Course overview
Production data systems require more than traditional SQL databases. This course takes you beyond PostgreSQL into the tools that power modern data infrastructure at scale. You’ll start with Snowflake, learning how its cloud-native architecture separates storage from compute to handle massive datasets efficiently. Then you’ll explore the NoSQL landscape—understanding when document, key-value, column-family, and graph databases solve problems that SQL can’t. Finally, you’ll get hands-on with MongoDB, building a flexible review system that handles schema changes without migrations and connects to Python analytics workflows. By the end, you’ll understand how companies like Netflix and Uber combine multiple database types in production, and you’ll be able to choose the right tool for each part of your data pipeline.
Key skills
- Setting up and querying cloud data warehouses using Snowflake's separated storage and compute architecture
- Understanding NoSQL database types and making informed decisions about when to use SQL versus NoSQL
- Building flexible document storage systems with MongoDB that adapt to changing requirements
- Writing MongoDB queries and aggregation pipelines for semi-structured data processing
- Integrating NoSQL databases with Python for complete data engineering workflows
- Designing polyglot data architectures that combine SQL and NoSQL databases effectively
Course outline
Production Database Tools [3 lessons]
Introduction to Snowflake 2h
Lesson Objectives- Understand what Snowflake is and its core architecture
- Create and configure virtual warehouses for query processing
- Load CSV data and create tables in Snowflake
- Write and execute SQL queries using Snowflake worksheets
- Monitor credit usage and manage compute costs effectively
Introduction to NoSQL: What It Is and Why You Need It 2h
Lesson Objectives- Understand what NoSQL databases are and their differences from SQL
- Identify four main NoSQL types: document, key-value, column-family, graph
- Make informed decisions when choosing NoSQL versus SQL databases
- Recognize real-world polyglot architecture patterns from industry examples
- Apply hands-on MongoDB skills to cement NoSQL concepts
Hands-On NoSQL with MongoDB: From Theory to Practice 2h
Lesson Objectives- Set up MongoDB Atlas cluster and configure database access
- Create and query flexible document schemas without migrations
- Build aggregation pipelines for analytics on operational data
- Handle schema evolution by adding fields to existing documents
- Integrate MongoDB with Python for complete data pipelines
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