Data has become a pivotal asset for all businesses but it can prove useless if it isn’t leveraged effectively. That’s where data engineering comes in. It lays down the foundation for data science applications by preparing raw data for collection and analysis. Specialized in a practice that mainly focuses on the end application of data analysis, data engineers are instrumental in automating the data flow process. Let’s dive further into what is data engineering and how it plays an important role in transforming a company’s big data ecosystem.
What is Data Engineering?
In simple terms, data engineering is the process of building useful systems that helps collect and store data from various sources. This can include anything from fixing errors in a database to making it look presentable. It is essentially the backbone of holistic business process management. This is why data engineering finds a broad range of applications across multiple industries such as retail, healthcare, finance, education, manufacturing, and banking, among others.
It comprises a set of operations aimed at building algorithms, developing interfaces, and utilizing software engineering principles to enable the easy flow of information. Dedicated specialists, known as data engineers, implement relevant database techniques to prepare data for further analysis by data scientists.
Why is Data Engineering so Important?
Data engineering plays an important role in the era of big data. Companies have access to so much information from the real and digital worlds. While this can be a great boon to businesses, multiple data sources can also lead to information overload. This results in scattered data, which restricts the organization from drawing relevant insights and seeing a clear picture of its business functions. That is where data engineering plays an integral part.
It simplifies data and makes it more reliable and useful for data scientists to work with. Also, the data infrastructure built through data engineering allows organizations to leverage the valuable benefits of data analytics.
Responsibilities of Data Engineers
Having understood what is data engineering, we now know that data engineers are primarily responsible for preparing and organizing data for data scientists to use. A data engineer’s main responsibilities can be grouped under two categories.
- Designing data infrastructure for data generation, transmission, storage, and processing
- Maintaining data privacy and accessibility
- Shaping efficient pipelines
- Building accurate data warehouses and reporting systems
- Developing data tools for analysis
- Establishing machine learning algorithms
- Collaborating with data scientists and other engineers to streamline and meet business goals
A good data engineer ensures that the final data is relevant, high quality, reliable, and ready for further use.
What Data Engineering Roles are in Demand?
Data engineering is not necessarily an entry-level job role. Most data engineers kick-start their careers as software engineers or Business Intelligence (BI) analysts and gain experience before moving into managerial roles.
Some of the most popular job titles within the data engineering field are data architect, big data engineer, Machine Learning (ML) engineer, cloud data engineer, data warehouse engineer, technical architect, solutions architect and Extract, Transform, Load (ETL) developer.
Since data engineering is a specialized field of study, it becomes imperative for these IT professionals to have hands-on experience as well as theoretical knowledge of data engineering processes.
Why is Data Engineering Critical to Digital Transformation?
The exponential advancement of data in the modern business world has made data engineering not just a necessity but a growth enabler. Working with huge volumes of data, especially in the digital world, requires an automation process that can actually benefit an organization. Data engineering helps with that and more.
Here’s how: A key aspect of digital transformation is data quality and skilled data engineers help segregate data and boost efficiency. There is likely to be a stronger emphasis on improving everything from operations to analytics. This demands improvements in data architecture, infrastructure, and systems. The data engineering team helps facilitate these changes that serve business needs by building efficient data pipelines.
How Do Data Scientists and Data Engineers Work Together?
A good way to explain how both these roles function is by using the analogy of civil engineers and architects. Architects are responsible for developing initial plans, while civil engineers work on finding structural limitations and implementing these plans. Similarly, in the world of data, data scientists direct analytical plans while data engineers focus on the technical implementation of core solutions.
Both are required to work closely in order to integrate and streamline data to enable business solutions. Once a data scientist has worked on developing a model to process data, a data engineer will figure out how to incorporate this within the overall data processing pipeline. Through each step, both professionals must have good communication skills in order to easily consolidate ideas.
What Skills Do Data Engineers Need?
Now that we know what is data engineering, let’s understand the skills needed to become a data engineer. To begin with, since data engineers use software programs to conduct performative analyses of databases, a background in software development or programming is essential. They must be experienced in coding languages like Python, Java, and Scala. They must also be hands-on with technical tools such as SQL database design, Hadoop, Spark, and Hive, among others.
The most sought-after skill for a data engineer is the ability to design, build and maintain a data warehouse. Pipeline design and data architecture are some other essential skills that every professional data engineer should have a conceptual and practical understanding of.
Is Data Engineering a Good Career?
According to the DICE tech job report 2020, data engineering jobs saw the highest year-on-year demand and it was the fastest-growing tech occupation, beating data scientists. Simply put, as long as businesses continue to use data to draw insights and make strategic decisions, there will always be a demand for data engineers in one form or the other.
Rapid digital transformations, especially post-pandemic, have resulted in an explosion of data. This has given rise to a hike in demand for competent data engineers. Some notable tech companies that hire data engineers are Amazon, Accenture, and Capital One. And since data engineers make more than $110,000 a year, as per Glassdoor, it also emerges as a popular and well-paying career choice.
In conclusion, if your aim is to target a complex business problem, integrating data engineering principles is likely to be the solution. Digital automation and data-driven insights are the future of business, and it is only a matter of time before every organization learns how to efficiently use data and harness optimum business solutions.
Armed with this knowledge of what is data engineering, if you are keen to learn more, explore the extensive range of courses on data sciences on Emeritus, offered in collaboration with globally renowned universities.
By Neha Menon
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