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Difficulty: MediumData Integration and Analytical Storage

A financial services firm is designing an analytical data platform to query historical audit logs. The platform must meet the following requirements:
- Data volume: 45 TB45\text{ TB} of historical records stored in CSV format.
- Ingestion rate: Data is uploaded weekly in a single batch.
- Query latency: Data analysts run exploratory ad-hoc queries sporadically; results are expected within a few minutes.
- Resiliency: The data must survive a primary datacenter outage.
- Security: External auditors require access via Shared Access Signatures (SAS) that can be instantly revoked without rotating primary storage keys.

You need to design a query and storage solution that minimizes operational overhead and compute costs. Which solution should you recommend?

  1. A
    Azure Synapse Analytics dedicated SQL pools with the data loaded into dedicated database tables, stored in a Geo-Redundant Storage (GRS) Azure Data Lake Storage Gen2 account, using Shared Access Signatures (SAS) associated with a stored access policy.
  2. B
    Azure Synapse Analytics serverless SQL pools querying the data stored in a Locally Redundant Storage (LRS) Azure Data Lake Storage Gen2 account, using Shared Access Signatures (SAS) associated with a stored access policy.
  3. Azure Synapse Analytics serverless SQL pools querying the data stored in a Geo-Redundant Storage (GRS) Azure Data Lake Storage Gen2 account, using Shared Access Signatures (SAS) associated with a stored access policy.Answer
  4. D
    Azure Synapse Analytics serverless SQL pools querying the data stored in a Geo-Redundant Storage (GRS) Azure Data Lake Storage Gen2 account, using ad-hoc Shared Access Signatures (SAS) valid for one year without a stored access policy.

Answer

Azure Synapse Analytics serverless SQL pools querying the data stored in a Geo-Redundant Storage (GRS) Azure Data Lake Storage Gen2 account, using Shared Access Signatures (SAS) associated with a stored access policy.
Using Azure Synapse Analytics serverless SQL pools is the most cost-effective solution for ad-hoc, sporadic querying as you are only billed for the data processed by each query. Storing the data in a Geo-Redundant Storage (GRS) account satisfies the resiliency requirement to survive a primary datacenter outage. Utilizing Shared Access Signatures (SAS) associated with a stored access policy allows the security team to instantaneously revoke access to the external auditors by modifying or deleting the policy, without needing to rotate storage account keys.

Step-by-Step Solution

1
Select the appropriate compute model for analytical querying.
Serverless SQL pools are selected because they charge per query (based on data processed) and do not run continuously, which minimizes costs for sporadic ad-hoc queries.
Dedicated SQL pools require provisioning persistent resources, leading to high idle costs, while serverless pools scale automatically and have zero idle cost.
2
Determine the storage redundancy model that survives regional outages.
Geo-Redundant Storage (GRS) is selected to replicate data to a secondary region.
Locally Redundant Storage (LRS) only replicates data within a single datacenter, failing the requirement to survive a primary datacenter outage.
3
Design the security token mechanism for external auditors.
Shared Access Signatures (SAS) linked to a stored access policy are selected.
Stored access policies allow immediate revocation of SAS tokens by editing or deleting the policy, avoiding the need to rotate the main storage account keys.

Key Concept

Data Integration and Analytical Storage
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Topics

Data Integration and Analytical Storage

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