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Difficulty: HardData Integration and Analytical Storage

An enterprise is designing a telemetry analytics platform on Azure to ingest and store clickstream logs from regional applications. The platform must meet the following requirements:

* Storage and Volume: Ingest and store 15 TB15\text{ TB} of historical logs in CSV format within a hierarchical namespace storage account, with a nightly batch pipeline appending 200 GB200\text{ GB} of new data at a rate of 10 MB/s10\text{ MB/s}.
* Exploratory Queries: Enable data analysts to run ad-hoc, exploratory SQL queries on the raw files with a target query latency of under 15 seconds15\text{ seconds}. Compute cost must only be incurred during query execution.
* Resilience: The underlying storage must remain available for read access during a primary Azure region outage.
* Security: Grant third-party partners read-only access to a specific directory in the data lake, with the ability to instantly revoke access if credentials are compromised.

Which storage configuration, analytics service, and authorization mechanism should you recommend to meet these requirements?

  1. A
    Configure the storage account to use Locally Redundant Storage (LRS). Deploy Azure Synapse Analytics serverless SQL pools to query the CSV files. Generate Shared Access Signatures (SAS) linked to a Stored Access Policy for external partners.
  2. Configure the storage account to use Read-Access Geo-Redundant Storage (RA-GRS). Deploy Azure Synapse Analytics serverless SQL pools to query the CSV files. Generate Shared Access Signatures (SAS) linked to a Stored Access Policy for external partners.Answer
  3. C
    Configure the storage account to use Read-Access Geo-Redundant Storage (RA-GRS). Deploy an Azure Synapse Analytics dedicated SQL pool to query the CSV files. Generate Shared Access Signatures (SAS) linked to a Stored Access Policy for external partners.
  4. D
    Configure the storage account to use Read-Access Geo-Redundant Storage (RA-GRS). Deploy Azure Synapse Analytics serverless SQL pools to query the CSV files. Generate long-lived Shared Access Signatures (SAS) with expiration times defined directly in the SAS token URI for external partners.

Answer

Configure the storage account to use Read-Access Geo-Redundant Storage (RA-GRS). Deploy Azure Synapse Analytics serverless SQL pools to query the CSV files. Generate Shared Access Signatures (SAS) linked to a Stored Access Policy for external partners.
The correct answer combines Read-Access Geo-Redundant Storage (RA-GRS) to ensure regional read-resilience, Azure Synapse Analytics serverless SQL pools to support cost-effective ad-hoc SQL queries on raw CSV files in Azure Data Lake Storage Gen2, and Shared Access Signatures (SAS) linked to a Stored Access Policy to enable immediate revocation of third-party access.

Step-by-Step Solution

1
Identify the storage configuration that meets the resilience requirements.
Read-Access Geo-Redundant Storage (RA-GRS) is selected.
RA-GRS replicates data to a secondary region and allows read access to the secondary endpoint during a primary region outage, satisfying the resilience SLA, whereas Locally Redundant Storage (LRS) is vulnerable to regional outages.
2
Determine the correct analytics service that satisfies the cost and query requirements.
Azure Synapse Analytics serverless SQL pool is selected.
Serverless SQL pools allow running ad-hoc T-SQL queries directly against CSV files in the data lake, and costs are calculated based on the data processed rather than active server time, eliminating idle compute costs.
3
Select the secure authorization mechanism for external partners.
Shared Access Signatures (SAS) linked to a Stored Access Policy are selected.
Linking the SAS to a Stored Access Policy allows administrators to revoke access immediately by deleting or modifying the policy, whereas standard SAS tokens with the expiry embedded in the URI cannot be revoked without rotating the storage account keys.

Key Concept

Designing resilient, cost-effective, and secure analytical storage solutions in Azure.
Estimated Time:2m 0s
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Topics

Data Integration and Analytical Storage

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