Vector, Graph, and DynamoDB
1h 12mIntermediate2024-07-18
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
In this course, MLOps expert Noah Gift explores vector and graph databases, powerful tools for managing and extracting insights from large, complex datasets. As data volumes continue to grow, scalability is crucial. We'll learn how vector and graph databases can efficiently store data while maintaining relationships, enabling more advanced analytics. Through real-world examples, you'll see how these databases unlock scalability for machine learning, fraud detection, social networks, and more.
Skills covered
Cloud ServicesCloud ComputingOne-Off
Concepts
1. Vector, Graph, and DynamoDB
- 01 - Picking a database
- 02 - Intro to Amazon Neptune
- 03 - Key Rust CLI
- 04 - Rust CLI Graph lab
- 05 - What is SQLite - Key features
- 06 - ETL with SQLite
- 07 - ETL with SQLite - Demo
- 08 - What is DynamoDB
- 09 - What are vector databases
- 10 - Using CRUD with DynamoDB and the CLI
- 11 - Using CRUD with DynamoDB and Python
- 12 - Using CRUD with DynamoDB and Rust
- 13 - Learn AWS CloudShell - Demo
- 14 - Learn AWS CodeCatalyst - Demo
- 15 - Learn AWS CodeWhisperer - Demo
- 16 - Create a table with the CLI
- 17 - Populate table batch
- 18 - Query a table with values
- 19 - Project walkthrough
- 20 - Semantic search
- 21 - Quickstart Qdrant
- 22 - Qdrant Rust client
- 23 - Vector database architecture
- 24 - Enhance semantic search
- 25 - Graph databases
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