Storage Types and Query Performance

Concepts covered: sqlCast

The question that opens the platform-scale type conversation: 'we have data flowing from Postgres source databases through a Kafka stream to an Iceberg lake to a Snowflake warehouse, with consumers reading from Arrow IPC for ML serving. Walk me through the type system across the path.' This is not a CAST question. The platform-grade answer walks the type alignment at each boundary: Postgres NUMERIC to Avro decimal logical type, into Iceberg DECIMAL, into Snowflake NUMBER, into Arrow Decimal128. Each conversion has a precision contract; misalignment at any boundary loses data. Three platform concerns First: codec design. Each transport (Arrow, Parquet, Iceberg metadata, Avro, Protobuf) has its own type system. The platform's job is to align them so data crosses without loss. Second: domain

About This Interactive Section

This section is part of the Type Casting: Advanced lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.