Data Engineering vs Data Science – what is the difference? 

These days, the amount of data generated worldwide is growing at an accelerated pace. It is estimated that the volume of data generated, consumed, copied and stored will reach more than 180 zettabytes by 2025*.

No matter how big or small businesses are, data is of utmost importance and can help them succeed and outperform competition.  

Therefore, modern companies should be aware of the power of data and use it for: 

• decision-making process, 
• gaining competitive advantage in the market, 
• adopting sound business strategies. 

Companies should look for new ways to tap into the data on a regular basis. As a result, the need for data professionals, like Data Engineers and Data Scientists, has never been greater than now.  

Unfortunately, the increasing need for data experts and analyzing big data also translates into the growing confusion around data disciplines and their focuses.  

One of the most common misconceptions is based around the professions of Data Scientists and Data Engineers. In many cases, even in the most reputable companies, the name “Data Scientist” and “Data Engineer” are sometimes used reciprocally.

Nevertheless, is Data Engineering and Data Science the same? 

Or maybe is Data Engineering a part of Data Science and Data Engineers should belong to a Data Science team?

Read on to find out: 

• Is Data Engineering and Data Science the same? 
• What is the daily job of a Data Engineer?
• What does a Data Scientist do ? 
• What is the difference between Data Engineering and Data Science? 

Is Data Engineering and Data Science the same? 

Unfortunately, no. It would be too simple 😉 

When talking about Data Engineering vs Data Science, it should be emphasized that they are two separate disciplines that are often used interchangeably. In many cases, the reason behind this is the nebulous usage of titles of data professionals that vary between companies.  

Nevertheless, it should be made clear that they are distinct areas with their proper profiles, responsibilities and outputs. Both Data Engineers and Data Scientists are data-focused and should support businesses in getting valuable insights from generated data. Nonetheless, they are separate entities. 

Let’s look closer at each of them, compare their main features, and highlight the major differences between Data Engineering and Data Science.  

What is the daily job of a Data Engineer? 

In short, a Data Engineer builds, develops, tests and maintains data architectures.

Therefore, Data Engineering is primarily focused on: 

• developing the framework for processing, storage, retrieval, and validation of data from different sources for data analysis, 

• discovering the best ways, optimal solutions, and toolsets for streamlined data acquisition (creating free flowing data pipelines), 
• cleaning corrupt data and implementing the right data scheduling. 

Consequently, Data Engineer’s job is to lay the foundations, clean and transform data into a useful format for data analysis performed by Data Scientists and Data Analysts.  

Data Engineers implement appropriate hardware, software and programming languages to create APIs (Application Programming Interface) for large-scale data processing, query optimization and data scheduling.  

They start with a specific goal and the result of their work is to put together a functional system for data collection, like databases or large-scale processing systems, to make data consistent, available to users, and meaningful for business requirements. 

What does a Data Scientist do ? 

In brief, Data Scientists analyze, cleanse and organize big data using domain knowledge.

Data Science is still a thriving field with a booming job market expanding on a regular basis.  

In brief, Data Science is the process of extracting valuable business insights from underlying data by developing models and procedures by implementing various Data Science tools. Its focus is to exploit appropriate methods and tools from statistics, application domain, and computer science to process structured, semi-structured and unstructured data to get meaningful insights.  

As a result, Data Scientist role is to clean and organize data, monitor business processes, as well as use the knowledge for developing industry-specific analysis and intelligence models by using machine learning and statistics.

Data Scientists need to apply analytical, programming, and business skills and prepare visual or graphical representations of the underlying data.  

What is the difference between Data Engineering and Data Science?

As you can see, when we look at Data Engineering vs Data Science, they are separate disciplines with different focus and methods of reaching their goals.  

It is difficult to say which is better or easier – being a Data Engineer or a Data Scientist. They are equally important for businesses assisting them in gaining valuable insights from data, making better-informed decisions and staying competitive in the market.   

Let’s sum up the key features of Data Engineers’ and Data Scientists’ areas of expertise. 

Feature	Data Engineering	Data Science
Focus	Discovering opportunities for data acquisition.  Developing, building, testing, and maintaining data architectures (like databases and large-scale processing systems)   Cleaning corrupt data and data scheduling;	Cleaning and organizing data  Monitoring business processes   Performing descriptive statistics and analysis to get insights;
Work profile	Helping Data Analysis and Data Science teams by applying feature transformations for data analysis and machine learning models on the datasets;	Establishing and optimizing statistical and machine learning models to solve business needs;
Areas of expertise	Programming, middleware, and hardware-related knowledge Machine learning and statistical knowledge not obligatory;	Mathematics, statistics, computer science, and application domain   Hardware knowledge not required;
Responsibility	Optimizing and improving performance of whole data pipelines;	Optimizing performance of machine learning and statistical models;
Outputs	Data flow (you can read about dataflows here), storage, and retrieval system  Recommendations of ways to improve data reliability, efficiency, and quality;	Data product  Communication of findings to decision makers;
Technologies	Python, Scala, SQL, Apache Spark, Apache Kafka, Apache Airflow, AWS Kinesis, AWS Redshift, AWS S3, Snowflake, HDFS, Google Cloud Storage, Google Cloud Composer, Presto, Cassandra, DynamoDB, MongoDB, PostgreSQL, MySQL	Apache Spark, D3.js, SPSS, SAS, Stata, Julia, Jupyter Notebook, Keras, Matlab, Matplotlib, NumPy, Python, Pandas, PyTorch, R, Scikit-learn, SciPy, TensorFlow

Summary – Data Engineer vs Data Scientist professions

As you can see, when looking at Data Engineering vs Data Science, it needs to be said that they are two distinct professions, but they are related to one another. Both Data Engineers and Data Scientists address distinct data problem areas and require specialized skillsets and approaches to reach their goals.  

Data Engineers need to transform raw data so it can be later used by Data Scientists who develop machine learning data models on top of it. Although Data Scientists build models for data analysis and visualization, they are fully dependent on Data Engineers’ job to get processed and enriched relevant data that can help address business problems.  

Both Data Engineer and Data Scientist professions have numerous opportunities to expand with growing amounts of data produced and consumed, the advent of Internet-of-Things (IoT) and the Big data technologies arising. Consequently, both occupations should already be required in IT-based organizations and the importance of Data Engineer and Data Scientist jobs should not be undermined.  

* Source: https://www.aparavi.com/resources-blog/data-growth-statistics-blow-your-mind