Data science vs. data engineering: Defining the difference

Data flows through pipelines, warehouses, and real-time systems. But volume doesn’t create value.

Value requires a trustworthy data infrastructure combined with analytical real-time decisions, predictive modeling, and operational intelligence. Data engineering and data science bridge this gap, transforming raw data into reliable analytics using orchestrated pipelines and sophisticated modeling methods.

Data engineering involves the flow, transformation, and storage of data between systems. Meanwhile, data science uses statistical and computational methods to identify patterns, conduct hypothesis tests, and inform decision-making.

Teams can fall out of alignment, experience delays, and unreliable results when the boundaries between the two are not clear. In this article, we’ll investigate the similarities and differences between data science and data engineering, and look at tools that facilitate aligning both workflows. We’ll also see how these two disciplines collaborate to unlock the full potential of data.

Technical foundations and core workflows

Although both fields help extract value from data, one focuses on infrastructure and flow, while the other concentrates on analysis and insight. Let’s look at their workflows and technical details.

Data engineering

Data engineering is the practice of developing systems that move, organize, prepare, and store raw data. It supports a range of data types, including tables in databases, JSON from an API, or text and images.

Data moves through a series of engineering steps to ensure it is clean and ready for downstream use. Let’s have a look at these steps:

• Data ingestion: The first step involves pulling raw data from numerous data sources, including internal databases, third-party APIs, log files, and event logs. This process can operate in real-time or in batches, depending on the use case.
• Preprocessing: After ingestion, data engineers cleanse and standardize data using automated pipelines. Common preprocessing operations include standard data quality checks, such as formatting, deduplication, schema checks, and eliminating null values. This step ensures that downstream systems get quality data.
• Transformation: After preprocessing, data pipelines reshape and enrich data. This includes joins, aggregations, filters, or calculations to prepare data sets for analysis and interpretation. Engineers commonly version and modularize transformations for reuse and traceability.
• Storage: After transformation, data pipelines transfer data to the storage layer, storing data in systems such as data warehouses or lakehouses..The result is that data stakeholders can find and use high-quality, queryable datasets in their analytics, data applications, and AI solutions.

Data science

Data science utilizes processed data to analyze and perform tasks such as predictive modeling and analytics. Typical data science tasks are:

• Exploratory data analysis (EDA): EDA involves examining distributions, correlations, and anomalies to gain a deeper understanding of the data. It recommends appropriate models or transformations and facilitates early detection of data quality problems.
• Feature engineering: During the feature engineering process, raw data are converted into more interpretable inputs to enhance the accuracy of a predictive model. This can be done by generating ratios, binary flags, time lagging, or domain-specific indicators.
• Statistical analysis and testing: Statistical techniques such as hypothesis testing and confidence interval estimation can be used to evaluate the nature of relationships and variability in data. This brings scientific rigor to analysis and aids data-driven conclusions.
• Visualization: Results are frequently condensed into charts, dashboards, or visual narratives to convey the insights. Plotting libraries, such as Matplotlib, Plotly, and Streamlit, make it easy to explore and share rich results.
• Machine learning (ML): ML models learn directly based on data when pattern recognition and predictive automation are required. ML can provide scalable intelligence based on historical and streaming data, in the form of supervised learning, unsupervised discovery, or deep learning architectures.

Tools and technologies

The tools used by data science and data engineering are indicative of their respective and different objectives. Every domain has a specific tech stack tailored to its unique processes and priorities. Let’s find out in the table below:

The challenges facing both data scientists and data engineers

Data engineering and data science have distinct objectives, but they often encounter similar challenges due to architectural dependencies, evolving data sources, and organizational silos. Let’s have a look at some of the major problems these workflows encounter:

Schema drift. Source systems may change unexpectedly by renaming fields, changing data types, or dropping columns. These changes may disrupt pipelines, undermine downstream models, and distort aggregated measures.

Distributed data inputs. Integrating data from systems like CRM (customer relationship management) systems and payment platforms often introduces issues such as key mismatches, uneven update intervals, and format conflicts. Without proper entity resolution and event time alignment, data science models risk producing inaccurate or misleading outputs.

Latency limits. Latency constraints create friction in various stages of data engineering and data science processes. In engineering, delays in ingestion, processing, or delivery of data may lead to downstream failures of dependent systems and delay real-time analytics. However, in data science, this means models can be trained on stale or incomplete data, limiting their capacity to make accurate and instant predictions.

