Data Engineers vs ETL Developers

Introduction

We live in a world in which a wide variety of data constantly circulates. To manage them, humanity has already come up with many tools and continues to create new ones. As a result, there are also too many data management tools themselves. Blockchain technology has also contributed to the increase in data volumes and the developers were faced with the task of not only systematizing constantly updated information, but making the entire data management process accessible in real time. Since any information is the basis for the processes of analysis and decision-making, participants in blockchain networks must constantly have this opportunity.

They need this in order to monitor transactions, make forecasts, calculate fraudulent actions, etc. There are different ways to manage blockchain data, as well as different purposes for company projects that require this data. Such tasks are not always simple and easy to implement, especially in a short time. And since today the practice of outsourcing various processes has proven itself very well, the blockchain data management process can also be transferred to a blockchain data provider. Such a step creates a lot of advantages for companies that work with blockchain projects, and therefore can be considered as the most acceptable and cost-effective.

Two specialties with similar requirements

Getting acquainted with the latest methods of data management, including data from blockchain projects, you can see that, among other tools and methods, such a technique as ETL is constantly mentioned. Let us immediately note that today this technique is no longer innovative, just like the profession of an ETL developer, which has gradually transformed into a more universal specialty – a data engineer. However, many tools for working with data, both within the methodology itself and outside of it, have become firmly established in everyday information management processes.

Speaking about the profession of a data engineer, it should be noted that the requirements for his competence include the ability to work with modern technologies and tools, such as Python, Azure, AWS, Spark, etc. Of course, similar requirements were also typical for ETL developers, therefore, by and large, the latter can also work as data engineers. In fact, both ETL developers and data engineers carry out the same algorithm of actions – data integration, loading and transformation – and have an identical range of responsibilities, which includes the following steps:

• understand the desire of the customer (business);

• collect initial data;

• determine the structure of the data;

• specify the frequency of data loading;

• determine appropriate tools for loading and unloading data, for their transformation and storage;

• create a data pipeline taking into account business requirements;

• implement process automation;

• create a data model for storing and accessing them BI;

• train users in the data management process.

It should be noted that in the list of responsibilities listed above, there is one position missing, which is inherent mainly in the functions of a data engineer. Unlike ETL developers, data engineers sometimes need the ability to scale data site models, that is, they help receive data, process and run it.

Knowledge and skills that are required

If you analyze the requirements of companies-employers for the skills that a data engineer should have, you can systematize them into a list of 11 main positions. It should be noted that there is a group of basic skills that not only a data engineer, but also an ETL developer should have. So, basic skills should include:

1. Knowledge and ability to work with the SQL programming language
2. Knowledge of Business Intelligence methods and tools for processing information obtained from transaction results
3. Ability to work with analytical data warehouses – traditional and cloud
4. Thorough knowledge and use of ETL and ELT tools for data integration, sometimes requiring documented evidence of this knowledge
5. Possession of system administrator skills, because ETL tools are often installed on a server, where the ETL developer needs to be able to connect using the command line.

The following are the higher level skill positions and data engineers should have them :

6. Ability to work with big data systems, structured or unstructured
7. Knowledge and ability to work with additional programming languages Python, Scale, etc.
8. Ability to use cloud technologies

The last positions are even higher level requirements for a specialist at the level of data engineer plus machine learning engineer

9. Knowledge of basic statistics and mathematics
10. Understanding Data Science approaches (regression, decision trees)
11. Ability to use machine learning tools (neural networks, NLP, Computer Vision)