For several Internet of Things (IoT) applications, data scientists get the spotlight. These are the men and women who find the insights hidden within Big Data so that businesses can make strategic, intelligence-based decisions. However, they need help to pull off this feat. They can’t deliver these critical insights without the help of data engineers. These unsung heroes work behind the scenes to make sure the Big Data is there to analyze.
Like the general contractor to a data scientist’s architect, data engineers design and maintain the networks and software that keep the Big Data pipeline operating. Data engineers build the framework that will house the data. The roles of data scientist and data engineers can be confusing because there is some overlap. Data engineer and data scientist are not different titles for the same job, however. The two jobs need different skills and experience. Some data scientists can do data engineering. Some data engineers can do data analysis and data visualization.
In enterprises, data engineers’ skills are needed for large applications. Oh the other hand, research is a primary focus of the data scientist. Like general contractors, data engineers are a special breed. The best have certain personality traits that help them excel: focus, mechanical aptitude, patience, and persistence. Good data engineers get down in the trenches. They want to understand how and why data pipelines work – or don’t. Data engineers need patience and persistence to set things right.
These curious, hands-on folks create the backdrop for data scientists to do modeling. They gather, store and process data so that data scientists can analyze it for insights. Responsible for data management, data engineers handle procedures, guidelines, and standards. They develop data management technologies and software engineering tools. They design custom software and discover ways to recover from disasters. They improve data reliability, efficiency, and quality. User-defined functions and analytics are part of a data engineer’s job, too.
On the other hand, data scientists are drawing the plans rather than pouring the foundation.
They handle analytic projects that arise from the needs of the business. Data scientists also take on data mining architectures, modeling standards, reporting and data methodologies. They manage data mining system performance and efficiency as well.
Data does not magically show up at the data scientist’s door. Data engineers do the valuable work of building and maintaining the data pipelines that send information to data scientists. They can run basic learning models if they understand algorithms. But data scientists tackle business problems that take sophisticated machine learning algorithms. Really good data scientists adapt machine learning models to meet changing requirements of the business or agency.
The Right Tool for the Job
Database integration and unstructured Big Data are the challenges that data engineers take on. They must clean up that unstructured data before they pass it to anyone in the organization who needs it. Like a contractor building a house to specs, data engineers set up the foundations for data scientists to work easily with data. Data engineers should know data warehousing, database design, data collection and transfer, and coding.
Any contractor knows that you need the right tool for each job. The tools data engineers use depend mostly on which part of the data pipeline they focus on. Data engineers at the rear of the pipeline build APIs for data consumption, integrate datasets from external sources and analyze how the data is used to support business growth.
For them, Python is a good language. Data engineers use it to write code related to data ingestion. Python can talk to any data store, such as NoSQL or RDBMS. They might have to use Big Data technologies like Hadoop and Spark to suggest improvements based on how data is used. Among the important tools for a data engineer are:
- NoSQL databases e.g. Cassandra and MongoDB
- Hadoop and related tools such as HBase, Hive, Pig, etc.
Benefitting from the Growth Curve
Data engineers are rewarded for their expertise and special skills. In the United States, data engineers’ average salary is $95,526. The low end of the pay scale is $65,000 and the high end is $121,000. U.S. demand for these jobs should grow 15 percent by 2024. That is faster than the average for all U.S. occupations. Some of the biggest names in business and the U.S. government are ramping up their requirements for both positions.
The Economist Intelligence Unit conducted a survey in 2015 involving 422 executives in the U.S. and Europe. They were asked about the digital skills most in demand in industries like financial services, healthcare, manufacturing, and retail. Forty-three percent of the executives said that in three years, analytics and big data skills will be the most important digital capabilities at their companies.
This evidence shows that there is a great need for data engineers (builders) and data scientists (architects) alike. Organizations are willing to pay handsomely for the skills that help them gather and analyze the data that can transform their businesses. Proactive IT professionals stand to advance their careers and do engaging work by developing or updating their skills now.
Learning@Cisco product manager Neeraj Chadha has more than 20 years of experience in the networking industry. Over that time, he has functioned as a software developer and network engineer, and in various aspects of product management. Currently, he guides the overall product strategy and evolution of Cisco courseware and certifications around wireless, collaboration, and Big Data and analytics. Neeraj’s primary areas of focus include technology trends, digital transformation, continuing education and product strategy.