Data Engineering involves designing, building, and maintaining systems and architectures for collecting, storing, processing, and analyzing large sets of data. Data engineers ensure that data is accessible, reliable, and efficiently processed for analytical and operational use.

Basic understanding of programming languages such as Python or SQL

Familiarity with databases and data modeling concepts

Some knowledge of cloud computing platforms (e.g., AWS, Azure, GCP) is helpful but not mandatory

Introduction to Data Engineering

Programming for Data Engineering (Python, SQL)

Data Warehousing and ETL Processes

Big Data Technologies (Hadoop, Spark)

Cloud Data Engineering (AWS, Azure, GCP)

Data Pipelines and Workflow Orchestration

Data Storage Solutions (SQL, NoSQL, Data Lakes)

Data Integration and APIs

Job roles include Data Engineer, Big Data Engineer, ETL Developer, Data Architect, and Cloud Data Engineer. Data engineers are in high demand across various industries including tech, finance, healthcare, and retail.

Data Engineering focuses on the architecture, systems, and processes for collecting, storing, and processing data. Data Science focuses on analyzing and interpreting complex data to help inform business decisions. Data engineers build the infrastructure that data scientists use for analysis.