Organizations often face a critical decision: should they use a data lake or a data warehouse? A data lake stores raw, unstructured data (images, logs, IoT signals), while a warehouse is optimized for structured analytics. Modern data engineers often work with hybrid architectures that combine both. For instance, companies ingest raw logs into a lake (AWS S3, Azure Data Lake) and transform them with Spark before sending cleaned data into a warehouse for business analysts.
Data Lakes vs. Data Warehouses: What Engineers Should Know
