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Data engineering vs data science: What’s the difference?

Globally, 59% of companies have adopted big data, either on a small or large scale. In fact, it was the second most popular implemented technology in 2023 after the cloud.

While businesses are embracing data at all levels, yet many still struggle with understanding what really keeps things rolling—data science or data engineering.

As digital transformation accelerates and AI continues to evolve, both fields are in high demand. But what sets them apart?

This article will explore the key differences between data engineering and data science, how they complement each other, and why both are essential for businesses.

What is data engineering?

Data engineering is the process of designing, constructing, and maintaining the systems and architecture that enable the collection, storage, and processing of data. It involves setting up the technical foundation for managing large volumes of structured and unstructured data.

Its goal is to create a reliable data infrastructure that can handle growing amounts of information, automate data workflows, and ensure that data is clean and accurate.

What does a data engineer do?

The core responsibilities of a data engineer center around managing the flow and structure of data within an organization. This begins with creating data pipelines that automate the movement of data from sources—databases or external files—to a centralized system. They also oversee data architecture that supports data collection, storage, and processing at scale.

Additionally, data engineers handle ETL (Extract, Transform, Load) processes to ensure data is clean, properly formatted, and ready for use. They prioritize data quality and reliability by implementing monitoring systems and correcting issues as they arise. Lastly, they work on optimizing data workflows to guarantee the smooth operation of data pipelines and reduce latency.

Data engineers rely on a wide range of technologies:

• SQL to create and manipulate database schemas, perform complex queries, and extract relevant data for further processing.
• Python for automating data workflows, building data pipelines, and handling ETL processes.
• Cloud platforms (AWS, Google Cloud, and Azure) to store and process data at scale. These platforms offer scalable storage solutions (Amazon S3), processing tools (AWS Lambda) for serverless computing, or Google BigQuery for fast SQL querying.
• Apache Hadoop provides distributed storage and computing.
• Apache Spark enables faster data processing by working with in-memory data.
• Kafka to build data pipelines that process streams of data in real-time.
• Talend, Nannostomus, or Apache Nifi to manage ETL processes.

These tools are essential for running data pipelines, managing ETL processes, and ensuring smooth data flow.

What is data science?

Data science is the practice of using advanced analytical techniques and algorithms to extract knowledge from data. It integrates statistics, programming, and machine learning to analyze data, identify trends, and build predictive models.

Data scientists use both structured and unstructured data to solve complex business problems and generate actionable insights.

Responsibilities of a data scientist

A data scientist’s role is centered around uncovering insights from raw data. They are responsible for identifying trends, solving problems, and making predictions based on their analysis. This often requires working with large datasets from multiple sources, which they clean, transform, and prepare for analysis. 

For example, a data scientist might predict customer churn for a subscription-based service. By analyzing user behavior, interaction frequency, and service usage, they can identify factors that lead to cancellations. They then build a machine learning model that helps the company anticipate which customers are likely to leave.

Here are some of the key tools commonly used by data scientists:

• Python and R programming languages
• SQL for querying databases
• Scikit-learn is widely used for building machine learning models
• TensorFlow and PyTorch for deep learning
• Matplotlib, Seaborn, Tableau for data visualization

The difference between data science vs data engineering

While data science and data engineering are closely related, they differ in several key areas: scope, skill sets, and workflow objectives.

Data engineering focuses on building and maintaining the systems that store and process data, while data science is centered around analyzing that data to extract insights. Each field requires distinct skills, with data engineers specializing in data architecture and pipelines, and data scientists focusing on statistics, machine learning, and modeling. Additionally, their workflows and goals differ, as data engineers prioritize creating reliable infrastructure, while data scientists aim to interpret data and drive business decisions.

Check out the chart below to see the difference in more detail.

Aspect	Data engineering	Data science
Scope	Builds and maintains data systems, pipelines, and architectures	Analyzes data to generate insights, make predictions, and drive decisions
Skills	Data architecture, SQL, Python, ETL processes, cloud platforms (AWS, GCP)	Statistics, machine learning, Python, R, data visualization tools (Tableau, Power BI)
Workflow & objectives	Create reliable data infrastructure, automate data workflows, ensure data quality	Interpret data, build models, generate insights, create forecasts
Data flow	Handles data ingestion, processing, and storage	Works with processed, cleaned data
Interaction with data	Primarily works with raw, unstructured data	Interacts with structured and cleaned data
End product	Data pipelines, data warehouses, data lakes	Reports, predictive models, insights, forecasts

How data engineering and data science work together

Data engineers and data scientists usually work together. The data engineers kick things off by building the systems that collect and process all that raw data. They clean it up, organize it, and store it in a way that makes it easy to access. Basically, they make sure the data is ready for the data scientists to dig into.

Once the data is prepped, it’s handed over to the data scientists. They use that clean data to build models, run experiments, and find patterns.

But the collaboration doesn’t stop there. Data engineers and data scientists keep working to fine-tune the system and make sure it can handle new models and more data as the business grows.

In terms of outsourcing, there are companies that can handle both sides of the data flow. For example, Intsurfing big data company covers the entire spectrum—from building the data infrastructure to delivering actionable insights. They set up the data pipelines, manage ETL processes, and ensure everything is in place for smooth data flow.

The final word

Both data engineering and data science are essential for any data-driven strategy, but depending on your business goals, one may take priority over the other.

If your organization is struggling with managing or accessing data, data engineering should be your focus. On the other hand, if your data is already well-structured and you’re looking to generate insights or predictions, it’s time to prioritize data science.