How One Startup Uses Serverless SQL to Query Data Lakes in Real Time

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How One Startup Uses Serverless SQL to Query Data Lakes in Real Time

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Lucas and Luna dive into the story of a logistics startup that replaced nightly batch ETL jobs with serverless SQL queries against their data lake. The founders were struggling with a 24-hour delay in shipment tracking data. By switching to Athena and Presto on S3, they cut query latency from hours to seconds and reduced their data infrastructure bill by 60%. Lucas explains the technical architecture: partitioned Parquet files, columnar compression, and how they used AWS Glue for schema inference. Luna asks the hard questions about when this approach breaks down—like concurrency limits and hot partitions. They also discuss the trade-offs between serverless SQL and traditional data warehouses like Redshift or Snowflake. If you're building analytics on raw data without wanting to manage a cluster, this episode is a practical blueprint. #ServerlessSQL #DataLake #Athena #Presto #AWSGlue #Parquet #ColumnarStorage #Logistics #RealTimeAnalytics #ETL #DataEngineering #Business #Technology #FexingoBusiness #BusinessPodcast #TechnicalCoFounder #StartupStack #Infrastructure Keep every episode free: buymeacoffee.com/fexingo

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