Explore the World of Data Engineering: Types and Application in Financial Markets

What is Data Engineering?

Data engineering is a field in which data preparation meant for analysis in the enterprise takes place. Data preparation, here, implies the individual who constructs and tests the data. This process leads to such data which can be used productively for implementing in the analysis required by a particular enterprise. This data ready for the use builds the data architecture.

Data engineers are experienced to develop and manage large volumes of data. Also, one of the main responsibilities of data engineers is to aid data scientists to convert raw data into clean and usable data.

Raw data, here, implies the data which is extracted directly from the source and can consist of several issues such as duplicates, non-stationarity etc. Clean and usable data means the data which is ready for being used for various purposes in trading such as backtesting, analysis and forecasting the trades in the future.

Next, we will find out the responsibilities in the field of data engineering.

Responsibilities in the Field of Data Engineering

Data engineering is usually done for providing the enterprise with accurate data and requires proficiency in the programming languages such as Python, Java etc.

Simultaneously, data engineers have the following responsibilities:

• Support data scientist/analyst
• Manage data
• Divided into three types of data engineers
• Keep evolving

Support data scientist/analyst: Data engineers support the data scientist/ analyst in carrying out the operations based on the optimised data. Data engineers are mainly responsible for creating and maintaining the data infrastructure.

Manage data: Data engineers are basically needed to manage the data also. Their responsibilities do not end at creating the optimised data for professional use. They also need to manage the data further which implies making sure there are no further errors, is easily accessible and reliable.

Divided into three types of data engineers

There are usually three types of data engineers, namely:

• Generalist
• Pipeline-centric
• Database-centric
• Generalist

There are some data engineers who do all the work of creating the data pipeline such as retrieving the data from the sources to processing it and doing the final analysis. This procedure takes up the entire skillset of a data scientist as well. This is required for small companies or the teams which do not have much of the staff for specialization.

Pipeline-centric: They are required in mid-sized companies which have complex data needs and need the data team to conduct a lot of work that requires the background of distributed systems and computer science.

Database-centric: These data engineers are usually found in large companies with their data distributed across the databases. There are various data analysts in such companies and the data engineers are required to pull the information from the main application of the database into the analytics database.

Keep evolving: Data engineers keep evolving with technological advancements and the introduction of various models with that. The data engineering domain is progressing at a rapid pace, powered by disruption in Internet of Things (IOT), Artificial intelligence and machine learning models. Hence, data engineers also need to keep evolving and learning new practices in the field.

Going forward, we will find out about data engineering in the financial markets.

Data Engineering in the Financial Markets

In the financial markets, a data engineer needs to do the regular work of collecting data, cleaning the data which implies taking out errors such as duplicates. The last step is automating the trade with the help of the cleaned data.

Moreover, there are certain other ways in which data engineering helps the financial markets. These are:

• Risk management
• Predictive analytics
• Fraud detection
• Algorithmic trading

Risk management

Since managing the risk is an extremely important aspect of any financial institution, data engineers play an important role. With the help of clean data sets, the errors in the prediction of trades do not take place. This is important because if the machine learning model gets fed with the erroneous data, it will lead to continued losses for the investor.

Predictive analytics

With the help of predictive analytics, the investor can foresee the data patterns and can take the right actions for the same in the present. A data engineer helps the firm/individual etc to take the right decisions while investing in the financial market this way. For instance, if the machine learning model is fed with data which has duplicates or irregularities, it will lead to erroneous input. This erroneous input, in turn, will make for false predictions in the trade and hence, less gains.

Fraud detection

Going forward, data engineering also helps with fraud detection. As it is extremely important to detect if a hacker has hacked into the system to make the data malicious/unfit for feeding the predictive model, it is a must to get the same checked and cleaned by the data engineer. 

Algorithmic trading

In the algorithmic trading domain, data engineers help with the cleaning of the data which is to be fed to the machine learning or deep learning models for predicting the trades. An algorithmic trading system executes the orders with the help of pre-programmed instructions. While doing so, the data is required for historical backtesting which helps to understand if the created strategy would have worked well on the past data or not. While doing so, if the data fed for backtesting is not looked into properly, it can lead to wrong trading decisions in the future. 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 modeling

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 need both a data scientist and a 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 utilize the same for 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 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.