4. Data engineering is more practical
It is one thing to design and create a data warehouse or a database. More important than that is to make them efficient, scalable, durable, and fast. Thus, there is a lot of practical work for data engineers.
The tech stack for data engineers contains many tools. Amazon RedShift, Google BigQuery, Hadoop, Spark, Kafka, SQL and NoSQL databases, GraphQL, Airflow, Kafka, Python, and Scala are some of the tools used by data engineers.
Data engineers use these tools to engineer a system to provide a data pipeline. They usually handle an enormous amount of data so the data pipeline vigorous, stable, and scalable. Last but not least, the data needs to be accessed fast. It is obvious that a system that can handle such operations requires lots of practical work.