Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
- Forbes
Globally, many think that data scientist is the best job after Harvard declared it to be one of the hottest jobs of the decade back in 2012. And since then, many have been choosing it as their career path.
But the role of a data engineer is as much important as the data scientist is. Because if a data scientist develops a breakthrough algorithm, then the big data engineer puts it into production for use by the business, according to a statistics report from Gartner that only 15 percent of big data projects ever make it into production. And while they never dig into the reasons why 85 percent of big data projects never make it there.
Let’s have a look at the comparison between data scientists, big data engineers.
| Data Scientists | Big Data Engineers | |
|
Definition |
Similar to the word ‘scientist,’ data scientists gather data, build and maintain databases, clean and segregate data for several needs and also work on data visualization and analysis. | The big data engineer experts deal with a continuous and huge amount of data, define parameters and datasets for analysis, and program analytical systems to offer strategic insights for businesses. |
|
Skills |
SAS, R, Python programming, Hadoop similar tools, SQL database, Analytical skills, Statistics, Mathematics, Visionary thinking, Restructuring Data, Database Construction, and Management
|
Java, Hadoop, Mathematics and Statistics, Programming and Computer Science, Analytical Skills, Business Strategy |
|
Impact on Various Industries |
Web development, Search Engines, Advertisements, Internet search, E-commerce, Finance, Digital advertisements, Telecom, Utilities, Adaptive Algorithms, AI Systems | Retail, Banking and investment, Fraud detection and analyzing, Customer-centric applications, Operational analysis, E-commerce, Financial Services, Communication
|
Data engineering is the process of developing and building systems for collecting, storing, and analyzing data. It is a wide field with various applications in several industries. Firms have collected massive amounts of data, and they need data infrastructure and personnel to sort and analyze the information. This resulted in the demand for professional big data engineers who work to design systems that collect, manage, and convert the raw data into usable information.
A professional data engineer needs a set of skills to effectively perform the tasks.
The data engineers are needed to be well-versed in the data warehousing solutions, programming languages required for analysis and statistical modeling, and developing data pipelines. They can choose to do a data engineering certification which gives them better insights into all these areas.
This information is useful for data scientists and business analysts to interpret. The main objective is to make data accessible so that the companies can take help of it for evaluating and optimizing their business’s overall performance. That is why for everyone data scientist firms require at least two data engineers and according to Jesse Anderson’s blog on oreilly.com, one may require as many as 5 data engineers per every 1 data scientist.
By being a certified data scientist an individual can improve their skills before beginning their career in this field. Certification gives excellent knowledge and guidance by giving exposure to real-time projects.
Data engineers are in increasing demand especially those who hold at least one data engineering certification. Fortunately, the more tedious aspects of the data engineering role can be automated to let the data engineer focus more on the logic of the pipelines. So while data engineers may be more essential than data scientists, there is hope in the form of automation that can make the data engineers more productive.
The data engineering automation will do the same in the big data space. So while data engineering is hard, big data engineers are rare and are in high demand. If one wants to be a data engineer, and they want to get more efficient at doing their job, or they know a data engineer or someone who wants to become one. They should start their preparation now. And must learn more about how they can help respond to the current demand for data engineering and get the data science algorithms into production.
