AWS Certified DevOps Engineer - Professional

Data Transformation

Raw data often needs to be cleaned and transformed before it can be used for analysis. Transformation may involve removing duplicate records, handling missing values, changing data formats, joining datasets, filtering information, or creating derived fields.

AWS provides managed services that can support data transformation and integration workflows.

Data engineers need to design transformation processes that are reliable, scalable, and maintainable. Automated pipelines can reduce manual processing and improve consistency.

ETL and Data Pipelines

ETL stands for Extract, Transform, Load. It describes a common approach to data integration where information is extracted from source systems, transformed into a suitable format, and loaded into a target data store.

Modern data architectures may also use ELT, where data is loaded first and transformed later.

AWS data engineering solutions can support both approaches depending on the architecture and workload.

Well-designed pipelines should handle errors, retries, monitoring, scheduling, and changes in source data.

Data Processing

Data processing converts raw information into useful datasets. Processing can involve filtering, aggregating, joining, sorting, and transforming large amounts of information.

The choice of processing technology depends on the workload. Batch processing may be appropriate for scheduled analytical jobs, while streaming technologies can be used when organizations need to process data continuously.