Which of the following best describes what a data lake architecture typically involves?
Data lakes are designed to store massive volumes of raw data in various formats and are commonly built on cloud infrastructure to provide scalable, elastic storage. They rely on parallel (distributed) processing engines—such as Apache Spark or Hadoop—to efficiently query and process large datasets across many nodes simultaneously, rather than relying on batch-only processing or a fixed relational schema as in an RDBMS. This distinguishes data lakes from traditional data warehouses, which often use structured schemas and batch ETL processing.
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