What is the main difference between data science vs data engineering?

Data Science is a vast & comprehensive topic of study that incorporates domain knowledge in business, mathematics, statistics, CS, and information science. It focuses on using scientific techniques, methodologies, procedures, and algorithms to extract significant patterns & insights from massive datasets. Big Data, ML, and Data Mining are the basic elements of data science.
On the other hand, Data Engineering is an amplitude of Data Science that focuses mostly on the real-world uses of data collection and analysis. It focuses on creating data pipelines that can gather, prepare, and transform data (both structured and unstructured) into consumable forms for data scientists to review.
In order to gather, store, clean, and process data in real-time or in batches and get it ready for more analysis, data engineering makes it easier to build the data process stack. Data engineers essentially develop the support systems used by data scientists.
We must first discuss the similarities between the two profiles of data engineers and data scientists before delving into their differences. Their educational backgrounds are the most significant resemblance between the profiles of Data Engineers and Data Scientists. Both professionals typically have backgrounds in mathematics, physics, computer science, information science, or computer engineering. For Data Science job descriptions, these academic fields are overwhelmingly recommended. Programmers with expertise in languages like Java, Scala, Python, R, C++, JavaScript, SQL, and Julia make up both data engineers and data scientists.
Focus is the primary distinction between data engineers and data scientists. The infrastructure and architecture for data collection are built by data engineers, whereas data scientists are primarily focused on doing sophisticated mathematical and statistical analysis on the gathered data.
Data engineers, as was already mentioned, create, test, integrate, and optimize data gathered from various sources. They build open data pipelines that enable real-time analytics applications on complicated data using Big Data tools & technology. To make data more reachable, data engineers also create challenging queries.
However, data scientists are more concerned with providing answers to important business concerns, such as how to improve customer experience, cut expenses, and optimize corporate operations. Data Scientists formulate pertinent inquiries, look for unobserved patterns, construct hypotheses, and then draw appropriate conclusions using the data format provided by Data Engineers.

When it comes to data science vs data engineering, Both of them are the most demanding careers of the 21st century. Before answering this question about data science vs data engineering, go through them below as I have mentioned.
A Data Scientist builds models using mathematics, statistics and machine learning to explain and predict complex behavior, and codifies those models into real-world software. A Data Engineer designs and builds data architectures for ingestion, processing, and surfacing data for large-scale data-intensive applications.
Often the Data Scientist and Data Engineer will work together to build an end-to-end solution for companies requiring advanced analytical models that are operationalized at scale.
As with the Data Scientist there is no formal path to becoming a Data Engineer since it is a unique blend of skills that have been brought together to form a distinct and much needed discipline. The requirements for a Data Scientist are typically more “academic” as they are expected to understand and conduct scientific research and know how to build and test advanced models. PhDs are often sought for Data Science, with backgrounds in the hard sciences or computer science. Data Engineers typically come from an engineering background with less emphasis on the academic background, although many still have Masters degrees. Often developers interested in designing large scale architectures for data-intensive applications can move towards this field as there is much less emphasis on science and math and more on engineering and development.
Data Engineers should understand the core concepts in computer science and should be very well versed in building and designing large scale applications; end-to-end. They should understand the pros and cons of using relational and noSQL databases. They must know how to design effective pipelines for both batch and streaming use cases. They must know what it takes to operationalize a working model and how to help push some of the “lab” specifics (training and validation) into real-time engines. They must understand distributed computing and should be able to work with the Data Scientist to help split algorithms effectively to still yield predictive accuracy across a variety of domains. They should know when to push schemas towards the application to allow for “data lake” designs that assist in large scale analysis but still serve domain-specific applications. And they should be very familiar with the core technologies that are used to build these systems.
I Guess you might have a clear insight about data science vs data engineering.