Data is the élan vital of the world we live in. 

Without it, we wouldn't be lapping up the perks of personalized experiences that make our lives easy, convenient, and engaging. 

On the business front, data has made a game-changing impact, with leaders taking decisions that matter, building products that serve, and shaping experiences that we love. On coming to terms with its massive potential, decision-makers are scrambling to adopt the new-league practices that simplify curation and analysis of data. 

Two of such approaches are Data Science and Data Engineering that have been under the spotlight since the mainstreaming of big data. Though interconnected, both of these approaches have distinct characteristics and responsibilities, which modern businesses must understand.

The article to follow lays out the key differences between data science and data engineering disciplines and what organizations must choose based on their workflows, objectives, and targets. 

So, let's set the ball rolling. 

What is Data Science?

Data science refers to methods and techniques that help extract hidden knowledge and insights from data. Data scientists apply statistical methods, ML algorithms, and data visualization methodologies to analyze data, dig up patterns hidden in it, and forecast what might happen in the future.

Data scientists have many responsibilities, including:

  • Analysis of data
  • Extracting patterns and trends within data
  • Building predictive models on the back of ML techniques
  • Creating data visualizations
  • Making data comprehensible and ready to act on
  • Collaborating with key stakeholders to turn data into action

In short, when you hire data science services professionals, you hire people with the required domain expertise, technical skills, and strategic vision that helps you turn raw into actionable insights and take effective decisions on the go.

What is Data Engineering?

Data engineering is fundamental to every organization wanting to leverage its data, irrespective of its type and form. It entails processes that aid in the curation, control, and management of data across the board. The objective of data engineering services is to ensure data is reliable, accessible, and efficiently processes. 

Data engineers have many responsibilities, including:

Building and maintaining data pipelines

  • Creating a central repository
  • Developing data storage systems
  • Ensuring data integrity and interoperability
  • Establishing data integration
  • Adhering to compliance and standards
  • Building data validation and governance models
  • Optimizing the performance of data systems 

Differences Between Data Science and Data Engineering

 Key Differences Between Data Science and Data Engineering

Refer to the table below to understand the salient features and differences between data science and engineering disciplines. 

Aspect

Data Engineering

Data Science

Focus & Goals

Establishing an infrastructure for effective processing and storage of data. 

Running analyses of data to uncover hidden patterns and trends in data and make predictions. 

Skill Sets

Expertise in software engineering, databases, and distributed systems.

Knowledge of statistics, ML, and data visualizations.

Tools and Technologies

Apache Hadoop, Apache Spark, SQL, and cloud platforms like AWS, Azure, and Google Cloud can assist along the journey. 

Python, R, TensorFlow, and data visualization tools like Tableau and Power BI can come in handy.

Output

Produces data pipelines, storage systems, and frameworks for data processing.

Produces insights, predictive models, and visualizations for decision-making.

Conclusion

Data engineering and data science go hand in hand when it comes to leveraging the power of data for effective decision-making and business planning. 

However, the two disciplines are as different from each other as the north and the south pole. Both have different objectives. Both require different skill sets. Both need different tools and techniques. 

Organizations must choose the one that aligns with their requirements and caters to their agendas. If you're looking to streamline the data ecosystem within your organization, data engineering is your pursuit. However, if you're looking to nurture intelligent business functions while powering up innovation initiatives, data science is the way to go. 

Once you've made the decision, hire professionals and kickstart your journey to become an organization that keeps a data-first mindset at the forefront of every decision it makes.