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Difficulty: MediumData Integration and Analytical Storage

A digital education company is designing an analytical data platform to process and analyze student interaction logs on Azure. The platform must meet the following requirements:

* Volume & Ingestion: Ingest 3 TB3 \text{ TB} of JSON logs daily at a peak velocity of 2,0002,000 events per second.
* Latency: Raw logs must be queryable via standard SQL within 1515 minutes of ingestion.
* Query Patterns: Daily dashboard queries targeting the last 2424 hours, and monthly exploratory reports analyzing historical data up to 22 years old. Historical reports must execute without manual intervention or data retrieval delays.
* Business Continuity: The analytical data store must remain available and resilient against a primary datacenter outage.
* Cost Optimization: Storage costs must be minimized by automatically moving older data to a lower-cost tier when it is no longer queried by the daily dashboards.

Which design should you recommend for the analytical storage, lifecycle policy, and query engine?

  1. A
    Store the logs in an Azure Data Lake Storage Gen2 (ADLS Gen2) account configured with Locally Redundant Storage (LRS). Implement a lifecycle policy to transition logs older than 30 days to the Cool tier, and use an Azure Synapse Analytics serverless SQL pool to execute the monthly historical reports.
  2. B
    Store the logs in an Azure Data Lake Storage Gen2 (ADLS Gen2) account configured with Read-Access Geo-Redundant Storage (RA-GRS). Implement a lifecycle policy to transition logs older than 30 days to the Archive tier, and use an Azure Synapse Analytics serverless SQL pool to execute the monthly historical reports.
  3. Store the logs in an Azure Data Lake Storage Gen2 (ADLS Gen2) account configured with Read-Access Geo-Redundant Storage (RA-GRS). Implement a lifecycle policy to transition logs older than 30 days to the Cool tier, and use an Azure Synapse Analytics serverless SQL pool to execute the monthly historical reports.Answer
  4. D
    Store the logs in an Azure Data Lake Storage Gen2 (ADLS Gen2) account configured with Read-Access Geo-Redundant Storage (RA-GRS). Implement a lifecycle policy to transition logs older than 30 days to the Cool tier, and provision an Azure Synapse Analytics dedicated SQL pool to execute the monthly historical reports.

Answer

The correct architecture uses Azure Data Lake Storage Gen2 configured with Read-Access Geo-Redundant Storage, transitions older data to the Cool tier using a lifecycle policy, and queries it with an Azure Synapse Analytics serverless SQL pool.
The correct design uses Azure Data Lake Storage Gen2 (ADLS Gen2) with Read-Access Geo-Redundant Storage (RA-GRS) to provide regional disaster resilience. To optimize costs without delaying reports, logs older than 30 days are automatically transitioned to the Cool tier (since Archive tier data requires a multi-hour rehydration process). Monthly exploratory reports are run using a serverless SQL pool, which is cost-effective for infrequent, ad-hoc queries because it does not require a provisioned, running cluster.

Step-by-Step Solution

1
Select the correct storage redundancy model.
Read-Access Geo-Redundant Storage (RA-GRS) is selected to ensure resilience against a primary datacenter outage.
Locally Redundant Storage (LRS) only replicates data within a single datacenter, failing the business continuity requirement.
2
Select the lifecycle storage tier for historical data.
The Cool tier is selected for archiving data older than 30 days.
The Archive tier introduces rehydration latency (several hours), which violates the requirement that monthly reports must run without manual intervention or data retrieval delays.
3
Select the query engine for ad-hoc monthly reporting.
Azure Synapse Analytics serverless SQL pool is selected.
Dedicated SQL pools require active compute provisioning and are inefficient for ad-hoc, monthly exploratory queries over raw files in ADLS Gen2, whereas serverless SQL pools execute queries on-demand and charge based on data processed.

Key Concept

Data Integration and Analytical Storage architecture design combining cost optimization, redundancy, lifecycle management, and query model selection.
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Data Integration and Analytical Storage

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