5 Reasons Why You Should Learn Data Engineering
Data engineering is the design and development of systems and infrastructure that automatically collect, store, and analyze large amounts of data. Its goal is to develop methodologies and tools that ensure maximum perfection of processes, stability, and reliability of information. This is achievable through programming.
A data engineer is a software engineer specialized in data technologies. They build systems where any employee of the company can easily access the data they need. Thus, they set up tools that the data team and their company need for success. It is data engineers who create the foundation of the modern data ecosystem.
If you know the Python programming language and are attracted to working with data, this profession will interest you. Read the blog and find out the 5 reasons why you should choose data engineering.
1. Data engineering is the backbone of data science
Data engineers are the first to receive large amounts of structured and unstructured data that enter the company’s systems. Therefore, they are the foundation of the data strategy. It is data engineers who start working with them and ensure their collection, storage, and transformation. The more efficiently they work, the easier it becomes for other team members (data scientists and analysts) to conduct subsequent processes, and vice versa.
Consequently, a lot depends on the work of data engineers. This is confirmed by the principle of how companies with well-developed strategies staff their data teams. Usually, in such organizations, we find this ratio: for every data scientist, there are 2-3 data engineers. In companies with more complex data, this number equals 4-5 engineers.
Data engineering is full of technical challenges
The essence of data engineering is to develop functions that will be large-scale and effective. This helps other team members focus directly on the data and devote time to their analysis, rather than trying to solve programming problems. Moreover, data engineering requires less mathematics than data science. Therefore, if programming is your strength and you prefer it to mathematics, this direction is the ideal option for you.
A respected and valuable profession
Making life easier for data scientists is not the only result that motivates data engineers. They also have a growing influence on ongoing processes in the world.
We create 2.5 quintillion bits of data daily. Due to their scale, the importance of data engineers has never been as great as it is today. Their need will increase even more in the future. According to Business Insider, by 2025 there will be 64 billion IoT devices. For comparison, in 2018 this number was 10 billion. This trend indicates the multiplication of data from more sources. Consequently, the demand for data engineers will increase significantly in the future for their effective processing and storage.
This means that data engineers have a diverse path to discover their areas of interest and deepen relevant skills. Many tools and technologies available today help them in this. For example, popular tools such as Amazon Redshift, Amazon S3, Apache Cassandra, Apache HBase, Apache Kafka, Azure, PostgreSQL, Redis. Of course, it’s not necessary to know how to use each tool, but this list shows well how versatile the world of data engineering is. And which technique you use to perform tasks depends on your desire and choice.
Since data engineers also possess software engineering skills, they can also create digital products. If you want to participate in startup development or become an entrepreneur and start your own company, learning this profession will definitely be useful. The techniques needed for data engineering will help you both create a digital product and analyze its operation.
If working remotely is more comfortable for you, data engineering is a good choice for this. Most tasks don’t require being in the office. Due to the high demand for specialists, it’s definitely possible to find a remote job or work on short-term private projects. In addition, you can contribute to the development of the data science community. On average, 65% of professional developers post Open Source projects on Stack Overflow once or more per year. So, you too can make changes and develop and refine new techniques for people working in the field.
High income
It’s true that salary shouldn’t be the only decisive factor when looking for a job. However, we can’t deny that it still plays a big role in this process. The average income of data engineers will not leave you indifferent – this number is equal to $117,000.
This data is not surprising at all. The skills needed for data engineering, such as Python, SQL, are among the highest-paid knowledge. According to LinkedIn, there are many more job postings for data engineer positions (up to 112,500 vacancies) than for data scientists (up to 70,000 vacancies). And the demand is very growing – in 2019 it increased by 88% compared to the previous year. This trend will definitely continue in the future.
Learning data engineering is valuable even if you don't plan to continue your career in this profession
Even if you don’t want to start working in data engineering and are more attracted to the position of data scientist, it will still be beneficial for you to gain knowledge in this regard:
As a data specialist, you will often have to perform tasks that correspond to the role of another position, including data engineering;
Learning different approaches will help you better understand the issue. Also, you will be able to improve skills that you rarely use in your daily activities;
You will become more confident, which will also reflect on your career advancement;
Communication with data engineers will be easier for you, which is the key to effective collaboration.
As you can see, data engineering is a field full of adventures. It will never lack new and interesting challenges. So, if you want to have one of the most in-demand and highly paid skills in the future, start learning data engineering. Join us at Commschool!
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