What is one outcome of creating a Streamlit app in Snowflake?
本练习是课程的一部分
通过我们的互动练习之一，将理论转化为实践
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.
当前练习
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.
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.
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.
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.
