What Is Data Engineering, And Why Is It So Important?

The “engineering” portion is crucial to comprehending data engineering. Data engineering services are responsible for the design and construction of many objects. Data engineers create pipelines that modify and transmit data into a highly useable format when it reaches Data Scientists or other end users. These pipelines must collect data from various sources and consolidate it into a single warehouse that represents it uniformly as a single source of truth.

Although it appears straightforward, this profession requires a high level of data literacy. That is why Data Engineers are so scarce.

The field of data science receives a lot of attention and excitement. However, there has been an increase in interest in using the technical skills testing platform for data engineering opportunities in recent months.

“What is data engineering?” you might wonder. Why data engineering is now widely acknowledged as critical and what a Data Engineer’s duty is. It’s crucial to remember that the concept of data engineering and what a Data Engineer performs is still evolving, so take this overview with a grain of salt.

Origin Of Data Engineering?

Many consider data engineering a profession that has existed for at least a decade, if not more since the invention of databases, Microsoft SQL Servers, and ETL. Some would argue that database management systems have been popular since IBM introduced them in the 1970s.

The phrase “information engineering” was coined in the 1980s to encompass database architecture and data analysis software engineering. The term “big data” was coined sometime after the internet’s emergence in the 1990s and 2000s. However, DBAs, SQL Developers, and IT experts in the industry at the time were not as “Data Engineers.”

To conclude, several significant technological shifts occurred, increasing the volume, diversity, and velocity of big data. The title “Data Engineer” began to appear in the circles of emerging data-driven organizations like Facebook and Airbnb about 2011. These organizations needed to develop systems to manage masses of potentially important real-time data fast and correctly because they were sitting on mounds.

The phrase data engineering services originated to designate a role that moved away from standard ETL technologies and built its own to handle the rising volumes of data. As big data became more prevalent, the term “data engineering” was coined to characterize a type of software development that focused on data – data infrastructure, data warehousing, data mining, data modeling, to name a few examples.

Why Is Data Engineering So Important At This Time?

You’ve probably heard or read about Gartner’s prediction that 85 percent of big data projects fail, which they made in 2017. It was primarily due to a scarcity of dependable data infrastructure. Data isn’t reliable enough to be used to make essential business decisions. Things had not improved by the time we reached 2019. Eighty-seven percent of data science projects never make it to production, according to IBM’s CTO. According to Gartner, only 80% of projects will fail presently.

What is the reason for this?

Data Scientists were frequently required to develop the necessary infrastructure and data pipelines in the early days of big data analytics. It was probably not in their skill set or job requirements. Among Data Scientists, there would be duplication of effort and inconsistencies in data use. Companies were unable to get the most value from their data initiatives due to these challenges, and as a result, they failed. It also resulted in a high turnover rate among Data Scientists, which persists to this day.

With the flood of completed corporate digital transformations, the Internet of Things, and the race to become AI-driven, it’s clear that firms will need a significant number of Data Engineers to create the framework for successful data science programs.

As a result, Data Engineers’ importance and scope will continue to expand. Companies require teams of employees whose primary purpose is to process data so they may extract that value.

What is the link between data scientists and data engineers, and how do they differ?

Companies used to believe that they could get away with using Data Scientists to perform data engineering services. What has contributed to the “unicorn effect” and the scarcity of Data Scientists?

Some Data Scientists claimed to be able to perform the functions of a Data Engineer.

Data scientists and Data engineers have evolved into two independent and distinct positions, albeit with considerable overlap, due to the volume and speed of data today.

In an advanced analytics team, firms today realize the importance of both Data Scientists and Data Engineers. Without Data Engineers to support this function, it’s nearly impossible to undertake any serious data science.

Data Engineers and Data Scientists frequently collaborate, although their primary abilities and tool knowledge are distinct.

Advanced analytics of data collected and stored in a company’s databases is the focus of Data Scientists. Data Engineers are in charge of designing, managing, and optimizing data flow between databases across the firm.

As a result, Data Scientists will be well-versed in arithmetic and statistics and R, algorithms, and machine learning methods. SQL, MySQL, and NoSQL, as well as architecture and cloud technologies, as well as agile and scrum processes, will be more familiar to Data Engineers.

Both are likely to be familiar with Python and visualization techniques, as well as other programming languages.

Conclusion

With so many options, it’s no surprise that some businesses are still trying to figure out what data engineering service is and how to find and hire data engineers. Over the last few years, the criteria for working as a data engineer have increased. That’s why, like with data science, Thinking of a “Data Engineer” as a group of individuals with a diverse set of data engineering abilities. Several factors will influence which ones you prioritize.

Data engineering is gaining popularity among the young generation folks and companies and people are trying to use it to make data oriented decisions. If you not yet implemented data engineering start using it and enhance your productivity.