Transitioning from data engineering to data science would require one to get well versed with Statistics, Machine learning and enhance Programming skills (along with ML specific libraries).

To your question on the difference between the two, data engineering focuses on working with databases and ETL (extraction, transformation and loading)process. While Data Scientist consumes these databases to primarily analyze the dataframes to perform insight generation and implement statistical/machine learning algorithm as per the objective of the use case.

Both the streams are different in terms of their scope, implementation and skills required.

Talking about the growth, both the work roles have emerging growth, since both plays a crucial role in the ecosystem of data. And it's very subjective to comment on the same, as its highly dependent on changing scenarios of industry trend and demands.

I hope that answers your questions. Thanks!

Let's take analogy to understand. If you want to cook a dish, you need to source all the raw material which is ingredients to prepare the dish. Assuming you went and got all the required things(Data Engg) and now someone else in your family is cooking the dish. (Data Scientist). Both are dependent

So, Data Engineers demand will never be faded and would be required more than a DS. Companies are collecting massive amount of data and they need people to bring that data in right format, build data pipelines, all ETL related task, choosing between SQL, NOSQL and blob storage depending on the data and of course cloud skills. If you feel this is the right bucket for you chose Data Eng path.

On the other hand if you are more interested in solving business problems, have domain knowledge, Machine Learning or DL and mathematical knowledge, statistics knowledge and this work gives you kick, go for DS path.

Data engineering is the future. Gone are the days when you would just work with Databases and pipelines. DE as of today has carved out many branches and there is some exciting stuff going on in this field.

1. Real-time data processing.
2. Cloud Computing
3. Change Data Capture (CDC)
4. Data governance - Data dictionary, Data Lineage, Data Observability

These are few of the points that the current DE teams are working on. Refer to this article for more in-depth analysis - https://medium.com/@shenoy.shashwath/the-future-of-data-engineering-navigating-the-data-path-d284671475a0

Hi Krishan,

Data engineers build and maintain the systems that allow data scientists to access and interpret data.

Data scientists then clean this data and use it to build and train models that solve a certain problem.

Both fields have a very good growth potential. If we are talking from a "landing the job" perspective, data science is tougher to get into.

Hope this answers your question.

Best,

Aayush