Building Data Warehouse with Snowflake, Python and SQL

This course contains the use of artificial intelligenceDisclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor. Welcome to Building Data Warehouse with Snowflake, Python and SQL course.

This is a comprehensive project based course where you will learn how to build a fully functioning data warehouse using Snowflake and implement dimensionality modelling. This course is a perfect combination between Snowflake and Python, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in data engineering. In the introduction session, you will learn the basic fundamentals of data warehouse and snowflake, such as getting to know about data warehouse architecture and learning about the difference between online transaction processing and online analytic processing.

Then, in the next section, we will download grocery inventory and restaurant delivery datasets from Kaggle, it is a platform that offers many high quality datasets from various industries. Afterward, we will set up a Snowflake account, learn how to navigate the Snowflake dashboard, and load the grocery inventory datasets. Then, after that, we are going to clean the dataset by removing missing values and duplicates.

The objective is to make sure the data is valid and ready to use. Following that, we will learn about basic sql concepts needed for data warehousing, for example, we are going to learn how to use select, aggregate functions, where, having, order by, case, group by, distinct, window functions, and common table expressions. Then, we will run these SQL queries to analyze grocery inventory data and extract meaningful insights from the data.

Then, in the next section, we are going to transform and load data using SQL, where we will prepare and organize the data before building the data warehouse. In addition, we will also perform DataFrame operations using Snowpark like filtering, selecting, grouping, and transforming data. Then, after that we are going to perform dimensionality modelling, specifically we will create fact tables, dimension tables, and define relationships between those tables.

The objective is to organize our data efficiently for reporting, analysis, and business intelligence. Once the data is ready, we are going to build a basic grocery inventory data warehouse using Snowflake and SQL. This data warehouse can be used for analyzing inventory levels, product availability, stock movements, and overall inventory performance.

Then, in the next section, we will learn about data warehouse security and access control, specifically we will learn how to manage user roles, permissions, and access privileges in Snowflake. Additionally, we are also going to build a real world project, where we are going to build a restaurant delivery data warehouse. We will use restaurant delivery data to analyze orders, delivery performance, customer behavior, and sales trends.

Lastly, at the end of the course, we are going to build a machine learning model to predict delivery time using data from our data warehouse. First of all, before getting into the course, we need to ask these questions to ourselves, why should we build data warehouse and why should we use Snowflake? Well, here is my answer, By having a data warehouse, it will help us to centralize and organize data from multiple sources, making it easier to analyze and generate insights.

In addition, Snowflake enables us to efficiently store, process, and analyze large amounts of data in a cloud based environment.