Eight most in-demand big data skills in 2023

The most in-demand big data skills

Whatever your role in a big data team, these are the must-have skills for the resumes of data engineers, data architects, data analysts, and more.

Programming languages

Core languages worth investing your time and money in include Python, Java, and C++.

Of course, it’s not necessary (or possible) to learn every programming language out there, but the more relevant ones you know, the better your career prospects get.

According to the Jefferson Frank Careers and Hiring Guide: AWS Edition, the top three programming languages expected to be most in-demand in 2023 are Python, Java, and JavaScript.

Cloud professionals also named C#,Golang (Go), Node, Linux and Terraform as some of the hottest languages to have in your toolbox this year.

Most open-source big data offerings are written in Java, so if you know that language well you’ll already have the kind of technical mindset necessary to tackle Hadoop For Spark.

Spark uses Scala because the language was created with functional programming and immutability in mind, making it compatible with behavior-oriented Spark APIs and its RDDs.

Python is hugely popular for text analytics and creates a solid foundation for big data support.

If you work with or develop big data platforms, you’ll no doubt be involved with scripting, so it’s useful to have at least one major scripting language in your back pocket, and Python is one of the strongest contenders.

Machine learning, AI, and Natural Language Processing (NLP)

The widening digital skills gap means that organizations all over the world are in a never-ending race to snap up big data professionals with machine learning, AI, and NLP skills—with the global machine learning market forecasted to hit $209 billion by 2029.

Neural networks, reinforcement learning, adversarial learning, decision trees, logistic regression, supervised machine learning, NLP mining technologies—the list goes on and on.

The data shows that the more you can offer, the more valuable an asset you’ll be to any progressive tech-focused employer today. For example, while a 2021 McKinsey report found that 56% of organizations have adopted AI in at least one function, just 12% agree that the supply of machine learning professionals is adequate. Meanwhile, in the UK, up to 10,000 jobs in AI and data science open each month.

This aligns with findings in our Careers and Hiring Guide, too, where AWS professionals report machine learning and AI as one the most common areas in which they lack training and development. Despite this, machine learning was one of the areas most in-demand, with 30% of partner clients on the hunt for professionals with machine learning skills.

Capitalize on high demand and become a must-interview candidate by developing these hotly-desired big data skills for your resume.

Quantitative analysis

Quantitative analysis is a huge part of day-to-day life in big data because it’s all about the numbers.

Having a background in mathematics—especially calculus and linear algebra—will give you a great foundation for understanding the probability, statistics, and algorithms that are fundamental to many big data skills. This is why, of those tech professionals who received a higher education, most come from STEM backgrounds. For example, our Careers and Hiring Guide found that of those that continued with further education and went on to undertake a degree, the top fields of study included engineering (14%), mathematics (11%), and economics (7%).

A strong background in math and statistics will put you ahead of the pack, and getting acquainted with powerful tools like SPSS and R will make you an even more attractive hire.

Data mining

Advances in tech have made data an invaluable asset, and taken data mining to staggering new heights as a result.

Big data pros with serious data mining experience are in high demand across the tech landscape, so invest some time in building out your data mining kit with industry favorites like Rapid Miner, KNIME, or Apache Mahout.

Problem-solving

Having a naturally analytical mind will take you a long way in this line of work. Whether you’re a naturally gifted analyst or not, it’ll take continuous practice to hone those skills and become a big data bigshot.

There are countless ways to sharpen your analytical thinking, and this don’t have to be technical based. Solving puzzles, playing chess, or enjoying videogames are all activities that challenge your problem-solving skills.

The key is consistency.

SQL and NoSQL databases

SQL forms the bedrock of the big data movement and is central to Hadoop Scala warehouses.

Distributed NoSQL databases like MongoDB are fast-replacing their more traditional SQL counterparts, including the likes of DB2 and Oracle, allowing for far more efficient storage and access capabilities.

NoSQL works in perfect harmony with Hadoop in terms of its data processing abilities, and having NoSQL skills gives you access to a whole range of job opportunities anywhere in the world. Simply put: don’t just get to know NoSQL databases, master them.

Data Structure and algorithms

These are fundamental skills that you’ll build your career on when it comes to big data or data science, so make sure you’ve got them polished to perfection as early in the game as possible.

By learning about data structures and algorithms you’ll become familiar with data types (stack, queues, and bags), sorting algorithms (quicksort, merge shot, heapsort), and data structures (binary search trees, red-back trees, hash tables)—essentially the bread and butter of any big data role today.

Even as a junior data scientist, you’ll need to know your way around unstructured data.

This is undefined content that doesn’t have a place in your database tables, for example, videos, blogs, customer feedback, audio, and social media posts.

Because this data isn’t streamlined, sorting it is notoriously complex, earning it the nickname ‘dark analytics’.

Unstructured data is pretty valuable because it reveals insights that can be vital to the decision-making process. Any data scientist worth their salt needs to be able not only to understand but manipulate this kind of data across various platforms.

Interpretation and data visualization

Without analyzing data and deriving insights, a business can’t function effectively. As a big data professional, it’s essential that you have a strong understanding of the business environment and domain your employer operates in.

The ability to visualize and interpret data is an essential big data skill that brings creativity and science together. Data visualization and analysis requires a lot of precise science and mathematics but also calls for inventiveness, imagination, and a natural curiosity.

The AWS Big Data certification at a glance

AWS Certified Big Data – Specialty is the certification to have if you’re a cloud professional looking to earn your big data stripes. The exam is designed to test your ability to carry out high-level analysis and confirms your capacity to use the provider’s core big data offering to design and maintain data structures, and employ a range of tools for data analysis automation.

The exam:

• Multiple choice, multiple answer
• 170 minutes
• $300 registration fee

Requirements:

• AWS Certified Cloud Practitioner or a current Associate-level certification: AWS Certified Solutions Architect (Associate), AWS Certified Developer (Associate) or AWS Certified SysOps Administrator (Associate) recommended
• At least five years’ experience in data analytics
• Experience using AWS Big Data services and detailed knowledge of their place across the data life cycle
• Experience designing scalable, cost-effective architecture for data processing

What’s the average salary for AWS Big Data jobs?

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