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BigData / Data Lake Interview questions

What query engines work with Data Lakes (Presto, Athena, Spark SQL)?

Data lakes support multiple query engines, each optimized for different workloads. Understanding their strengths helps choose the right tool for each use case.

Apache Spark SQL: Distributed SQL engine part of Apache Spark. Excels at large-scale batch processing, ETL, and ML integration. Supports structured streaming for real-time SQL. Reads Parquet, ORC, Avro, Delta, Iceberg, Hudi. Best for: complex transformations, ML pipelines, large ETL jobs. Drawback: higher latency than dedicated query engines.

Presto/Trino: Distributed SQL query engine for interactive analytics. Designed for sub-second to minute-scale queries across petabytes. Supports federation—query multiple data sources (S3, HDFS, MySQL, PostgreSQL, Kafka) in single query. Best for: ad-hoc analytics, dashboards, exploratory analysis. Drawback: memory-intensive, not ideal for long-running ETL.

AWS Athena: Serverless query service based on Presto. No infrastructure management. Pay per query (cost = data scanned). Integrates with Glue Data Catalog. Supports Parquet, ORC, Avro, Delta, Iceberg, Hudi. Best for: serverless analytics, infrequent queries, quick data exploration. Drawback: cost can be high without partitioning.

Google BigQuery: Serverless data warehouse optimized for analytics. Separates storage from compute. Blazing fast on large scans. Automatically clusters data. Best for: BI, reporting, large aggregations. Drawback: primarily GCS, costs scale with usage.

Snowflake: Cloud data platform supporting structured and semi-structured data. Excellent query performance. Automatic clustering and optimization. Multi-cloud support. Best for: data warehousing, BI, consistent performance. Drawback: higher cost, less flexibility than open formats.

Apache Hive: SQL-on-Hadoop engine. Slower than Presto/Spark but battle-tested. Supports complex ETL. Best for: legacy Hadoop environments, batch processing. Drawback: high latency.

Choosing the Right Engine: Interactive analytics → Presto/Athena. Large-scale ETL → Spark. Serverless simplicity → Athena/BigQuery. Data warehousing → Snowflake/BigQuery. Streaming + batch → Spark. Multi-source federation → Presto/Trino.

Which query engine is best for interactive ad-hoc analytics?
What makes AWS Athena serverless?

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