Name: Introduction to Modern Data Engineering with Snowflake
Rating: 4.833333333333333 (36 reviews)

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Learn how to build modern, continuous data pipelines with Snowflake in this hands-on course. Following the Ingestion-Transformation-Delivery (ITD) framework, you'll ingest data at scale, transform it with SQL and Snowpark, deliver data products, and orchestrate everything into automated pipelines using tasks and DAGs. You'll work the way data engineers do every day, using Snowflake alongside Visual Studio Code, GitHub, and the command line.

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Modern data engineering with Snowflake

Discover how the explosion of data gave rise to data engineering, and how Snowflake reshapes the modern approach. You'll frame your work around the Ingestion-Transformation-Delivery (ITD) framework, prepare your development environment, and build your first simple pipeline in Snowflake.

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Batch data ingestion with Snowflake

Ingest data into Snowflake at scale using a range of powerful techniques. You'll load data through the Snowflake Marketplace, the Snowsight web interface, the Snowflake CLI, and the COPY INTO command, pull from external systems with native connectors, and tune virtual warehouses for efficient batch ingestion.

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Data transformations with Snowflake

Transform raw data into analysis-ready data using SQL and Snowpark for Python. You'll create and apply user-defined functions, stored procedures, streams, and Dynamic Tables, and run transformations from inside Visual Studio Code using Snowflake's official extension.

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Delivering data products with Snowflake

Turn clean, transformed data into data products that deliver value. You'll share datasets through the Snowflake Marketplace, build and share insights with a Streamlit in Snowflake application, and deploy a Snowflake Native Application using the Snowflake CLI.

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Orchestrating continuous data pipelines with Snowflake

Bring your pipelines to life with orchestration. You'll automate pipeline steps using user-managed and serverless tasks, link tasks into a task graph (DAG), and execute individual tasks and entire DAGs to run continuous, automated data pipelines.