready professional, one must learn data visualization tools. Industries hire professionals who can tell stories from the information obtained from the datasets.

The data visualization process highlights the data trends and removes the outliers to make the data presentable and precise.

 Data visualization is a prerequisite for big data experts. 

It should be concise and deliver the necessary information without any confusion. Charts, tables, graphs, infographics, dashboards, and geospatial data are types of data visualizations.

Also read: The Science of Storytelling: Data Visualization

The big data industry uses numerous tools to create these visualizations for large datasets. These tools make the visualization job easier for the designer by including millions of data points and automating the visualization process.

If you need to stand out from the bundle of resumes, just learn to use the following data visualization tools.

  • Tableau
  • ChartBlocks
  • Infogram
  • Datawrapper
  • D3.js
  • Google Charts
  • Grafana
  • Polymaps
  • Looker
  • Qlik Sense
  • Microsoft Power BI
  • Zoho

3. Understand Business Domains and Big Data Tools

A skilled professional is one who knows the data and the field well. Domain familiarity helps you sustain yourself in the field. If you aim to start a business, you must do background research. Knowing the market segment, potential customer base, and events is essential.

This knowledge strongly supports the business by developing insights, finding patterns, and observing loopholes keenly. Professionals with domain expertise put forward innovative solutions for product development and customer acquisition. Businesses look for a workforce who can add value to their models.

The vastness of big data requires tools and techniques to process it. Big data tools are software that put the raw data into algorithms to build valuable insights from the complex data sea. Companies hire professionals who have hands-on experience with big data tools.

Also read: What is Big Data Architecture: Types, Tools, and More

Learning big data tools can help you land high-profile job roles like big data engineer and big data developer. The market is full of a variety of big data tools. Your selection depends on features like business objectives, dataset size, analysis type, tool pricing, ease of use, and security. The most used big data tools are mentioned below.

  • Apache Hadoop
  • OPenrefine
  • Apache Spark
  • MongoDB
  • Tableau
  • Hive
  • RapidMiner
  • Lumify
  • Apache Storm 
  • Cassandra
  • Adverity
  • Pentaho
  • Cloudera

4. Predictive analytics

It is a branch of advanced analytics that answers a crucial business question: “What might happen in the future?” The analytics procedure yields predictions regarding business outlook using historical data.

Employing the approaches of statistical modeling, data mining, and machine learning, analytics models assess historical data to unveil patterns, monitor trends, and exploit this information to forecast upcoming business trends. Industries today are flooded with voluminous data from log files, images, and video.

This data exists in distinct data repositories. To make it functional, an individual must have predictive analytical skills.

Algorithms and statistical techniques, including logistic and linear regression models, clustering, classification models, neural networks, time series models, and decision trees, are used for the process. 

Predictive analysis is a crucial big data skill; mastering this is essential for generating intellectual input for business modeling. Companies are highly professional with predictive analytics skills to model the business features, generate actionable insights, set operational priority, fraud detection, cost optimization, and risk analysis.

Also read: Predictive Analytics – A Customer-centric Approach

Some important tools which you can learn for learning predictive analysis are

  • Orange
  • Anaconda
  • IBM Watson
  • Altair

5. Skills of Programming

Programs are lines of code that execute complex tasks to generate output.  To qualify for a big data developer position, you must know how to program models. Big data is an emerging field. Thus, the skill set and demands of big data are evolving continually.

Learning programming languages for big data comprehends how technical experts utilize it to retrieve, arrange, store, and update huge amounts of data in warehouses. One must be prepared with fundamental and developed programming languages per the company’s requirements.

programming skills

With strong programming knowledge, you can build sound backend engineering supporting the business in critical times. Various programming languages can be used according to the development procedures. The features of each vary with respect to interfaces, ease, security, and cost factors. Most used big data programming languages are mentioned below.

  • Python
  • Scala
  • R programming
  • Java
  • SQL
  • Julia
  • Go
  • C++

6. Problem-Solving Skills

Big data accompanies multiple problems due to its volume and hybrid nature. To work on the data, debugging the problems is crucial. Working with data requires patience to identify and solve the issues within.

It takes effort to understand the problem and find what to do to cure it. If you know how to deal with complex situations for the smooth functioning of data, you have the skill of a big data expert.

Machine learning requires continual problem identification and solution search. Being curious about data and its consequences makes it easier to simplify data analytics.  Proactive problem-solving skill helps in identifying opportunities, developing a theory, and resource management.

Your skills bring you answers to the unanswered questions. A problem solver searches for problems in data and pivots to a better implementation. However, there is no ideal way to solve the problems but one general way mentioned below to encounter the challenges in big data.

  • Understand your problem
  • Modularize the problems for better problem solving
  • Categorise part of problems that can be solved by own
  • Search for solutions by using documents and manuals
  • Debug using small samples
  • Consider the error messages in detail
  • Communicate with the team for solutions

7. Quantitative analysis

Analytics is incomplete without maths and statistics. Resting its base on the shoulders of traditional quantitative theories, big data processing needs knowledge of statistics and linear algebra.

For effective and exploratory data analysis and identifying important trends and relationships, you need to know the core concepts of statics, random variables, probability distribution, linear algebra, regression analysis, calculus, association, and hypothesis testing. Meticulous statistical thinking is an important skill in mining signal data from noise. Plus, linear algebra creates algorithms for big data analytics tasks.

