AWS Certified Data Engineer - Associate

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.

Data engineers should understand processing requirements and select technologies that provide the required scalability and performance.

Database and Data Store Selection

AWS provides multiple database technologies, and selecting the correct one is an important responsibility for data engineers.

Relational databases can be suitable for structured transactional data, while NoSQL databases can support flexible schemas and high-scale applications.

Data engineers may also work with analytical data stores, object storage, caching systems, and specialized databases.

The right choice depends on data structure, access patterns, scalability, consistency, latency, performance, and cost.

Data Quality