Data Science vs. Data Engineering: Which Big Data Solution to Choose?
Companies searching for a big data solution often rely on recruiting. They understand that data is incredibly complex by nature and always at risk becoming disorganized and unusable. In order to manage the effort, they turn to specialized data professionals. The question is who to hire: a data scientist or a data engineer?
The answer depends on what state your data is in. If you’re in the early stages, a data engineer is more helpful. If you’re farther along, you probably need a data scientist. It’s also worth asking whether hiring offers the big data solution you’re actually looking for? Before you make any selections, learn more about the difference between data science and data engineering.
How Do They Compare?
Both professions have essentially the same goal: to help organizations optimize how they use data. Professionals in either field receive a lot of the same training and have many of the same skills. They differ in how they apply those skills and in the specialized training they pursue. For that reason, data scientists and data engineers are not interchangeable. It’s crucial to determine which you really need, then recruit someone with those exact skills.
How Are They Different?
Think of big data as a skyscraper. Data engineers are the ones who design and build the structure. Data scientists are the ones who work inside of it.
Data engineers are responsible for collecting and integrating data as comprehensively as possible. They create the infrastructure of big data, a system that is logically organized and intuitively accessible. Without data engineers, data would just be a diffuse mass of information that would be impossible to search for insights.
Conducting that search is the job of data scientists. They are the ones who interact with data to create reports, fulfill queries, identify trends, and detect anomalies. Data scientists rely on the infrastructure of big data, but they are not responsible for it. Instead, they work with executives and decision makers to turn data into actionable insights.
How Important Are They?
Both are important, but here’s the problem: demand for data professionals exceeds supply by 50-60%. Companies may want to hire one or both professionals, but the recruiting effort will be difficult, and the right candidate(s) will be costly. Thankfully, data scientists and engineers are important but not essential.
Technology like Syncsort replicates the work of world-class data professionals. Our solutions can help you integrate and organize your data as effectively as an engineer would. Our solutions can also help you optimize and synthesize data with the skill of a scientist. We offer an alternative to trying to find, recruit, and retain one of the most in-demand technical professionals in the labor market. We also help companies make the most of their data without needing endless investments and exceptional in-house IT expertise.
Having a data scientist or engineer on your roster is always an asset, but the simple fact is that it’s not always possible. And even if you can make a hire, you may need a big data solution the exceeds what one professional can provide. In all these instances, Syncsort is available to make up the difference. Learn how to unleash the power of data – Download our eBook: The New Rules for Your Data Landscape.