Top 50 Azure Data Engineer Interview Questions

Azure Data Engineer interviews test knowledge of data pipelines, storage, and processing frameworks. These questions cover the tools and concepts that come up most often.

Data Storage and Modeling

What is the difference between a data lake and a data warehouse?

A data lake stores raw, unstructured, and structured data at scale in its native format, while a data warehouse stores structured, processed data optimized for fast, consistent analytical queries.

What is Azure Data Lake Storage Gen2?

It's a set of capabilities built on Azure Blob Storage that adds a hierarchical namespace, making it efficient for big data analytics workloads that need file-and-directory-style operations at scale.

What is the medallion architecture (bronze, silver, gold)?

It's a data design pattern where raw data lands in a bronze layer, gets cleaned and conformed in a silver layer, and is aggregated into business-ready gold layer tables for reporting.

Data Integration and Pipelines

What is Azure Synapse Analytics?

Azure Synapse Analytics is an integrated analytics platform that combines big data processing, data warehousing, and data integration into a single service with a unified workspace.

What is the difference between Azure Data Factory and Azure Synapse pipelines?

Synapse pipelines are built on the same engine as Data Factory but run inside the Synapse workspace alongside SQL pools and Spark pools, letting data movement and analytics live in one unified environment.

What is a dedicated SQL pool in Synapse?

A dedicated SQL pool is a provisioned, MPP (massively parallel processing) data warehouse engine in Synapse used for large-scale structured analytics with predictable performance.

What is a serverless SQL pool in Synapse?

A serverless SQL pool lets you query data directly in a data lake using T-SQL, without provisioning dedicated compute, and you're billed only for the data processed by each query.

Processing and Compute

What is Azure Databricks, and how does it relate to Apache Spark?

Azure Databricks is a managed platform for running Apache Spark workloads, providing collaborative notebooks, cluster management, and performance optimizations on top of open-source Spark.

What is the difference between batch processing and stream processing?

Batch processing handles large volumes of data collected over time and processed at intervals, while stream processing handles data continuously as it arrives, typically with lower latency requirements.

What is Azure Stream Analytics used for?

Azure Stream Analytics is a real-time analytics service used to process and analyze streaming data from sources like IoT devices or Event Hubs using a SQL-like query language.

What is partitioning in the context of big data processing?

Partitioning divides large datasets into smaller, manageable chunks based on a key (like date or region), which improves query performance and parallelism during processing.

Data Quality and Governance

What is Azure Purview (Microsoft Purview)?

Microsoft Purview is a data governance service that helps organizations discover, catalog, and classify data across their estate, tracking lineage and enforcing data policies.

How do you handle schema drift in a data pipeline?

Schema drift is typically handled by designing pipelines to tolerate column additions or type changes gracefully, using dynamic schema mapping features rather than hardcoding rigid column lists.

What is data lineage, and why does it matter?

Data lineage tracks the origin and transformation history of data as it moves through a pipeline, which is essential for debugging, auditing, and building trust in analytical results.