Data Engineering Concepts: From Basics To Advance Techniques

Data engineering is a field that focuses on designing, building, and maintaining data systems. Data engineers work with large amounts of data and are responsible for ensuring that it is accessible, reliable, and secure. They use a variety of tools and techniques to extract, transform, and load data into data warehouses and data lakes.
One of the key tasks of a data engineer is to design data pipelines. Data pipelines are a series of steps that data goes through to be processed and analyzed. These steps may include data extraction, data cleaning, data transformation, and data loading. Data engineers use tools like Apache Kafka and Apache Airflow to automate these processes.
Data engineers also work with data storage systems. Data warehouses are large repositories of data that are optimized for analytical queries. Data lakes, on the other hand, are less structured and can store a wide variety of data types. Data engineers use tools like Hadoop and Apache Spark to manage and process data in these systems. In addition to data pipelines and storage systems, data engineers are responsible for data quality and governance. They develop data quality checks to ensure that data is accurate and consistent. They also implement data governance policies to protect sensitive data and comply with regulations.