Data engineering is the process of designing, building, and maintaining the infrastructure that supports the storage, processing, and analysis of data. This includes everything from data quality and ETL (Extract, Transform, Load) processes to data warehousing, data checks, and more. The goal of data engineering is to ensure that data is accurate, consistent, and readily accessible for analysis.

Data quality is a critical component of data engineering. This involves ensuring that data is complete, accurate, and consistent across all sources. Data engineers use a variety of techniques to validate and cleanse data, such as outlier detection, data profiling, and data standardisation.

ETL processes are also a key part of data engineering. These processes involve extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse or other storage solution. ETL can be a complex process, requiring the use of specialised tools like Wherescape RED, Azure Data Factory, or SQL Server T-SQL.

Data warehousing is another important aspect of data engineering. A data warehouse is a centralised repository of data that is used for reporting and analysis. Data engineers design and build data warehouses, ensuring that they are scalable, reliable, and able to handle large volumes of data.

Data checks involve verifying that data is accurate, consistent, and complete. Data engineers use a variety of tools and techniques to perform these checks, including data profiling, data validation, and data auditing.

Tools like Wherescape RED, Azure Data Factory, SQL Server T-SQL, and Oracle PLSQL are commonly used in data engineering. These tools help data engineers to design and build data pipelines, automate ETL processes, and ensure data quality and consistency.

Data engineering is the process of designing and building the infrastructure that supports the storage, processing, and analysis of data. It involves a range of activities, including data quality, ETL, data warehousing, data checks, and more.

I have listed some favourite study guides and books here, which are useful for a lot of aspects of data engineering.