The ultimate Guide to Data Engineering.

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. This article aims at providing a step by step guide on how one can become a data engineer.
Most data engineers have a bachelors background in computer science or a related field where fundamentals such as cloud computing ,coding skills, and database design are taught.
To become a data engineer one should first focus on developing data engineering skills such as;
Coding. Common programming languages applicable in data engineering are SQL, NoSQL, Python, Java, R and Scala. Proficiency in these languages is essential to this role
Relational and non-relational databases. Databases rank among the most common solutions for data storage.
Extract, transform and load(ETL) systems. This is the process by which data is moved from data bases and other sources into a single repository like a data warehouse.
Big Data Tools. Data engineers don't just work with regular data. Tools and technologies are evolving and vary by company , but some popular ones include Hadoop, MongoDB, and Kafka.
The second step is getting certified. Certifications validate ones skills to employers. The common certifications include Big Data Engineer, Cloudera Certified Professional Dat, IBM Certified Data Engineer or Google Cloud Certified Professional Data Engineer.
Building a portfolio of data engineering projects. A portfolio is often a key component in a job search as it recruiters, hiring managers and potential employers.

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