← Excel Impact Interview Insights
Start by clarifying the specific limitations of AWS Glue for unstructured data (e.g., schema enforcement, performance, cost). Then propose alternative ETL tools that better handle unstructured data, such as Apache Spark, Databricks, or AWS Glue with custom scripts, and discuss trade-offs in terms of scalability, cost, and integration with the data warehouse. Emphasize a product manager's perspective: aligning tool choice with business needs, team skills, and long-term maintainability.
Pro tip: Frame your answer around the 'build vs. buy' decision and total cost of ownership, not just technical features. Show you understand that the best tool depends on the team's expertise, existing infrastructure, and the specific nature of the unstructured data (e.g., text, images, logs).
Ask clarifying questions about the workload: what types of unstructured data, volume, velocity, and the target data warehouse. This shows you don't jump to solutions without understanding requirements.
Briefly state why Glue isn't cutting it: e.g., limited support for complex transformations on unstructured data, performance bottlenecks, or cost at scale. This justifies the need for alternatives.
Suggest 2-3 alternative ETL tools (e.g., Apache Spark with custom code, Databricks, Apache NiFi, or cloud-native services like AWS Lambda with S3 triggers). Explain how each handles unstructured data better.
Compare alternatives on dimensions like scalability, cost, ease of use, integration with the data warehouse, and team expertise. Highlight that the choice depends on specific constraints.
Make a recommendation based on the context, and explain how you would validate it (e.g., proof of concept, cost analysis). Show product thinking by considering stakeholder needs and long-term strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.