Data Pipeline Automation in Snowflake

PodstawowyPoziom umiejętności

Zaktualizowano 07.2026

Load, automate, and optimize data pipelines in Snowflake using COPY INTO, Snowpipe, streams, tasks, dynamic tables, and query performance tools.

Opis kursu

Master the tools and techniques for building reliable, automated data pipelines in Snowflake. You'll start by learning how to ingest data at scale — configuring stages and file formats, loading data with COPY INTO, and choosing between batch loading, Snowpipe, and Snowpipe Streaming for different latency and volume requirements. From there, you'll build end-to-end pipeline orchestration skills: capturing row-level changes with streams, chaining multi-step workflows with task DAGs, and creating declarative, auto-refreshed pipelines with dynamic tables. You'll also learn when to reach for external and Iceberg tables for multi-engine and cloud-native scenarios. The second half of the course sharpens your querying and transformation skills — extracting and unnesting semi-structured JSON from VARIANT columns, applying grouping extensions and window functions for advanced analytics, and encapsulating reusable logic in UDFs and stored procedures. Finally, you'll tackle query performance: reading Snowflake's Query Profile to pinpoint bottlenecks, selecting the right optimization tool — Search Optimization, Query Acceleration Service, clustering keys, or materialized views — and writing cache-friendly SQL that avoids the common anti-patterns that silently degrade performance.

Wymagania wstępne

Ten kurs nie ma żadnych wymagań wstępnych

Data Loading, Ingestion, and Connectivity

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.

Opis kursu

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