DP-900 Describe an analytics workload on Azure Practice Question
A data engineering team is designing a modern data warehouse on Azure. They have raw data landing in Azure Data Lake Storage Gen2 (ADLS Gen2) as Parquet files. They need to perform transformations using Apache Spark, and then load the transformed data into Azure Synapse Analytics for high-performance analytical queries. The team wants to use a single orchestration service to schedule, monitor, and manage the entire pipeline. Which Azure service should they choose for orchestration?
⚠ Common exam trap
Watch out — candidates often confuse Azure Databricks (a compute/transform service) with an orchestration tool, but the question explicitly asks for a service to 'schedule, monitor, and manage the entire pipeline,' which is the core function of Azure Data Factory, not Databricks.
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 choice because it is a cloud-based ETL and orchestration service designed to schedule, monitor, and manage data pipelines at scale. It natively supports triggers (e.g., time-based, event-based) and can orchestrate Apache Spark transformations via Azure Databricks or HDInsight, then load the transformed data into Azure Synapse Analytics using built-in copy activities or pipelines. ADF provides a single pane of glass for end-to-end pipeline management, including dependency handling and error monitoring.
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 Data Factory
Why this is correct
Azure Data Factory is a PaaS data integration and orchestration service designed specifically to build, schedule, and monitor ETL/ELT pipelines at scale. It supports control-flow activities (If Condition, ForEach, Until), triggers, automatic retries, and lineage tracking, and can execute both Azure Databricks and Azure Synapse Spark activities to perform transformations. Because it also provides native connectors to thousands of data sources and targets, it is the correct central orchestrator for a modern data warehouse in Azure.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is a managed Apache Spark compute platform optimized for big data processing, analytics, and machine learning, not for pipeline-wide orchestration. While it exposes a job scheduler for individual notebooks or JARs, it does not provide the end-to-end control flow, heterogeneous data movement connectors, or centralized monitoring and alerting that a full data warehouse pipeline requires. In a modern data warehouse architecture, Databricks is more appropriately called as a compute step from an orchestration service like Azure Data Factory, not used as the orchestrator itself.
- ✗
Azure Logic Apps
Why it's wrong here
Azure Logic Apps is a low-code integration service for connecting apps and data with hundreds of connectors, best suited to lightweight, event-driven workflows such as sending notifications or synchronizing CRUD operations. It lacks native compute orchestration for Spark or Synapse, does not manage cluster lifecycles, and is not built to handle complex, long-running ETL transformations with branching, retries, and data-quality checks at warehouse scale. Treating Logic Apps as an ETL orchestrator would force you to implement custom code around its limited execution model.
- ✗
Azure Data Lake Analytics
Why it's wrong here
Azure Data Lake Analytics was a deprecated U-SQL batch analytics service that processed data already stored in Azure Data Lake Storage, but it was not an orchestration or data integration tool. It had no pipeline scheduling, dependency management, or broad connector surface, and it was retired as capabilities moved to Azure Synapse Analytics and Azure Databricks. Using it today would introduce technology debt and it cannot serve as the controlling layer for a modern data warehouse.
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Data Roles and Core Concepts
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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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