There is no doubt about the importance of the data engineer role in any data-driven organization. It takes data engineers to set up, run, and maintain data architectures and pipelines while also preparing, structuring, maintaining, and ensuring streamlined data flow so that data is available to and useful by other professionals like data scientists and analysts who make sense of it. Data-driven organizations by leveraging insights from data, position themselves to deliver better customer experience, explore new opportunities in time, and mitigate threats ultimately maintaining a competitive edge.
However, data engineering is a much broader discipline than its definition depicts, and the data engineering role is not as straightforward as one would imagine. While many companies have taken the steps to adopt data-driven technologies and invested heavily in digital transformation and data analytics, many are still struggling to become data-centric and the gap is certainly in the skills. Professionals responsible for managing an organization’s data ought to possess the right skill set, knowledge, and culture to steer operations in the right direction.
Thus, in addition to data science educational qualifications such as a bachelor’s degree or masters in computer science, a certification in software or data engineering, they also need to understand that the technology field is fast evolving and personal development initiatives like undertaking real-world practice projects, enrolling in a data engineering Bootcamp, and actively taking part in data engineering related forums, will play a significant role in updating their skills.
How the role of the data engineer has evolved over time
By 2019, the tech job reported data engineering as the fastest-growing role with an annual growth of 50% in terms of open positions and 40% in terms of interviews. Thanks to the gaining importance of big data, the data engineering role is not only gaining more importance by the day but also evolving with the emergence of more robust high-performance, and flexible technologies to handle big data in real-time.
The role of the data engineer came up around 2011 in the advent of Facebook and other sites like Airbnb which would generate vast volumes of real-time data that required robust tools to manage the data fast and accurately. Then, the data engineers were to first figure out how to migrate data from outdated systems, databases, and platforms, to modern flexible high-performing data architectures to enable fast and easy access of data for better quality insights. Traditional databases, SQL servers, and ETL tools lacked the capacity to make the most of such vast volumes of data.
Data engineering, as the name suggests, is a type of engineering focused on data management processes including data infrastructure development and management, data mining, modeling, analysis, presentation, and more. The data engineer works hand in hand with the data scientist as his/her role is to lay down the framework and infrastructure used by the data scientist to perform complex data analysis.
With the current technological advancements, disruptive technologies like the internet of things (IoT), cloud computing, blockchain, artificial intelligence, and machine learning have come up while automation is gaining traction fast. Thus beyond the ETL tasks that they initially performed, data engineers are expected to be well-versed with cloud environments and emerging technologies.
Overall, data engineers are responsible for making sure that users of data have access to the right and high-quality data for their specific applications at the required time.
What is the future of data engineering?
Automation of tools seems like an inevitable future of tech. The world is coming to terms with the convenience, speed, and efficiency with which automation can help accomplish certain tasks performed by humans with minimal errors.
The big question in the data engineering field is
“will automation replace the role of data engineers in the future?”
Research has found a 3% chance of automation replacing data engineering roles which means that the data engineering role is nowhere near being replaced by automation. This is to say that the demand for data engineers is projected to rise in the future. Businesses envision tools that come designed to automatically undertake such tasks as collecting, preparation, cleaning, and structuring. However, the data engineering role cannot be completely eliminated since it is required during the setup and configuration of data infrastructures and their customization to match the needs of the business. He is also relied upon to design data models that will be used for the data processes listed. Ideally, automation works well only for tedious repetitive, and time-consuming tasks done by humans.
Other projected changes for data engineering
1. Overlap between data engineering and other roles
While the data engineering roles have been distinct in the past, the future will see an overlap with other designations such as software engineering.
With the current upskilling trends, software engineers and data scientists are acquiring engineering skills to enable them to take up some engineering duties.
2. Development of real-time and data streaming infrastructure
This is a current trend that is already undergoing evolution. The vast amounts of transactional and social data being generated daily demand real-time analytics which may not be realized by batch processing ETL technology that was used in the past.
The emergence of technologies such as Apache Storm, Apache Kafka, and more now makes it possible to undertake real-time data processing and analytics for greater quality insights. Such technologies are also designed to scale fast to take care of unpredicted demand.
3. IoT and self-service analytics
The advent of IoT has taken IT services to a whole new dimension and enterprises are now shifting to self-service data and analytics platforms. Self-service tools and platforms enable professionals to analyze data as well as build reports and dashboards to obtain insights with minimal IT support. While analytics tools were considered complex and a reserve for data scientists and engineers, self-service analytics tools are simple business intelligence tools that non-technical professionals can use to achieve a faster time to insight, faster decision-making, and spurred innovation. As expectations for intelligent self-service analytics tools rise, eyes are on data engineers to make it possible.
With more and more businesses prioritizing digital transformation, many are on the lookout for robust tools and platforms to manage the colossal volumes of streaming data generated faster and in a wider variety than ever before. Automation is certainly an option that businesses are exploring to make data-driven decisions and it takes well-versed and skilled data engineers to do the job right. Secondly, today even start-ups and small businesses are data-centric therefore the demand for data engineers is set to rise who will provide tools and solutions for businesses leveraging data. Seemingly, their roles in larger organizations are growing as the focus now shifts to data standards, security, and governance.