To address these challenges, both workflows must be designed for low-latency performance at scale. Data engineering workflows can use stream processing, incremental transformation logic, and event-driven orchestration to minimize lag.

Workflows in data science can separate feature engineering, inference, and use continual learning techniques to adapt to changes in data without requiring retraining.

Lack of visibility. When teams don’t track lineage and metadata, they lose visibility into how data flows through the pipeline. It becomes difficult to trace errors, understand dependencies, or explain how a number was calculated.

Use end-to-end observability tools that track data lineage, monitor pipelines and manage metadata to identify failures. Use clear, documented datasets and track how features are created to make models easier to explain and reproduce.

Siloed definitions and fragmented ownership. Without centralized documentation or modeling layers, inconsistencies grow as different teams independently define metrics. Data contracts, common semantic layers, and domain-based ownership (as found in data mesh architectures) can facilitate the standardization of business logic across workflows.

How dbt brings data science and data engineering together

Many of these problems occur because scientists, engineers, and other data stakeholders use different platforms and tools to work with data:

• Scientists and engineers use different platforms for data storage and different languages to transform raw data into datasets that yield actionable insights
• Analysts don’t have one place where they can find high-quality datasets or verify the quality of data
• Every team uses different names and calculations for critical business metrics, resulting in confusion and a loss of trust in data

dbt is a data control plane for data that’s natively operable across various data and cloud platforms. It provides a common platform that all data stakeholders can use to collaborate on data products as part of a mature analytics development lifecycle.

Using dbt, data science and data engineering can bridge the chasms that have historically kept them divided. The result is higher-quality datasets delivered in less time and with measurable business impact.

In data engineering

Data platforms are growing, and data engineering requires flexible tools. dbt addresses this by bringing version control, testing, and documentation to the transformation layer.

• Scalable SQL pipelines: dbt allows data engineers to write SQL transformation logic as discrete, reusable models, making maintenance easier. These models also yield a clean DAG, ensuring proper build dependencies in complex pipelines.
• Fast development and testing. With dbt Fusion, data engineers can debug and test analytics code locally, without setting up a remote data warehouse or checking code into source control. This reduces development lag time and shortens time to release for data product changes.

• Operational excellence in data pipelines: dbt projects are stored in Git and integrated with CI/CD pipelines, allowing them to be peer-reviewed and deployed in a version-controlled manner. It supports a robust testing framework with built-in support for generic tests (distinct, not null, relationships) and custom assertion tests to identify data issues before they reach production.
• Pipeline reliability and idempotency: Pipelines can be re-run without creating duplicates. dbt provides deduplication with stable keys and audit logs to track and troubleshoot runs. This promotes the use of separate development and production targets, allowing for safe testing and staging environments prior to production release.
• Governance and project structure: dbt supports industry best practices, such as dividing include staging, intermediate, and mart structures, which make data pipelines more modular and understandable. Engineers can centralize metadata and decouple model logic from schema details by declaring tables as sources in dbt. Updating the source config ensures all dependent models stay aligned when upstream changes occur.

In data science

Tools like dbt are becoming increasingly important as data engineering and data science merge. Data scientists can obtain clean, well-documented, and production-ready data thanks to dbt's automated testing, version control, and clear data lineage.

• Reliable data: dbt allows automating the data testing, ensuring that datasets are accurate and trusted prior to analysis. This enables data scientists to concentrate on modeling instead of tedious data cleaning.
• Feature engineering collaboration: dbt enables rapidly building, testing, and sharing canonical feature sets, such as user activity aggregations. This ensures a consistent understanding of data definitions used for modeling.
• Data lineage visibility: dbt automatically creates a DAG lineage graph through reference and source functions, making it easy to see how data was transformed and where it originated. This visibility contributes to the explainability, impact analysis, and auditability of model inputs.

Conclusion

When data engineering and data science are closely connected, it creates a strong, end-to-end data ecosystem.

The common denominator between data science and data engineering is the need for common standards, observability, and scalability. dbt supports these needs by centralizing transformation logic, enforcing testing and documentation, and aligning workflows across the data lifecycle. The result is better, higher-quality data products delivered in less time.