What Is The Real Difference Between Data Engineers and Data Scientists?

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To launch your data career, you’ll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard’s Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship. Wondering which data career path is right for you? Read on to find out.

Although data engineers and data scientists have overlapping skill sets, they fulfill different roles within the fields of big data and AI system development. Data scientists develop analytical models, while data engineers deploy those models in production. As such, data scientists focus primarily on analytics, and data engineers focus more heavily on programming.

To launch your data career, you’ll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard’s Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship. Wondering which data career path is right for you? Read on to find out.


What Do Data Engineers Do?

Data engineers create and maintain key data infrastructures like databases, data warehouses, and data pipelines. Data engineers also prepare data for production by converting raw, unstructured data into a structured format that can be analyzed and interpreted.

The work of data engineers is foundational to big data analytics. Data engineers construct data pipelines that capture data from users, SaaS platforms, and other data producers. Data pipelines process this data in real-time and store it in warehouses for analysis. This process is referred to as ETL (extract, transform, load).

The responsibilities of a data engineer vary depending on organizational size. A data engineer at a small company might build data ecosystems and manage the entirety of the data flow, similar to a full-stack data scientist. At a mid-sized company, data engineers craft custom tools to support big data analytics. At large companies that handle large, complex volumes of data, data engineers often focus on optimizing ETL processes.


What Do Data Scientists Do?

Data scientists analyze and interpret data to solve business problems. Initially, data scientists explore data and conduct market research in order to formulate business questions around a specific trend or pain point. Data scientists must then frame business questions as data analytics problems.

To identify critical patterns within a data set, data scientists use advanced analytical techniques powered by machine learning and statistics. Data scientists build models to establish relationships between data objects. Predictive models forecast future events based on historical data, while prescriptive models recommend actionable changes in business strategy based on current and historical data.

Data scientists must also interpret the results of their analyses to design data-driven business solutions. When data scientists present their findings to stakeholders, they must build a cohesive narrative that communicates the meaning of their results and how those results can inform business strategy.


Key Data Engineering Skills

Data engineers need a robust software engineering foundation and must use programming knowledge to deploy models, build data pipelines, and orchestrate data warehousing solutions. Python, Java, and Scala are three of the top programming languages most commonly used by data engineers.

Data engineers must also be able to manipulate database management systems, which facilitate information storage and retrieval. Data engineers use SQL to build and manage relational database systems.

Data engineers also need to understand the basics of distributed systems and demonstrate fluency in Hadoop, which is a framework that enables distributed processing of vast data sets. A strong understanding of data APIs is also a must. Software applications use APIs to access and retrieve data, and data engineers build APIs in databases so that data scientists can query the data.

Finally, data engineers use cursory machine learning knowledge to understand the needs of data scientists, deploy models in production more efficiently, and build improved data pipelines.


Key Data Science Skills

Data scientists have strong programming skills and a solid understanding of statistics. Python is known as the lingua franca of data science, and data scientists use this popular programming language to write code and use powerful Python-based tools. Data scientists also use R to manipulate data, implement machine learning algorithms, and conduct statistical analysis. Data scientists also use SQL to read, retrieve, and add data to databases.

Machine learning is also a key data science skill. Data scientists use algorithms to clean, categorize, and analyze large data sets. Machine learning combines computer science and statistics, and machine learning models help data scientists make data-driven predictions and recommendations.

Data scientists must also be well-versed in data visualization, which uses charts, graphics, maps and more to represent data to stakeholders. Data scientists must also be able to create coherent narratives that show how their findings impact an organization’s business goals.


Top Tools for Data Engineers

Data engineers need to be proficient with distributed processing technologies and tools used to work with data at scale. Top tools for data engineers include:

  • Apache Hadoop and Apache Spark. Hadoop is a major big data tool that enables batch processing of vast datasets across servers. Spark is a data processing engine that enables stream processing.
  • Amazon Web Services/Redshift. Data warehousing applications like AWS are built to show a long-range view of data over time.
  • Microsoft Azure. Data engineers use this cloud technology to build data analytics systems at scale. 
  • C++. Data engineers use this programming language to rapidly compute large data sets quickly in the absence of predefined algorithms.


Top Tools for Data Scientists

Data scientists need a strong command of analytical and data visualization tools, including:

  • Tableau. This data viz software allows data scientists to create interactive visualizations.  Tableau can manage large amounts of data and interface with multiple data sources.
  • Jupyter. This interactive computational notebook can be used for writing live code, cleaning data, data, viz, and more.
  • Apache Hadoop. Hadoop can store large data sets and stream the data to applications like MapReduce, which handle data analytics.
  • Scikit-learn. This predominantly Python-based machine learning library offers features like data classification, regression, clustering, preprocessing, and more.