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Este exercício faz parte do curso
Master Snowflake's data ingestion pipeline — from staging files through COPY INTO, Snowpipe, and Snowpipe Streaming — and connect Snowflake to the broader data ecosystem through connectors, drivers, and export tools.
Build robust data pipelines using Snowflake-native orchestration — streams for change capture, tasks for scheduling, dynamic tables for declarative transformation, and external and Iceberg tables for open-format storage.
Query semi-structured JSON with dot notation and FLATTEN, aggregate data with GROUPING SETS, ROLLUP, and CUBE, compute window functions, and encapsulate reusable logic in UDFs and stored procedures.
Profile queries in Snowsight, identify bottlenecks with QUERY_HISTORY, apply Search Optimization, Query Acceleration Service, Automatic Clustering, and Materialized Views, and write SQL that benefits from Snowflake's three caching layers.
Exercício atual