Data Engineering and its Applications in Financial Markets – Part III

Learn about the responsibilities in the field of data engineering with Part I and Part II.

Let us now find out the difference between data scientists and data engineers.

Data Scientists Vs Data Engineers

Data Scientists

• Remain in constant interaction with data engineers who build the data infrastructure
• Act upon the data by making it come into use
• Utilise sophisticated machines to act upon the data or to make the data come into use
• Has a data pipeline which is basically the optimized data created by data engineer
• Do the research to identify trends and requirements of the enterprise they provide data for
• Use advanced analysis tools such as R, Hadoop and advanced statistical modelling

Data Engineers

• Provide the data infrastructure that can be used for the particular purpose of the enterprise. For instance, for trading, making business decisions etc.
• Need to build data with high performance which is reliable for the particular purpose of the enterprise
• Use tools like SQL, MySQL which support the tools used by data scientists
• Creates a data pipeline which implies optimized data for the data scientist to be able to use it
• Help the data scientists by maintaining the data infrastructure required by them to take practical actions. For instance, feeding the data to the machine learning model for trading etc.

The main point here is that you will be needing both a data scientist and data engineer to make the data sets function appropriately. Both are critical for any enterprise involved with the use of data sets for making important decisions.

Without a data scientist, a data engineer will not be of much help since a data scientist makes the data come into actual or practical use.

Similarly, with the help of a data engineer, the data will be built without errors such as wrong entries, duplicate data etc.

Let us now take a look at the future of data engineering further.

Future of Data Engineering

As technology is rapidly changing and advancing for the better, data engineering is also transforming completely. Ever since the Internet of Things (IOT), artificial intelligence, hybrid cloud computing etc. have made their entry into domains such as financial markets, data engineers also are expected to transform and learn to utilise the same for a better functioning.

It is expected that the data engineering services market is expected to rise to USD 77.37 billion by 2023 from USD 29.50 billion in 2017 according to research.

It is expected so because there is wide adoption of big data over the past few years. And, in the future, with more technological advancements big data requirements are expected to grow and dominate the market.

Conclusion

This article mainly discussed the basics of data engineering. Data engineers play a crucial role in any enterprise or for trading since datasets are the most important when it comes to making decisions. Moreover, the future of data engineering is bright enough with more technological advancements and the need for big data usage.

Visit QuantInsti for additional insight on this topic: https://blog.quantinsti.com/data-engineering/.

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