The volume of data generated each day will not cease. The universe is said to generate approximately 463 exabytes by the end of 2025.
With the amount of data soaring, the need for data engineers is only going to rise.
Every organization needs a data engineer, an engineer who can help the organization move forward and process the data. On the frontline, we’re talking about engineers who can create data architectures, develop data set processes, conduct research for business questions, prepare data for predictive modeling, find hidden data patterns, etc.
The demand for data skills is going to surge in the post-pandemic era. With the projected increase in data amid the crisis, it is only wise to upgrade skills and stay relevant for the future.
With many organizations allowing work from home, this can be the right time to upskill in the trending tools and technologies. Data engineer program and data engineer certification programs are great options to start with. However, before getting into any of these programs you must ensure you have a structured learning plan – what to learn, which platform to choose, and what are the skills you would first like to take up?
Here’s a typical learning path you can follow:
- Gain proficiency in programming – programming skills lay a foundation in data engineering. Data engineers are at the intersection of data science and software engineering. If you’re looking to get into the data engineering field you need to first become a software engineer or learn software engineering. Therefore, getting in-depth knowledge and practical knowledge of these foundational skills is a must. The data industry revolves around the two major programming languages Python and Scala.
- Acquire knowledge in automation and scripting – automation is important for data engineers as most of the task they’ll be handling is tedious. Shell scripting is one way to tell the server (UNIX) what to do and how to do it. From shell scripts, you can easily start Python programs and run a task on Spark. Whereas CRON is a time-based scheduler that helps in marking when a specific job needs to be taken care of.
- Understand your databases well – you can start by learning the basics of SQL. SQL is known to be the lingua franca of everything related to data. Besides this, you need to learn how to model data. At times, you may find that your data is not structured, but stored in a less structured manner in a document base – MongoDB. There are renowned data engineer certification programs that give hands-on training.
- Data processing techniques – you need to learn how to extract data from different sources to process it, this technique is called data processing technique. If your dataset is small you might be able to process your data in R using dplyr or maybe in Python using pandas. However, if it a heavy dataset like gigabytes or terabytes you might need to do parallel processing.
- Learn how to schedule your workflow – once you have completed data processing, you would need to schedule them on a regular basis. If you’re looking to keep it simple, you may use CRON or you can use Apache airflow, a tool that will help schedule workflows in a data engineering pipe.
- Cloud computing – earlier companies had servers to handle data and some still follow the traditional method, however, with the help of cloud computing you can store as much data or application as you like. Cloud computing is efficient, scalable, cost-efficient, secured, etc. some of the best cloud computing services are provided by Azure, AWS, and Google cloud platform. A data engineer program may be able to teach you everything about cloud and how they function.
- Internal infrastructure – as a data engineer you need to know a tool or two about internal infrastructures. Docker and Kubernetes are two major tools you need to know.
You can now follow these simple steps and get ahead with your data engineer career.