Data Engineering: What You Need to Know

There has been a lot of talk about data engineering over the past few years and yet it continues to be confused with data science even though the two concepts are not the same by any means. Data engineering refers to the process of transforming raw data into a form that can then be run through data science tools. It essentially makes data more accessible and builds raw data analysis to develop predictive models and identify short- and long-term trends.

Further, it is fundamentally more important than data science. Data engineering offers an infrastructure that allows data scientists to analyze data and build models – there can’t be data science without data engineering. Essentially, data engineering can be considered the foundation for a successful data-driven company. It includes the facilitation of data stack development to accumulate, store, clean, and process data in real-time or in batches and prepares the data for further analysis.

Now, allow us to walk you through some of the key reasons why data engineering is so important in the modern era where data is available in plentitude:

1. Foundation of data science: A rather critical reason why data engineering is important in this day and age where there is no dearth of data is that it serves as the foundation of data science. You see, data science involves cleaning up and analyzing data to glean insights and metrics to help answer questions and address business problems. This, however, would not be possible without data engineering which seeks to develop, test, and maintain data pipelines and architectures that are then used by data science tools for analysis.
2. Develop data architecture: We will admit, that data science and data engineering are actually meant to work arm in arm but that does not take away from the fact that the two make distinctive contributions to data-based projects. In this regard, data engineering helps ensure such projects do not get stuck in the production pipeline owing to unmanageable data sets and it does so by enabling the development of a robust and well-grounded architecture. Doing so makes sure that data can be analyzed efficiently.
3. Data quality: As more and more companies kick off their digital transformation journey, an abundance of data is being created and this data is crucial to these companies’ efforts to succeed in the market, course. However, the quantity of data here is humongous and nature, often complex. Hence, before this data can make meaningful contributions to the company’s goals, it must be first organized in terms of security, quality, and availability and this is exactly where data engineering comes in. Data engineering seeks to ensure the collected data is streamlined and of the requisite quality, before it can be processed by data science tools.

Now, before we wrap this up, let us also quickly take a look at some of the key differences between data science and data engineering since the two are often confused with each other. Let us start with data science: it involves cleaning up and analyzing data, answering questions, and providing metrics that help companies address problems that plague the business. Simply put, data science has to mostly do with running advanced analytics on data gathered in the company’s databases. On the other hand, data engineering involves the development, testing, and maintenance of data architectures and pipelines which are then to be used by data scientists for conducting the requisite analysis. Think of data engineering as the foundation based on which data science can deliver the required metrics. Now that you understand what data engineering is all about and the role it plays, you can go ahead and start looking for a trusted data engineering consulting services provider to assist with your projects.

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