AWS Certified Data Engineer - Associate

Storage design should consider data formats, lifecycle requirements, access patterns, security, durability, and cost. Appropriate organization and metadata can make data easier to discover and process.

Data Lakes and Data Warehouses

A data lake is a centralized environment for storing large amounts of structured, semi-structured, and unstructured data. AWS provides technologies that can support data lake architectures.

Data warehouses are designed primarily for analytical workloads and structured data. They enable organizations to query large datasets and generate business intelligence.

Data engineers need to understand when different storage and analytics approaches are appropriate. Architecture decisions should be based on data volume, query patterns, performance requirements, governance, and business objectives.

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.