
Master an end-to-end data engineering project in Snowflake, from API JSON extraction to cleansing, transformation, and star schema modeling, with Streamlit dashboards.
Explore the six-part course structure, translating business use cases into requirements, modeling data flows, building end-to-end pipelines, and delivering streamlit-powered dashboards and real-time analytics with Snowflake.
Design a dimensional model using a star schema with location and date dimensions and a pollutant fact table to enable hour-by-hour analytics across locations, while outlining the end-to-end data pipeline.
Construct date and location dimension tables and a fact table using a hash-based key within the consumption layer, enabling dimensional modeling of AQI measurements.
Design and implement aggregated fact tables at city level to summarize station-level air quality index data, compute city-level aqi averages, and enable hourly trends via common table expressions.
Build a simple streamlit app inside snowflake to display a tabular city-level air quality dashboard (AQI, city, state, pollutants) using snowpark and a pandas dataframe.
Create a Snowflake standard table and a daily refresh task to ingest marketplace weather data, build an aggregated daily fact table, and enable hourly and daily data consumption via dashboards.
Want to learn how to architect, design and implement End To End Data Project in Snowflake?
Do you want to upgrade your Snowflake skill and learn how to build complete end to end data project using Snowflake Cloud Data Warehouse Platform?
In this course, you will learn everything, starting from source file analysis, evaluating different architectural issues to ingetrate data, layered architecture to store different datasets, design stage/raw/curated layers and finally automate your data pipeline and implemente fully automated data flow from source to visualization with real life.
Why should you take this course?
Understand everything about end to end data engineering project - a step by step guide
Different data integration challenges and how to build a solution to ingest data into snowflake.
How to design a layered architecture to store and process the data.
How to automate the data integraiton to ingest live data.
How to automate and create a fully functional DAG.
How to use Snowflake dynamic tables to create fully functional data pipeline.
How to create a visualization using streamline within Snowflake
How to use Snowpark Python library with GitHub action to automate data flow.
How to use free marketplace data and integrate it with Snowflake data pipeline.