Which option accurately differentiates among a database, a data warehouse, and a data lake based on each system's primary purpose in an organization's data strategy?
Databases handle day-to-day transactional workloads on structured data, data warehouses store integrated historical data for analytical queries, and data lakes keep large volumes of raw or semi-structured data for future exploration.
Databases ingest streaming data in real time, data warehouses support high-throughput row-level updates, and data lakes keep strictly curated datasets with fixed schemas.
Data lakes are optimized for online transaction processing, databases store historical analytics data, and data warehouses ingest raw sensor files with no defined schema.
Data warehouses are designed primarily for low-cost storage of videos and images, databases generate batch reports, and data lakes power interactive dashboards for business users.
Operational databases are engineered to support high-volume, low-latency transactions on well-defined schemas, such as order processing or customer records. Data warehouses aggregate cleaned, structured history so analysts can run complex SQL queries and produce business reports at scale. Data lakes provide inexpensive, scalable storage for raw or semi-structured files-logs, images, or sensor data-that may later be curated for analytics or machine-learning workloads. The correct option reflects these common roles, whereas the distractors swap purposes or describe capabilities that do not align with industry-standard definitions.
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What is the primary purpose of a database in an organization's data strategy?
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What is the difference between a data warehouse and a database?
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How does a data lake differ from a data warehouse?
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What is the main difference between a database and a data warehouse?
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What is the role of a data lake in an organization's data strategy?
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Why are schemas important for databases and less critical for data lakes?
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