B. Choose a system to store structured and semi-structured data that supports ad-hoc analysis and custom reporting.
Explanation:
- A data warehouse is designed for analytical workloads, such as querying, reporting, and business intelligence tasks.
- Modern data warehouses (e.g., BigQuery, Snowflake) can store and analyze both structured and semi-structured data (e.g., JSON, CSV) and support ad-hoc analysis and custom reporting.
- This flexibility makes them ideal for the organization's requirements.
Why not the other options?
A. Import data into a semi-structured time-series database:
Time-series databases are optimized for time-stamped data, like IoT or log data, not for general-purpose data warehouse analysis.
C. Copy unstructured data into a single large object store:
Object storage (e.g., Google Cloud Storage, AWS S3) is for storing unstructured data at scale. It lacks the tools needed for ad-hoc analysis or reporting.
D. Ensure data is stored in structured tables and rows to support transactional queries and relationships:
This approach is suited for transactional databases (OLTP), not for analytical workloads in a data warehouse (OLAP).
Conclusion:
A modern data warehouse supports both structured and semi-structured data, enabling ad-hoc analysis and custom reporting, making Option B the correct choice.
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joshnort
3 months, 1 week agojoshnort
3 months, 1 week ago