Confident Snowflake navigation - You will learn how to work comfortably in Snowflake by understanding user roles, navigating the database, reading schemas, and finding the objects you need without wasting time.
Hands-on SQL querying - You will practice writing SQL in Snowflake, so you can filter data on your own, count records, use DISTINCT correctly, and build clear queries for everyday analysis work.
Smarter data modeling - You will understand how data types affect table design and query behavior, which will help you choose better structures, avoid interpretation issues, and improve analysis quality.
Clean multi-table analysis - You will master joins, subqueries, and CTEs with WITH, allowing you to combine data from multiple sources in a structured way and build queries that stay readable as they grow.
Warehouse management basics - You will learn how to create and configure Snowflake warehouses, understand clustering options and scaling rules, and make better decisions about resources, performance, and cost.
Safe data changes - You will practice core data operations such as INSERT, UPDATE, and DELETE, so you can modify table contents with more confidence and better understand day-to-day data work.
Efficient loading and transforms - You will learn how to load data into Snowflake from flat files and other sources, create database objects, and apply transformations needed before reporting or further analysis.
Nested data handling - You will learn how to work with semi-structured data, recognize supported file formats, and use FLATTEN to extract useful values from nested structures for downstream analysis.
As a starting point, the platform is presented through its core administrative and analytical elements. The scope includes user roles, database navigation, schemas and objects, plus data types, exploration methods and the basics of building SQL queries in the Snowflake environment.
Writing effective SQL in Snowflake means combining core query patterns with clear result-shaping logic. The section covers filtering, aggregate functions such as COUNT and DISTINCT, as well as joins, subqueries and CTEs built with WITH to structure more complex statements.
Warehouse performance in Snowflake depends on how compute resources are configured and scaled. The material covers cluster options, explains the principles behind resizing and concurrency handling, and shows how warehouse management affects execution capacity for analytical workloads.
Day-to-day data maintenance relies on a small set of SQL statements with a large operational impact. The section focuses on INSERT, UPDATE and DELETE, clarifying how records are added, modified and removed in Snowflake and how these commands support controlled changes in existing tables.
Loading data into Snowflake starts with the right database objects and storage layout. Participants work through imports from flat files, learn how data is organized after ingestion, and see where transformations fit when preparing datasets for downstream use or exchange with other systems.
Semi-structured data in Snowflake requires a different approach to storage and querying than relational tables. This part explains supported file formats, shows how nested structures are represented, and demonstrates how FLATTEN is used to expand complex elements into rows for further analysis.
Basic SQL knowledge - You should understand basic SQL queries, including SELECT, WHERE, and ORDER BY, so you can focus on working in Snowflake instead of learning query syntax from scratch.
Tables and relationships - You should understand tables, columns, records, and relationships between data, because this will help you work with schemas, joins, and simple data structure design.
Working with data files - You should have basic experience working with data stored in files such as CSV or JSON, so it is easier for you to understand loading, file formats, and simple transformations.
Analytical mindset - You should be comfortable analyzing data and query results, so you can draw conclusions quickly, explore datasets effectively, and verify whether returned results make sense.
