"Data and Information is the oil of the 21st century, and analytics is the combustion engine." – Peter Sondergaard of Gartner.
As data emerges as the new currency of the 21st century, there is a growing demand for both data science and data engineering across industry domains. Here are a few mind-boggling statistics related to the same:
- Over 180 zettabytes of data will be generated by 2025.
- 80-90% of the data generated globally is unstructured.
- The Big Data analytics global market is set to reach around $103 billion by 2023.
- Over 97% of organizations are investing in Big Data and Artificial Intelligence (AI).
There is a growing industry demand for both data engineers and data scientists across the globe. Dice Insights had previously reported that data engineering is the trending job role in the technology domain. In 2021, LinkedIn also listed data engineering as the leading job role.
As data continues to proliferate in the business domain, there is a more distinct shift towards data engineering as compared to data science. For instance, there are 70% more open job positions in data engineering than in data science.
Along those lines, it's essential to question how data engineering compares with data science — precisely what this article elaborates on.
Data Engineering vs. Data Science - Understanding Hierarchy of Needs
Organizations need to understand how data engineering differs from data science to leverage their capabilities. To understand this difference, they must know the "hierarchy of needs" in the data process. This includes the following five levels starting from:
- Data collection is the starting level of gathering data using instrumentation, logging, sensors, and from external data sources. Typically, data infrastructure engineers perform the task of data collection.
- Data migration or storage is the next level that uses data pipelines, ETL processes, and structured (and unstructured) data storage. This is the domain of the data engineer.
- Data exploration or transformation is the third hierarchy level involving data cleaning, preparation, and anomaly detection. Data scientists and analysts perform the task of data transformation.
- Data labeling and aggregation is the next level that involves data analytics, key metrics, and data training. Again, data scientists and analysts typically perform this task.
- Data optimization is the last level that involves A/B testing and machine learning algorithms. Data scientists and analysts perform the task of data optimization.
Effectively, data engineering is about collecting relevant data and moving the data through pipelines for use in data science. While the data science function performs most of the tasks in the hierarchy, the data engineering function is crucial for preparing pipelines that are used later in production projects.
With this information, let's next understand the five main differences between data engineering and science.
Data Engineering vs. Data Science - 5 Main Differences
To get the basic difference right, here are five main differences between data science and engineering:
Though both data science and engineering serve the same final objective, they are distinctly different disciplines. Data engineering is the discipline that comprises the creation of various frameworks and APIs used to extract raw data from multiple structured and unstructured data sources.
On the other hand, data science is the discipline that extracts valuable insights from the collected data and creates business value from machine learning or ML models.
In short, a data engineer prepares the foundation for the data scientist to develop the data model.
- Area of Expertise
Data engineering and science disciplines require a similar background in mathematics, physics, or computer engineering. On their part, data engineers need proficiency in computer hardware, software, and middleware. They don't preferably need any expertise in machine learning and statistics. Here are some of their required competencies:
- Logical thinking
- Identifying relevant data to extract
- Organization and management skills
The data science discipline requires expertise in the areas of mathematics, statistics, computer science, and the technology domain. However, this discipline does not require hardware knowledge. Data scientists also need expertise in creating AI and ML models. Here are some of their required competencies:
- Good communication and analytics skills
- Problem-solving skills
- Data hypothesis
- Job Role
Next, let us discuss the difference in job roles for data engineers and scientists. Data engineers are responsible for designing and building the necessary infrastructure and architecture for data generation. Effectively, data engineers design, build, optimize, and test the collected raw data. They also deploy Big Data tools to build data pipelines that facilitate real-time data analytics. Data engineers can also write complex queries to access data sources.
On the other end, data scientists focus on their role in solving business problems and answering questions. These include business problems like how to optimize business processes and improve the customer experience. They focus on performing mathematical and statistical analyses on data provided by data engineers.
- Technical Skills
Data engineers have different sets of technical skills from data scientists. Data engineers need technical skills in programming, distributed systems, database design, and system architecture. Programming skills for data engineers include Python, SQL, ETL, Apache Spark, and Apache Hadoop.
Contrarily, data scientists require programming skills along with data wrangling and visualization skills, database management, and machine learning skills. Some of the top in-demand skills for data scientists include Python and R, along with business intelligence tools like Tableau and KNIME.
Data science and engineering are applied in different real-life use cases. For instance, data science is used in:
- Creating a recommended video list on YouTube.
- Filtering emails for spam and genuine messages.
Data engineering is used to pull daily tweets from a Twitter account and store them in a data warehouse.
As they are different disciplines, business organizations must approach them for different use cases and applications while comprehending their interdependence. This interdependence paves the way for immense growth potential.
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