Microsoft Azure Data Fundamentals DP-900 Practice Question
You are designing a data lake in Azure for a finance team. The dataset will store several terabytes of structured tabular data that analysts will query repeatedly from Azure Synapse Analytics. To reduce both storage space and scan time, which file format should you choose?
Parquet is a columnar storage format that applies compression and encoding on a per-column basis. Because analytical engines such as Azure Synapse Analytics can read only the columns required by a query, Parquet typically reduces the amount of data scanned and the overall storage footprint. CSV, JSON, and plain text store data row-by-row, offer little or no built-in compression, and therefore lead to larger files and slower scans for the same workload.
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What is Apache Parquet, and why is it suitable for analytical workloads?
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How does columnar storage in Parquet differ from row-based storage in CSV, JSON, or plain text?
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What makes Parquet more storage-efficient than CSV and JSON?
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Microsoft Azure Data Fundamentals DP-900
Describe core data concepts
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