25 Oct · 6 min read
Data is the digital economy's new oil - an untapped, enormously valuable asset. Every day, the world generates approximately 2.5 quintillions of bytes of data. We live in a world where global corporations are rushing to implement data science and analytics strategies to improve business performance. And we still live in a world where businesses compare data scientists vs data engineers.
Some predicted a severe shortage of data scientists by the end of 2018 in the IT industry a few years ago. It will become more difficult to keep up with the growing demand for expert data scientists. Furthermore, one more assumption has put data scientists on the defensive: everything else in data science will be automated by 2020. However, despite all expectations, assumptions, and disruptions, the demand for data scientists continues to grow. Let's look at which is the better option: data scientist or data engineer.
Data engineers are individuals who provide a platform for data modeling. A data engineer's job entails gathering, storing, and processing data. Data engineers have strong software engineering skills, extensive knowledge of databases, and experience with data administration. A data engineer's primary value is their ability to build and maintain data pipelines, which allows them to distribute information to data scientists. Data engineers who understand algorithms can run basic learning models. However, as the underlying business problem becomes more complex, professionals must use more sophisticated machine learning algorithms. This is where a data engineer's skills become limited, and organizations must hire data scientists.
Data engineers struggle with the challenges of database integration and large, unstructured datasets. A data engineer's ultimate goal is to provide clean data in a usable format to data analysts, data scientists, or whoever else may require it. To summarise, data engineers are data geeks who lay the groundwork for data scientists to efficiently work with the data required for calculations and experiments.
A data scientist is a professional who analyses and processes large amounts of structured and unstructured data. A data scientist excels at computer science, statistics, and mathematical applications. They analyze, model data, design frameworks, and then use their skills to interpret the data results so that businesses or organizations can create actionable plans. According to IBM, a data scientist is someone who is "partly analytical and partly creative."
A data engineer is a data professional who is responsible for preparing the data infrastructure for analysis. They are concerned with raw data production readiness as well as elements such as formats, resilience, scaling, data storage, and security. Data engineers are responsible for designing, developing, testing, integrating, managing, and optimizing data from various sources. They also create the infrastructure and architectures that allow data to be generated.
Their main goal is to create free-flowing data pipelines by combining various big data technologies that enable real-time analytics. Complex queries are also written by data engineers to ensure that data is easily accessible.
Data scientists are primarily concerned with acquiring new insights from the data which data engineers have also prepared for them. They conduct online experiments, develop hypotheses, and use their knowledge of statistics, data analytics, data visualization, and machine learning algorithms to identify trends and forecasts for the business as part of their job.
They also work with business leaders to understand their specific needs and present complex findings that a general business audience can understand.
Data engineers typically have a background in software engineering and are fluent in programming languages such as Java, Python, SQL, and Scala. Alternatively, they may have a degree in mathematics or statistics, which allows them to apply various analytical approaches to business problems.
Most companies prefer candidates with a bachelor's degree in computer science, applied math, or information technology to work as data engineers. Candidates may also be required to hold data engineering certifications such as Google's Professional Data Engineer or IBM Certified Data Engineer. It also helps if they have prior experience building large data warehouses capable of running Extract, Transform, and Load, or ETL, operations on large data sets.
Data scientists are typically presented with large amounts of data with no specific business problems to solve. In this case, the data scientist is expected to investigate the data, formulate appropriate questions, and present their findings. This necessitates that data scientists understand various techniques in big data infrastructures, data mining, machine learning algorithms, and statistics. They must also be up to date with all the latest technologies because they must work with data sets in various forms to run their algorithms effectively and efficiently.
Data scientists must be fluent in programming languages such as SQL, Python, R, and Java, as well as be familiar with tools such as Hive, Hadoop, Cassandra, and MongoDB.
Aside from the differences, data scientists and data engineers may share one thing: a background in computer science. This field of study is very popular among both professions. Of course, data scientists have frequently studied econometrics, mathematics, statistics, and operations research. They frequently have a bit more business knowledge than data engineers. Data engineers frequently come from engineering backgrounds, and they almost always have some prior education in computer engineering. All of this, however, does not rule out the possibility of finding data engineers with prior experience in operations and business acumen.
Education for Data Engineers You must understand that, in general, the data science industry is made up of professionals from various backgrounds: it is not uncommon for physicists, biologists, or meteorologists to find their way to data science. Others have transitioned from web development, database administration, and other fields to data science.
When it comes to salaries, the median market for data scientists is set at $135,000 per year on average. The starting point is $43,000, with an absolute max of $364,000. The medium market for data engineers is a little lower: they earn on average $124,000, and their minimum and maximum paychecks are also significantly lower: the minimum is $34,000 per year, and the maximum is $341,000. The source of the wage disparity is unclear, but it could be related to the number of open positions: according to Indeed.com, there are approximately 85,000 job openings for data engineers, while there are about 110,000 job openings for data scientists.
Companies seeking data engineers include PlayStation, The New York Times, Bloomberg, and Verizon, but companies such as Spotify, Facebook, and Amazon have also sought data engineers in the past. Data scientists, on the other hand, are in high demand at firms like Dropbox, Microsoft, Deloitte, and Walmart.
A data scientist starts with the observation of data trends and moves forward to discover the unknown. In contrast, a data engineer has a goal in mind and works backward to find the perfect solution that meets the business requirements. A data scientist's job is more like a research position, whereas a data engineer's job is more focused on development.
Many data engineers are involved in complex data transformations and writing machine learning code, but it is their focus that distinguishes them. A data scientist's primary focus is on data mining or statistical modeling, whereas a data engineer focuses on cleaning data, coding, and implementing machine learning algorithmic models perfected by data scientists.