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Using Snowflake to data engineer Kaggle and Google Trends data with Python procedures and tasks
Updated on Sep, 2026
Programming, Database and Design Development, SQL
Training 5 or more people ?
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Course Overview In this course, I construct a data engineering pipeline that gathers multiple sources of information: Kaggle datasets and Google Trends data fetched through SerpAPI. The pipeline aggregates and combines data on Netflix actors along with trends about them on Google within weeks after a new show is released. You will use Kaggle as a data source for datasets regarding Netflix shows and actors, and Google Trends through SerpAPI to fetch live search data for the actors. All data will be stored and processed within the Snowflake database, using its cloud-native architecture to maximize scalability and performance. Technical Stack Overview Snowflake Database Central repository for storing and querying data. Streamlit within Snowflake A web application framework where the data can be rendered directly within Snowflake. AWS S3 Storage location where some intermediate datasets are stored or fetched. Snowflake Python Procedures Code used to automate parts of the data manipulation and processing pipeline. Snowflake External Access & Storage Integrations Used to manage secure access to external services and storage. Outcome By the end of this course, you will have a fully functional data pipeline that processes and merges streaming data, cloud storage, and APIs to enable trend analysis, visualized in an interactive Streamlit app within Snowflake.
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