DP-900 Describe core data concepts Practice Question
A data engineer needs to load data from an on-premises SQL Server database to Azure Synapse Analytics every hour with minimal latency. Which Azure service should they use?
⚠ Common exam trap
Many candidates confuse Azure Data Factory with Azure Databricks or HDInsight, assuming any big data or analytics service can handle scheduled data ingestion, but only ADF is purpose-built for orchestration and low-latency data movement from on-premises 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 choice because it provides a fully managed, code-free ETL service that can connect to on-premises SQL Server via self-hosted integration runtime, and load data into Azure Synapse Analytics with low latency using a scheduled trigger (e.g., every hour). ADF supports incremental data loading and parallel copy activities, minimizing latency while handling the required frequency.
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 Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics platform optimized for big data processing, machine learning, and interactive data exploration. While it can connect to an on-premises SQL Server via JDBC and copy data into a Spark DataFrame, it is not designed for lightweight, recurring, low-latency ingestion tasks. Running hourly data loads would require spinning up or maintaining a Spark cluster, incurring significant startup latency and cost, which is contrary to the simple scheduled ETL pattern this question describes.
- ✓
Azure Data Factory
Why this is correct
Azure Data Factory is the correct choice because it is a cloud-based ETL and data integration service purpose-built for orchestrating and automating data movement. It provides a self-hosted integration runtime that securely connects to on-premises SQL Server databases, and its schedule triggers can run pipelines every hour with minimal latency. The service is designed specifically for copying data from sources like on-premises SQL Server to cloud destinations, making it the ideal tool for this workload.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a managed relational database service, not a data integration or orchestration tool. It can act as the destination of a data load, but it cannot pull data from an on-premises SQL Server on a recurring schedule by itself. Although its name includes 'SQL,' it lacks the pipeline, trigger, and connectivity features that Azure Data Factory provides, so it is incorrect for this question.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed service that deploys open-source big data frameworks like Hadoop, Spark, and Hive. While Spark or Sqoop on HDInsight could technically move data, doing so requires provisioning and managing a cluster, which is heavy-weight and not optimized for frequent, low-latency, scheduled data movement. This service is meant for large-scale batch analytics, not simple ETL pipelines, and it would introduce unnecessary operational overhead compared to Azure Data Factory.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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