Data engineering interview questions · medium
How do you merge data from different sources in ADF while maintaining data quality?
How would you optimize an ADF pipeline for high performance?
How would you migrate 1TB of data using ADF?
How would you optimize cost when using AWS for large-scale data processing?
Lambda vs. Glue: Discuss use cases for both services.
What alternatives to Kinesis would you consider for real-time data ingestion?
What integration challenges might you face with Glue Catalog in non-AWS environments?
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Learn the platform used by your target companies. AWS is most common overall (Glue, Redshift, S3, Kinesis). GCP is preferred by Google and startups (BigQuery, Dataflow, Pub/Sub). Azure is dominant in enterprise (Synapse, Data Factory). Learn one deeply and understand the equivalents on others.
Core tools: SQL, Python, Spark, Airflow (or equivalent orchestrator), one cloud platform. Increasingly important: dbt, Kafka, Terraform, Docker/Kubernetes, Delta Lake or Apache Iceberg, a data observability tool. The specific stack varies by company.
Yes. Apache Airflow is the most widely used orchestration tool and questions about DAG design, task dependencies, XComs, operators, and failure handling are common. If the company uses a different orchestrator, expect similar questions adapted to their tool.