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Zorluk: OrtaData Integration and Analytical Storage

A manufacturing company is designing an analytical data platform to monitor and analyze assembly line operations. The platform must meet the following requirements:
- Store 55 TB of historical machine telemetry in CSV format.
- Ingest real-time sensor streams at a rate of 10,00010,000 events per second (1010 MB/s).
- Perform real-time anomaly detection with a processing latency of less than 22 seconds.
- Support ad-hoc exploratory SQL queries over the historical CSV data with execution times under 3030 seconds, without the operational overhead of a running cluster.
- Ensure the storage solution remains available if a primary zone within the Azure region experiences an outage.

Which two components should you include in the design to meet these requirements? (Select TWO.)

  1. Azure Synapse Analytics serverless SQL pools to query the historical telemetry data directly from Azure Data Lake Storage Gen2.Cevap
  2. Azure Stream Analytics to process real-time sensor streams ingested through Azure Event Hubs.Cevap
  3. C
    Azure Synapse dedicated SQL pools to run the ad-hoc queries on the CSV data stored in Azure Data Lake Storage Gen2.
  4. D
    Locally Redundant Storage (LRS) for the Azure Data Lake Storage Gen2 account to protect against datacenter outages.
  5. E
    A Shared Access Signature (SAS) with a 2-year expiration window to grant read access to external analytical tools.

Cevap

Azure Synapse Analytics serverless SQL pools to query the historical telemetry data directly from Azure Data Lake Storage Gen2, and Azure Stream Analytics to process real-time sensor streams ingested through Azure Event Hubs.
The correct architecture uses Azure Synapse Analytics serverless SQL pools to run ad-hoc queries on the CSV data stored in Azure Data Lake Storage Gen2, as it avoids persistent cluster costs and management. Additionally, Azure Stream Analytics paired with Azure Event Hubs handles the hot path streaming analytics and real-time anomaly detection under the 2-second latency requirement.

Adım Adım Çözüm

1
Analyze cold path analytical query requirements
Identified the need to run ad-hoc SQL queries directly on CSV data in Azure Data Lake Storage Gen2 with zero persistent cluster overhead.
Azure Synapse Analytics serverless SQL pools are paid per query and require no cluster provisioning, satisfying the cost and low operational overhead constraint.
2
Analyze hot path streaming and ingestion requirements
Identified the need for an ingestion event broker and a stream processing engine capable of sub-2-second latency.
Azure Event Hubs combined with Azure Stream Analytics provides a managed, scalable solution for real-time anomaly detection.
3
Evaluate redundancy and security requirements
Ruled out Locally Redundant Storage (LRS) since the data must survive a zone outage, and rejected long-lived SAS tokens due to operational security risks.
Zone-Redundant Storage (ZRS) is required to survive a zone outage, and Shared Access Signatures with long lifetimes should use stored access policies for easy revocation.

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Data Integration and Analytical Storage
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