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Difficulty: EasyData Integration and Analytical Storage

An enterprise stores large volumes of raw sensor data in Parquet format within an Azure Data Lake Storage Gen2 container. Members of the data engineering team need to run occasional, exploratory SQL queries to analyze the schema and contents of new files. They do not want to load the data into a database or keep compute clusters running when no queries are active.

Which Azure Synapse Analytics component is the most appropriate option to satisfy these requirements?

  1. A serverless SQL poolAnswer
  2. B
    A dedicated SQL pool
  3. C
    Locally Redundant Storage (LRS)
  4. D
    A Shared Access Signature (SAS) token

Answer

A serverless SQL pool
The correct option is a serverless SQL pool because it allows data engineers to query data in the data lake directly without loading it or provisioning a cluster. It charges per query based on data processed, aligning with the requirement to avoid idle compute costs.

Step-by-Step Solution

1
Analyze the requirements
Occasional, exploratory SQL queries directly on Parquet files in Azure Data Lake Storage Gen2 without provisioning persistent compute clusters.
This establishes that we need an on-demand query engine that charges based on usage rather than continuous provisioning.
2
Evaluate the capabilities of serverless SQL pools vs dedicated SQL pools in Azure Synapse Analytics
Serverless SQL pools run queries on-demand and scale compute automatically, charging only for data processed. Dedicated SQL pools require running clusters and data ingestion.
This helps identify the Synapse component that satisfies the requirement to minimize idle compute cost and query files directly.

Key Concept

Azure Synapse SQL Serverless vs Dedicated Pools
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Topics

Data Integration and Analytical Storage

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