Data Engineering & Data Science: How Are They Different?
March 20th, 2023 - Aptaworks
In the past few years, data has undoubtedly become one of the most in-demand sectors in IT. With the various terms used to define different functions within the data world - from data science, data engineering, data analytics, to machine learning - it comes as no surprise that many users – within IT or business - still have some trouble differentiating those terms.
If you read through a few data engineer and data science job listings, you might even notice that there are overlaps between the skills demanded for each position. So, where do data engineering and data science overlap and how do they differ?
Definitions & Responsibilities
Data engineering refers to the process of designing and building a system that defines how raw data is collected, stored, and used for analysis.
On the other hand, data science is an interdisciplinary field that combines mathematics, statistics, artificial intelligence, and computer engineering to identify patterns and extract valuable insights to aid in decision-making.
From the above definitions, we can see that data engineer is the one responsible for building data architectures and preparing data for analysis, while a data scientist is the one responsible for analyzing data and converting them into actionable insights and predictions.
Data Engineering | Data Science |
Develop, construct, test, and maintain data architecture | Extract information from raw data for business insights |
Discover opportunities for data acquisition or integration | Develop and improve statistical models and machine learning algorithms for predictive analytics |
Cleanse errors and eliminate redundant copies of data | Identify patterns and test hypotheses for business questions |
Data Engineering vs Data Science Skills
Relevant skills and experience for data engineering:
Proficiency in SQL and other database technologies
Knowledge of programming languages such as Python, Java, or Scala
Understanding of data modeling and schema design
Familiarity with data warehousing and ETL (Extract, Transform, Load) processes
Knowledge of distributed computing systems like Hadoop, Spark, or Kafka
Familiarity with cloud platforms like AWS, Azure, or GCP
Relevant skills and experience for data science:
Expertise in statistical analysis and modeling
Familiarity with machine learning algorithms and frameworks such as TensorFlow, Keras, or Scikit-learn
Proficiency in programming languages such as Python or R
Experience with data visualization tools such as Tableau or PowerBI
Understanding of data mining and data cleaning techniques
Knowledge of deep learning and neural networks
Data Engineering vs Data Science Tools
As both data engineers and data scientists work with company data, there are some overlap in the tools they use. Check out the tools that are generally used for each role!
Data Engineering Tools | Data Science Tools |
SQL databases: MySQL, PostgreSQL, Oracle | Statistical analysis tools: R, SAS, SPSS |
NoSQL databases: MongoDB, Cassandra, Redis | Machine learning frameworks: TensorFlow, PyTorch, Scikit-learn |
ETL tools: Apache NiFi, Talend, Informatica | Data visualization tools: Tableau, Power BI, matplotlib |
Data modeling tools: ERwin, Visio, and Lucidchart | Deep learning frameworks: Keras, Caffe, MXNet |
Big data platforms: Hadoop, Spark | Big data platforms: Hadoop, Spark |
Cloud platforms: AWS, Azure, Google Cloud | Cloud platforms: AWS, Azure, Google Cloud |
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