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Difficulty: MediumData Integration and Analytical Storage

A financial services company is designing an analytical data platform to audit daily credit card transactions. The platform must store 15 TB15\text{ TB} of historical records and ingest new batch transaction data at a rate of 250 GB250\text{ GB} of CSV files nightly. Data analysts need to run ad-hoc exploratory SQL queries on the raw files with sub-minute query latency. The storage solution must remain available even during a complete regional datacenter outage. You need to minimize operational overhead and avoid paying for idle compute resources. Which two components should you include in the analytical storage and query design?

  1. Azure Synapse Analytics serverless SQL poolsAnswer
  2. Azure Data Lake Storage Gen2 with Geo-Redundant Storage (GRS)Answer
  3. C
    Azure Synapse Analytics dedicated SQL pools
  4. D
    Azure Data Lake Storage Gen2 with Locally Redundant Storage (LRS)

Answer

Azure Synapse Analytics serverless SQL pools and Azure Data Lake Storage Gen2 with Geo-Redundant Storage (GRS)
The correct architecture uses Azure Synapse Analytics serverless SQL pools for pay-per-query ad-hoc SQL analysis on raw CSV files directly in the data lake, eliminating idle compute costs and operational overhead. Azure Data Lake Storage Gen2 with Geo-Redundant Storage (GRS) ensures the storage layer survives a regional datacenter outage by replicating the files to a paired region.

Step-by-Step Solution

1
Evaluate the analytical query model requirements.
Serverless SQL pools are selected instead of dedicated SQL pools because queries are ad-hoc, run directly on raw CSV files, and compute costs must only be incurred during query execution with minimal operational overhead.
Dedicated pools incur high idle costs and administrative overhead, which violates the requirement to avoid paying for idle compute.
2
Determine the optimal storage account tier and redundancy level.
Azure Data Lake Storage Gen2 with GRS is selected.
A hierarchical namespace is required for organizing data lake files efficiently, and GRS replicates data to a secondary region to satisfy the regional resilience requirement, whereas LRS only protects against local hardware failures.

Key Concept

Selecting the optimal serverless query engine and geo-redundant storage configuration for ad-hoc analytical workloads.
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Topics

Data Integration and Analytical Storage

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