What is Data Engineering?

The “engineering” element is the key to knowing what data engineering is. Engineers create and construct things. Data engineers plan and construct pipelines that process and transport data into a format that is highly useable when it is received by data scientists or other end users. These pipelines must gather data from several unrelated sources and consolidate it into a single warehouse that consistently represents the data as the only reliable source.

Sounds simple enough, but this position requires a lot of data literacy abilities. Data engineers are in such low supply, which contributes to the role’s ambiguity. One example of a data engineering activity is shown in the image below..

 

Why is Data Engineering So Important Right Now?

You have probably heard or read about Gartner’s 2017 finding that 85% of big data initiatives fail. This was mostly caused by the absence of trustworthy data infrastructures. Data could hardly be relied upon for important business choices. 2019 has come and gone, and nothing has changed. 87% of data science initiatives, according to IBM’s CTO, never reach production. Gartner reaffirmed its forecast that just 80% of initiatives would fail as of this point. A New Vantage Report generated similar figures.

Why is this, then?

The majority of businesses have finished their digital transformations during the last ten years. As a result, there are now unfathomable amounts of new sorts of data being generated at an increased rate. While the need for data scientists to make sense of it all was previously clear, it was less clear who would need to manage and guarantee the quality, security, and accessibility of this data so that data scientists could do their duties.

Data Scientists were often asked to provide the necessary infrastructure and data pipelines in the early days of big data analytics in order to complete their tasks. Their expectations for the work and skill sets did not necessarily include this. As a consequence, data modelling would need to be carried out properly. Data scientists would not utilize data consistently, and there would be duplication of effort. Companies failed because these problems made it difficult for them to get the most out of their data efforts. Additionally, it resulted in a high turnover rate for data scientists that continues to this day.

The Internet of Things, the flood of completed corporate digital transformations, and the rush to become AI-driven make it abundantly evident that businesses today need a wealth of Data Engineers to lay the groundwork for fruitful data science endeavours.
Because of this, the significance and scope of the function of the data engineer will only increase. Businesses need teams of individuals whose main responsibility is to process data in a manner that enables them to get value from it.

Looking to hire data engineers?

Data2Bots recognizes the value of data engineering in achieving business scalability. That’s why we have a team of highly qualified and experienced data engineers ready to help you make insightful decisions on data. Our data engineering experts can also help you make it more relevant and accessible.

Bottom line

Complex business challenges are simpler to tackle using data engineering. We at Data2bot s provide digital automation and artificial intelligence services to expand the speed and dependability of the execution of complicated datasets. This enables all stakeholders to understand the business’s trajectory better and to make more sensible choices that will eventually result in success. Small company owners can adapt to the shift and get more out of their everyday data management with the assistance of our team of skilled data engineers and developers. Because we think it is only a matter of time until every firm learns how to utilize its data to leverage the best business solutions and strategies.