ExpertMedium1 markMultiple Choice
AZ-305 · Question 21 · Domain 2.2: Data Integration
A retail company is designing an ELT (Extract, Load, Transform) pipeline.
Requirements:
- Extract data from 50 different on-premises and cloud data sources (SQL, Oracle, Salesforce, REST APIs).
- Orchestrate the movement of this data into Azure Data Lake Storage Gen2.
- Perform complex data transformations using Apache Spark and Python.
- The transformation code must be developed collaboratively by data engineers using interactive notebooks.
Which TWO Azure services should you combine to meet these requirements? (Select TWO)
A retail company is designing an ELT (Extract, Load, Transform) pipeline.
Requirements:
- Extract data from 50 different on-premises and cloud data sources (SQL, Oracle, Salesforce, REST APIs).
- Orchestrate the movement of this data into Azure Data Lake Storage Gen2.
- Perform complex data transformations using Apache Spark and Python.
- The transformation code must be developed collaboratively by data engineers using interactive notebooks.
Which TWO Azure services should you combine to meet these requirements? (Select TWO)
Answer options:
A.
Azure Data Factory
B.
Azure Stream Analytics
C.
Azure Databricks
D.
Azure Event Hubs
E.
Azure SQL Managed Instance
How to approach this question
Identify the orchestration/extraction tool (Data Factory) and the Spark/Notebook transformation tool (Databricks).
Full Answer
Azure Data Factory is the premier data integration and orchestration service in Azure, featuring over 90 built-in connectors to extract data from various sources and load it into a data lake. Azure Databricks is an Apache Spark-based analytics platform optimized for Azure, providing interactive collaborative notebooks for data engineers to write complex Python/Spark transformations. Data Factory can natively trigger Databricks notebooks as part of its pipeline.
Common mistakes
Trying to use a single service for everything. While Synapse Analytics could potentially do both, the specific combination of '90+ connectors' and 'collaborative Python notebooks' strongly points to the classic ADF + Databricks pattern.
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