Top 10 Data Engineering Skills Every Engineer Must Have

It couldn't be more obvious that organisations worldwide have vast amounts of data at their disposal. And this quantity of data has increased drastically in recent years, especially as cloud computing has made its mark in tech. Statista predicts the creation and usage of data to reach 180 zettabytes (180 trillion gigabytes) by 2025. Yes, we can't believe that's a real number either. In 2020, these figures were recorded at 64.2 zettabytes (64.2 trillion gigabytes). So, it's clear there'll be a considerable leap in data consumption over the coming years. But what does this mean for you?

For data to be helpful, it must be processed and analysed. Data Engineering is one of the most rapidly advancing fields, and professionals in this field are among the highest-paid. So, what is data engineering? What are the skills required? How do you become the best in this field? Let's take a dive and discover the top ten Data Engineering skills needed to become a successful Data Engineer.

Data Engineering and Role of a Data Engineer

Data engineering – in its simplest form – involves making raw data understandable and usable. Yes, that really is it. Think of it as a complex data science domain that encompasses gathering, storing, accessing, processing, and analysing vast chunks of data. Typically, a Data Engineer's job includes:

• Raw data identification and acquisition
• Defining database schema
• Construction of data pipelines for data transfer
• Presenting processed data to data scientists for analysis

Top 10 Skills to Master Data Engineering

Once raw data has been through all these stages of data engineering, it becomes valuable to the organisation. A Data Engineer presents the raw data in an accessible format, which is really important to companies.  The bigger an organisation, the more its Data Engineers will have to work with larger datasets. So, we've put together the following skills to help you out:

1. Get Into Programming Basics

Programming forms the basis of the IT industry. Knowledge of programming language basics can help Data Engineers in almost all IT-related tasks. The most relevant programming languages for data engineering are the ones that are suited to building and maintaining data pipelines. The languages that meet these criteria Python and Java, but read below for the full list.

• Python tops the list of top programming languages a Data Engineer should learn. Python has proven efficient for Data Engineers in setting up pipelines and maintaining data flow with its simple syntax and proficiency in automation.
• Java is the oldest and the most appropriate programming language for a Data Engineer. Java is the language behind Data Engineers' most widely used data pipeline tool, Hadoop.
• C and C++ programming languages constitute many existing processes and frameworks that come in handy at different stages of data engineering.
• GoLang is a compiled language that allows a Data eEngineer to handle data using a parallel execution framework.
• Rust is a low-level programming language known for its speed and low memory utilisation.
• Scala is another useful language for secure data engineering and its applications in big data processing

2. Data Handling Using Database Systems

Once a Data Engineer has acquired raw data, that data has to be stored and managed. This task demands the engineer to set up, organise and maintain data in the database. Therefore, a Data Engineer needs to have data administration skills and knowledge of the required languages and tools:

• SQL (Structured Query Language)

Data management would not have come this far without SQL. SQL skills are essential for any Data Engineer worth their salt to store and manage data efficiently. SQL has evolved to incorporate reusable data structures and logic modelling, which has elevated its role in data engineering.

• NoSQL

As data usage has increased, it is increasingly challenging to store and manage data using relational database systems alone. Distributed database systems (NoSQL) have introduced flexibility in data engineering pipelines by:

• Making large amounts of data more manageable
• Handling input of data in various formats
• Scalability

3. Data Warehousing

Data Warehousing includes gathering data from various sources and structuring it in the form of an interpretable hierarchy. All the data becomes available for analysis in a centralised database, known as the data warehouse. Standard data warehouses used in data engineering include Redshift, Azure, Panoply, and BigQuery.

Data warehousing integrates multiple data engineering skills into one group. Skills in this category involve knowledge of ETL (Extract, Transform, Load) tools that allow data extraction, transformation, and loading in a data warehouse. Data engineers use these tools to enable the smooth transition of data between different analysis tools, resulting in faster availability of data to be analysed by data scientists and business experts.

4. Hadoop

Big Data refers to technologies that handle large volumes of data available in various formats. To retrieve valuable information from such a vast data set, Data Engineers need to understand tools that can deal with big data.

Apache Hadoop is an open-source framework that works as an all-in-one solution to Data Engineers' problems with handling big data. It is a collection of tools that allow parallel processing of big data sets using clusters of machines posing as a single unit.

Knowledge of Hadoop enables an engineer to create large-scale data processing applications useful for extracting analysable data.

5. Real-Time Data Processing

Another vital data engineering skill is to possess the knowledge of data processing tools for streaming data. Processing data in bulk is a complex task. Levelling up on this task is the real-time processing of data, which demands faster speed as real-time results are needed.

• Kafka is a widely used open-source real-time processing platform. It stores real-time data as event streams and allows Data Engineers to capture that data through the processing of those event streams.
• Spark is another real-time data processing tool that is helpful for Data Engineers. It allows for the fast development of data-processing pipelines.

6. Automation

Automation refers to optimisation by reducing repetitive manual work. It's a booming concept in the IT world as more and more companies are moving towards cloud computing. Automation can help enhance the efficiency of a Data Engineer's work by accelerating their processes at all levels.

Plus, remote working has increased the demand for cloud computing so Cloud computing platforms like Amazon Web Services (AWS) and Microsoft Azure can benefit Data Engineers. They offer many products and services that can automate data engineering pipelines.

7. Operating Systems

For running applications and performing data engineering tasks, a Data Engineer needs to know the background environment where these processes occur.

Knowledge of the underlying operating system proves useful for troubleshooting problems related to any data engineering task. Standard operating systems include LINUX, Microsoft Windows, Apple macOS, Solaris, and various UNIX distributions. LINUX is becoming popular among engineers for its efficiency in cloud computing.

8. Machine Learning

You may have heard of Machine Learning already, but it's becoming all the more popular in 2022. Machine learning comes under the AI umbrella that corresponds to automated data analysis and predictive methods. While it may be more beneficial for Data Scientists, it is also an essential data engineering skill.

Understanding machine learning can assist Data Engineers in locating underlying data patterns and getting a better understanding of what data scientists require. In addition, many data science job roles are an amalgamation of both data science and data engineering tasks under a single job title. Data Engineers can use knowledge of machine learning to perform both these roles in one.

9. Platform Certification

Preparation for certification exams is an excellent way to strengthen your data engineering skills. We know know everyone has a degree in a relevant computer science field (and that's okay!), so a Data Engineer can acquire the following certifications for developing their data engineering skillset:

● IBM Certified Data Engineer

● CCP Data Engineer from Cloudera

● Google Certified Professional

10. Don't knock it but ... Soft Skills

Technical skills may be vital for Data Engineers to perform their tasks, but soft skills add to a better understanding of their results in an organisational setup. It's good for a Data Engineer to have:

●    Communication skills for understanding requirements of Data Scientists and facilitating clients facing problems with the data.

●    Time management skills for efficiently managing and performing the tasks at hand.

●    Business skills for understanding their organisation's business goals to provide optimal results.

To sum it all up, a Data Engineer plays various roles in an organistion that demands a diverse skill set.  Everything from writing complex codes to managing databases and constructing cloud infrastructures. With the creation of more tools over time, this list will only get longer as Data Engineers will be required to master new tools and be skilled enough to choose the best tools for optimising their work.

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