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Zorluk: OrtaData Integration and Analytical Storage

A telecommunications provider is designing an analytical data platform to process and query call detail record (CDR) log files. The platform must meet the following requirements:
- Ingest 50 GB50\text{ GB} of CDR files daily at a peak velocity of 200200 files per minute.
- Store the raw logs cost-effectively in Azure Data Lake Storage Gen2 (ADLS Gen2).
- Enable analysts to run ad-hoc, exploratory SQL queries directly on the raw files with a query latency of under 15 seconds15\text{ seconds}.
- Orchestrate a weekly batch transformation job that aggregates the CDR data, which typically takes 25 minutes25\text{ minutes} to run.
Which of the following Azure components should you include in the design to meet these requirements? (Select TWO.)

  1. Azure Synapse serverless SQL pools to query the raw log files in Azure Data Lake Storage Gen2Cevap
  2. B
    Azure Synapse dedicated SQL pools to query the raw log files in Azure Data Lake Storage Gen2
  3. Azure Data Factory pipelines to orchestrate the weekly batch transformationCevap
  4. D
    Azure Functions on a Consumption plan to run the weekly batch transformation

Cevap

Azure Synapse serverless SQL pools to query the raw log files in Azure Data Lake Storage Gen2, and Azure Data Factory pipelines to orchestrate the weekly batch transformation
Azure Synapse serverless SQL pools are correct for ad-hoc exploratory queries because they allow querying unstructured or semi-structured data directly in Azure Data Lake Storage Gen2 using standard T-SQL without provisioning persistent resources. Azure Data Factory pipelines are correct for the weekly batch orchestration because they are built to manage complex data integration workflows with no runtime limits.

Adım Adım Çözüm

1
Analyze the requirement for ad-hoc, exploratory SQL queries on raw data stored in Azure Data Lake Storage Gen2 (ADLS Gen2).
Identify that Azure Synapse serverless SQL pools are designed specifically for querying files (such as CSV, JSON, Parquet) in place without needing to provision dedicated clusters, fitting the 1515-second latency and cost-effectiveness requirements.
Dedicated SQL pools require importing data into relational tables and provisioning continuous resources, which is inefficient for ad-hoc file exploration.
2
Analyze the requirement for orchestrating a weekly batch transformation job that takes 25 minutes25\text{ minutes} to run.
Select Azure Data Factory (or Synapse Pipelines) as the orchestration engine, which supports long-running batch workflows without execution time limitations.
Azure Functions on a Consumption plan has a 1010-minute execution limit, which would cause the 2525-minute run to time out.

Anahtar Kavram

Selecting appropriate Azure analytical and integration services based on query patterns, operational overhead, and execution limits
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Konular

Data Integration and Analytical Storage

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