AZ-900 Describe Azure architecture and services Practice Question
Which Azure service provides enterprise-grade data integration and ETL/ELT pipelines for moving data between on-premises and cloud data stores?
⚠ Common exam trap
It's easy for candidates to confuse Azure Data Factory with Azure Databricks or HDInsight, as candidates often associate 'data integration' with big data processing platforms rather than the dedicated orchestration service that handles connectivity, scheduling, and monitoring across heterogeneous sources.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Factory
Azure Data Factory (ADF) is the correct answer because it is a cloud-based ETL/ELT service specifically designed for orchestrating and automating data movement and transformation between on-premises and cloud data stores. It provides over 90 built-in connectors, supports hybrid data integration via self-hosted integration runtimes, and enables code-free pipeline creation for complex data workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is purpose-built for real-time, in-memory processing of streaming data from sources like Azure Event Hubs and IoT Hub. It is not an ETL/ELT service for batch data movement because it does not offer connectors for moving static data between 90+ stores, nor does it support scheduled pipeline orchestration. Its job is to run continuous queries against live data streams and emit results to outputs, which is fundamentally different from the batch-oriented orchestration that Data Factory provides.
- ✓
Azure Data Factory
Why this is correct
Azure Data Factory is the canonical ETL/ELT orchestration service in Azure. It provides over 90 native connectors to on-premises and cloud data sources, enabling you to design, schedule, and monitor data movement and transformation pipelines visually or via code. Data Factory supports both code-free mapping data flows and hand-written activities that can invoke compute engines like Azure Databricks and HDInsight, making it the dedicated service for orchestrating end-to-end data integration.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics platform optimized for big data engineering and machine learning. While it absolutely can perform ETL transformations within its scalable notebooks and clusters, it is fundamentally a compute and processing engine rather than a data orchestration service; it lacks the prebuilt connectors to 90+ sources and the pipeline scheduling/control-flow capabilities of Data Factory. In practice, Data Factory often orchestrates Databricks jobs, not the other way around.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed cloud service that deploys open-source big data frameworks such as Hadoop, Spark, Hive, and Kafka on dedicated clusters. It offers the raw compute power to execute large-scale batch transformations, but it does not provide a native, productized ETL/ELT pipeline service with built-in connectors across diverse data stores. You would use HDInsight as a compute target within a Data Factory pipeline to run Hive or Spark jobs, not as the service that moves and orchestrates data between sources and destinations.
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Azure Regions and Geographies
Key term
Pipeline
A pipeline is an automated series of steps that takes code from development to production, ensuring quality and speed.
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
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