Also read:

The job responsibilities of a big data engineer include working with algorithms, software, and mathematical tools. The quantitative analysis is of two types descriptive and inferential. Descriptive techniques like standard deviation, mean, median, mode, and skewness explain the rationale behind the numbers in big data.

While inferential quantitative analysis techniques like Monte Carlo simulation, Analysis of Variance, Cross Tabulations, and Factor Analysis utilize the analyzed data to make predictions and emphasize possible outcomes. 

Also read: Descriptive vs. Inferential Statistics

If you aim to be a big data analytics professional, some tools you can master are the names below.

  • SPSS
  • JMP
  • Stata
  • SAS
  • R
  • Excel

8. Machine learning

Businesses embrace machine learning algorithms to store, process, and mine valuable insights from the huge volume of data. They hire experts to overcome the challenge of handling the collected data and using them efficiently. These experts Understand, Train and deploy models to find and implement innovative AI-based solutions for complex business issues.

Embedding ML for big data analytics has proved to be a rational shift for businesses to take full advantage of the potential of big data. Hiring machine learning experts speeds up the decision-making of the organization. Businesses invest a huge amount in tools and technologies of ML to work intelligently on big data.

Also read: Top Machine Learning Trends to Follow

They give lucrative paychecks to people having a grip over these technologies and tools. The professionals categorize the incoming data chunks, identify patterns, and transform the data into information useful for business operations.

The role of machine learning in big data encapsulates data labeling and segmentation, data analytics, case simulation, market research, exploring customer behavior, modeling, and trend forecasting.

An ML expert must know how to apply supervised and unsupervised algorithms to accurately build buyer models and behaviors.  Adding machine learning and its algorithms to your skill set can open numerous opportunities for data analytics. 

Also read: Why Should You Learn Machine Learning

9. Structured Query Language

A data analyst should possess the power to interact directly with databases. The most used database querying language, SQL, is used for the task. With all its varieties, SQL is used as a foundation to build user-friendly dashboards and reporting tools. Using SQL, intuitive dashboards are created to curate reports and build insights from them.

SQL as a skill helps in integrating the databases directly into the big data and deep learning frameworks. This adds up to the system’s functionality without building the complete structures from scratch.

SQL is an intermediary linking complex data storage systems and their end users, making it more accessible to experts and data scientists. SQL helps in making completely supervised solutions for effective data integration to automate your data flow

 Knowing SQL as a skill for big data allows you to:

  • Integrate data
  • Form databases
  • Data cleaning and wrangling
  • Report generation
  • Write queries
  • Resolve data issues

10. Skills in Data Mining

To drill out big data, you need to master data mining. One has to be acquainted with the techniques and algorithms of data mining to process and comprehend the data in large datasets. Companies welcome people with experience in data mining tools and technologies with high salary perks. To land a good job in big data, you can learn some data mining tools like RapidMiner, KNIME, and Apache Mahout.

Also read: Data Reduction in Data Mining

11. Familiarity with technologies

Technology is used to process and handle big data capabilities. To become a big data professional, one must be familiar with various tools available in the market. The previous section of this article mentioned various tools for data analytics, visualization, and big data tools.

The selection of the tool is industry specific. One can learn and use tools Depending on the business objectives and cost constraints. Enthusiasts having statistical, programming, and analytical knowledge have a high chance of selection. 

Also read: Big Data Technologies

12. Familiarity with Public Cloud and Hybrid Clouds

Big data analytics has received new heights by entering into the cloud. Cloud computing provides exceptional scalability and flexibility, encouraging big data capabilities in business. Hence having cloud-based big data analysis knowledge comes under important big data developer skills.

Experts working on cloud delivery models offer cost-effective big data modeling. Public, private, and hybrid cloud selection depends upon personal and organizational requirements.

As a Cloud expert, you must manage structured and unstructured data and verify its authenticity. The tasks of analytics like data integration, analysis, modification, reporting, and visualization are done with the help of cloud service providers under Analysis as a Service (AaaS). 

Data scientists and big data engineers use cloud-based tools for model management and data pipelines. These tools help create, experiment, and publish models to connect to data pipelines and evaluate performance. 


Big data analytics has hidden capabilities that can be unleashed by gaining in-depth knowledge about the field. Technologies and tools available in the market cover different aspects and tasks of big data analytics.

Big data analytics is an interesting and evolving field, thus guaranteeing success. But entering raw can be dangerous. You can develop the skills required for big data analytics depending on your interests, experience, money, and personal and organizational goals.


  •  What are the 3 types of big data?

Big data is classified based on its organization and format. Three main types of big data are structured, semi-structured, and unstructured. Structured data has a predefined format and is well organized, while unstructured data exhibit an unknown format containing text, audio, and video files. Semi-structured is a combination of both. 

  • What are the skills of a big data team?

An efficient big data skill set contains technical, non-technical, and business skills.  Technical skills include computation framework knowledge, data mining, machine learning algorithms, statistics, maths, programming fundamentals, database handling, data visualization, and working on big data development tools.

Big data teams must have business knowledge and skills like domain expertise, project management, change management, communication, leadership, and teamwork. 

  • How can I learn big data skills?

You can attain the Big data skill set from various sources like the internet, books, and paid and free courses. All you need to do is learn from the courses, apply for internships or jobs, and work on projects to gain experience and master the skills. Certifications in top skills in demand can make you stand out in the crowd.

M.E. in AI | Technical Content Writer Research and Write audience centric content.

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