The road that leads to the Mansion for Data Engineers

According to LinkedIn, the salary for the Data Engineer role in the United States USD 99,000 per annum (Range from $65K – $1.6Lakh). A data engineer is a professional who facilitates and acts as a gatekeeper for movement and data storage. To stand firm in this position, the data engineers design, construct, install, test, and maintain scalable data management responsibilities.  In addition to the above, it is essential to understand and learn the skills that create an impression of a data engineer. The below-given details take you on the path that leads to the job of data engineering.

1. Having a good knowledge of computer programming

At the point of intersection between software engineering and data science are located the data engineers. Therefore, it can be stated that if someone holds the objective for being a data engineer, she has to be a software engineer first. It is, therefore, essential to know and practice the primary programming languages. The requirements of the organizations mainly revolve around the two crucial languages: Python and Scala.

Learning Python

Python should be known as the programming language, but it is equally essential that the data engineer know how to create software using it. High-quality software is well-structured, tested, and performs well. This means that the correct algorithm has been applied for the job. The knowledge that one gain is essential for the solid basis for writing efficient and testable codes.

Learning the basics of Scala

A good level of tooling exists in the data engineering world, which revolves around Scala. Scala is based on a static typing approach and a good functional programming base. It operates on Java Virtual Machine (JVM) and means that it has high compatibility with many Java libraries in the open-source platform.

2.  Having a sound knowledge of automation and scripting

Automation is a crucial area of work and study for data engineers. Many tasks that are required to be performed on the data need to be frequently repeated. This turns the task tedious to conduct. For instance, data engineers will be asked to clean the database and table many times. If it is realized that the automated job will require a lot of time, it is highly recommended to automate that process.

There exist several essential tools for creating automation. Shell scripting is an approach applied by data engineers to inform UNIX server what to do and when to do.

CRON is a time-based scheduler of a job. It comprises a specific notation that must be marked at the time of performing a particular job. For example, Apache Airflow is a tool that functions based on scripting capabilities to schedule the jobs to be completed.

3.  Being knowledgeable about databases

It is crucial to start with the knowledge of SQL basics. SQL is the lingua franca for the entire data. It is a popular language, and there are no chances that it will turn outdated in the coming time.

The reason behind why SQL is such an important one is that its coding makes it a declarative language. This means that the coding as in SQL offers details of not how to do it but the approach of what to do. The “query plan” is a sound one and a caretaker in this unique role. This also implies that any person can depict the meaning of the code. This also includes those who have no knowledge base of SQL.

SQL provides many dialects. The data engineer need not know all of these dialects; however, it is always good to know PostgreSQL and MySQL.

4.  The good knowledge base in the task of modeling data

To become a data engineer, it is essential to understand how one can model the data. Data models offer knowledge on how entities in the provided system interact and the primary material they are made up of. In short, data engineers should be able to read the database diagrams.

The data engineer should be able to spot the methods like data normalization or star schema. She should also be aware that some databases are better for transactions, and others can better analyze the data.

There are times when the data engineer can find that the data is not the structured way. Thus, in this case, it is essential to gain knowledge on how to understand the data, which is less structured.

5. Good knowledge base in data processing techniques

Data processing is a critical role and responsibility of a data engineer. In such a situation, the engineers must get the data from several sources and then conduct processing of it. If the datasets are not so big, you can win the battle with the application of Python with Pandas or R using dplyr. Or the data engineer can choose to use SQL engine for the critical role. But if the data is in terabytes or gigabytes, parallel processing can be applied to meet the need. The benefits of parallel processing are that more processing power can be used for the tasks, there can be a better application of memory on all platforms (for processing). For example, Apache Spark is the most commonly applied system for parallel processing.

Concluding remarks

Data engineer certification can be done online from this highly recommended Data Science Council of America). Your position shall be strengthened for any data engineer job using the above certification body. I wish you all the best!

Data engineers should be good in computer programming, automation, scripting, modeling, databases, and data processing. big data engineer, data scientist certification online

The data engineer should be able to spot the methods like data normalization or star schema. She should also be aware that some databases are better for transactions, and others can better analyze the data.


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