Data engineers create solutions based on raw data. Developing, building, testing, and maintaining data architecture is what constitutes their job.
Many raw data sources contain errors and anomalies, including duplicates, incompatibilities, and mismatches. After conducting a deep analysis, their task is to offer suggestions for improving the quality and reliability of the data.
Steps to becoming a data engineer
If you’re considering a career in data engineering, having prior knowledge of a few key things will help:
- Learn the fundamentals of data storage and structure
- Learn the basics of SQL
- Learn RexEx
- Get familiar with the JSON format
- Good understanding of Machine Learning
- Knowing a few programming languages (Python, Java, Scala, Go, etc.) is also important.
What tools do data engineers use?
It is always helpful to have a basic understanding of database architectures. MongoDB and Microsoft SQL Server for example are useful tools for all data engineers.
Certifications from the big cloud providers are probably the best way to both educate yourself in data engineering and demonstrate your abilities to employers.
With the following data engineering learning paths, you can acquire updated, proven, detailed knowledge of everything you need to learn data engineering.
- AWS Data Analytics Specialty
There are five domains covered in this learning path. You will learn how AWS data analysis services work together. AWS data services are also explained in the context of data storage, processing, visualization, and storage.
- Microsoft Azure Data Fundamentals
Both technical and non-technical persons can take this course and demonstrate their knowledge about core data concepts and how they are implemented with Azure data services.
The course covers basic data concepts, relational and non-relational Azure data, and how to describe an Azure analytics workload.
- Google Data Engineer Professional Certification
By completing this certification learning course, you can learn to work with Google’s managed cloud data warehouse, BigQuery. During the course, you will learn how to load, query, and process big data. The course teaches you how to use machine learning for data analysis, build data pipelines, and use BigTable for large data applications.
Big Data Engineering
In big data engineering, you typically work with databases and data processing systems that span large computing environments. The environments are often cloud-based, taking advantage of the scalable, distributed features of cloud-based solutions, as well as turnkey deployment, which makes development and deployment faster.
Data Engineers Vs Data Scientists
There are distinct tasks and skills required for both data scientists and data engineers.
Engineers design, test, and maintain data. A data scientist organizes and manipulates that data for insight. An engineer creates data that scientists can work with.
In order to achieve successful results, both roles are highly important and require mutual respect and collaboration.