The 21st century has witnessed a digital revolution, and at the forefront of this revolution is data and its rise in relevance and importance. Data has become the rightful currency of the present age, and this trend has led to a massive surge in demand for big data engineers and data scientists. With data science and data engineering becoming two of the most lucrative career options of the 21st century, many up-and-coming enthusiasts are left pondering upon one common question, data engineering vs data science: how’s one different from the other? This article gives an overview of the differences between the two.
Data Engineering and Data Science
Data engineering and data science complement each other, and one is definitely needed for the other to function smoothly. Data engineers design and develop ‘pipelines’ that transport and transform data into a usable form for data scientists. These pipelines are tasked with the responsibility of collecting data from various sources and then storing them in a single database that can represent the data as a uniform source of truth. Data engineering is necessary to support data scientists, analysts, and executives so that they can quickly and securely inspect all of the data available.
Data science is the practice of dealing with massive hordes of data using advanced tools, algorithms, and machine learning principles, all in an attempt to derive hidden meaning and decipher patterns from these trenches of data. A data scientist must be able to extract, manipulate, visualize, and maintain data. Data scientists must be proficient in programming languages and should also have a keen knowledge of machine learning principles.
Difference between Data Engineering vs Data Science
Although there are key similarities between data science and data engineering, there are certain aspects wherein they stand apart from each other. Let’s take a closer look at those aspects.
The job profile is a major factor that one must consider while pondering upon data engineering vs data science and the difference between the two. Data engineers build complex pipelines using big data tools and are tasked with designing, building, testing, integrating, and optimizing the data collected from multiple sources. Data scientists on the other hand decipher patterns from massive arrays of data and are tasked with answering key business questions such as optimizing business operations, reducing costs, etc. (Read more on Data Engineering Skills needed for Jobs)
Data science and data engineering have their own sets of pillars that act as a foundation and prerequisites for any aspiring job seeker. The three pillars of data science are computer programming, statistics and linear algebra, and machine learning and algorithms. The three pillars of data engineering are big data storage and processing, data pipelines, model ETL (Extract, Transform, Load). This also goes on to show that the skillsets required to become a data scientist and a data engineer also differ from each other.
The tools used are another important factor to be considered while understanding data engineering vs data science. Data engineers are known to use programming languages such as python, java, and Scala, along with data pipeline tools, and Big Data frameworks like Hive, Hadoop, Spark, etc. Data scientists also use programming languages such as python and Java but are more involved with advanced analytical tools and BI tools like Tableau Public, Rapidminer, and Splunk. Data scientists also heavily use ML libraries such as TensorFlow, Theano, PyTorch, Apache Spark to name a few. (Read more on Data Science Course Syllabus)
Data engineering vs data science is definitely not a question with one straight answer. Both play critical roles in companies, and both often end up complementing and supporting each other. We at Praxis understand that there is a massive need for well-endowed data engineers, and towards the goal of fulfilling this demand, have come up with India’s first post-graduate program in data engineering. Our 6-month long PGP comes with both online and in-class learning options and is topped off with exciting campus placement opportunities and amazing knowledge partners to give you the exposure and kickstart that you need and deserve.