8 Must-Have Skills to Become a Data Engineer

8 Must-Have Skills to Become a Data Engineer

If you aspire to be a data engineer and don’t know where to start or which skills to acquire, you’ve come to the right place. IT professionals over the last decade have increased significantly, and Data Engineering is amongst the fastest-growing fields with tremendous job opportunities. Since Data Engineering has an element of software engineering and data science, data engineers have increased exponentially worldwide across different industries. To be a Data Engineer you need to take on some projects and solve some problems, you can take all of that by visiting ProjectPro Data Engineering Projects.

Big players like Google, Meta, Microsoft, Amazon, Twitter, Instagram, YouTube, Zomato, and many others are generating raw data. This data needs to be organized, stored, and presented in more efficient ways for businesses to turn out more profitable, and that is when the big data concept was in demand. Data engineers do this job and are competent and qualified to store, process, and make this data usable for other organization members.

If you think you must master all the programming languages and acquire all the skill sets that influential programmers have, that’s not the case. You need to ease your way and focus on the below skills and programming languages you need to compete as a data engineer. Data engineers use programming languages such as Python, Scala, Java, SQL, Bash, Ruby, and many more. However, we’ll look at Python and Scala as they have been in demand.

1) Employable Programming Languages – Python and Scala

Is it mandatory to be the best and know all programming languages? Not. As we all know, the list of programming languages is ponderous. However, we shall talk about Python and Scala, which have been in demand over the last few years with the development of AI and Robotics.

  • Python: Python perhaps is one of the most common and easiest languages to learn and implement. Python is widely used in areas like Statistical Analysis and Modelling, while Java finds extensive applications in data architecture frameworks because most APIs are designed for Java. Python is also considered to be handy because of its rich library. Python, the default airflow language, can perform web-scraping, machine learning tasks, and pre-process big data using Spark.

  • Scala: Scala, also known as scalable language, is one of the developers’ community’s most trending and likable programming languages. To achieve better productivity, programmers need a flexible language, and Scala is the answer. Scala consists of REPL, which stands for reading, Evaluate, Print, and Loop, is a shell that allows a developer to do some interactive analysis and acts as a domain to write some of your programs, test them, and see if they work as, you expected them to work. Father of Java programming language, James Gosling, once said, “If I were to pick a language to use today other than Java, it would be Scala. This represents the acceptance Scala has in the programming industry.
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2) ETL Tools

ETL tools are essential for any data-driven business. Data in its raw form is quite complicated; developers use ETL tools to make it usable. ETL stands for Extract, Transform, Load. ETL tools enable business intelligence to access data from different sources, modify it and store it so that application developers can access this data and build applications. ETL tools are time-efficient and save plenty of time importing complex data from different sources. ETL tools have reduced the probability of errors while performing automated tasks. 

Developers use different ETL tools: Cloud ETL Tools, On-premise ETL Tools, Batch ETL Tools, and Real-Time ETL Tools. Common use cases of the 4 ETL tools would be seen in Data Warehousing, Migration, ELT, or Pushdown Optimization. As a developer, key features in an ETL tool would be real-time data access, an easy-to-use interface, connectivity, in-built monitoring, reduced error probability, and scalability

Assuming we now understand the basics of ETL tools and what they mean, it is also essential to realize that ETL tools help maximize big data use-case. Unlike the traditional methods of coding data into pipelines, ETL tools have graphical interfaces, which makes them easier and faster.

3) Data Warehousing 

Data warehousing is the process of building and using a data warehouse. Most companies hugely rely on data warehousing for analytical decisions as data is collected from heterogeneous sources. Data extraction, data Consolidation, data cleaning, and data integration are the main steps of data warehousing. The significant advantage of data warehousing is that the processing of a query does not require an interface when data is processed at a local source. Transformation of data and cleaning the data are vital in enhancing mining results. Data warehousing is a skill that would fit many industries and has the potential to grow exponentially in the coming years.

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4) Machine Learning

Tech companies have invested enormous amounts of money and time in Machine Learning. Machine Learning requires an excessive number of skills and knowledge across different domains. However, we’ll look at some of the experts who have adopted.

  • Computer Science and Programming Languages 
  • Data Modelling and Architecture
  • Spark and Hadoop 
  • Applied Mathematics and Neural Network

The demand for Machine Learning is amplifying, and you must have this skill and be a job-ready candidate to boost your career to new heights

5) SQL

As a data engineer, SQL is a programming language you need daily. Structured Query Languages was listed as one of the leading technologies and skills as a data engineer in job listings. SQL is a programming language that manages relational databases. SQL queries can help extract data from this relational database. Data can be accessed in seconds using only a couple of commands. Complex data can be stored in SQL tables to help spot a trend in a business that could result in increased profits. Data analytics, Data pipeline, Data modeling, and Data transformation are key aspects of SQL.

6) Distributed Systems

Contemporary computing would not have reached the heights it is today if not for Distributed systems. Distributed systems are a collection of autonomous computer systems that are connected to a centralized computer network. Distributed system software helps connected computers to share resources and coordinate their activities. The future of modern computing seems to be well-driven by distributed systems, with most applications incorporating some or the other form of Distributed systems.

7) Cloud Computing and Data Visualization 

Cloud Computing: Cloud computing has been around for more than two decades, and in straightforward words, “cloud” means internet, and cloud computing is the process of managing, storing, and accessing data from an internet source rather than from a local drive or server. With increasing popularity, cloud computing is as helpful as other skills mentioned in this blog.

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8) Data Visualization

Another data engineer skill that has become a buzz and has gained humungous attention is data visualization. Representation of crucial information in the form of charts, diagrams, pictures, etc., is classified as data visualization. The advantages of data visualizations are faster access to data, less storage, consistent data, eased migration, higher security, and big data analysis.

Acquiring the above skills will boost your professional career to deliver an exceptional performance as a data engineer. There are some fantastic hands-on data engineering projects to polish these skills. So, why wait? 

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