03 – NoSQL Ecosystem

NoSQL systems trade strict relational guarantees for scalability, flexibility, or performance in specific use cases.

📌 Overview

• Understand why NoSQL emerged
• Map use cases to the right NoSQL family
• Learn the strengths and weaknesses of each type

🧱 Families

[[Key-Value Stores]]

• Understand key → value access patterns
• Typical use cases: caching, sessions, feature flags
• Learn about examples like Redis, DynamoDB (key-value aspect)

[[Document Stores]]

• Store and query JSON-like documents
• Understand flexible schemas vs schema-on-write in RDBMS
• Learn use cases: content management, user profiles, catalogs

[[Wide-Column Stores]]

• Understand column families and sparse storage
• Learn about Cassandra, HBase, and large-scale write-heavy workloads

[[Graph Databases]]

• Model entities and relationships as nodes and edges
• Use cases: recommendation, fraud detection, social networks

[[Search Engines]]

• Understand why search engines (Elasticsearch, Solr, OpenSearch) are often treated as databases
• Learn concepts: inverted index, relevance scoring

[[Time-Series Databases]]

• Understand time-series workloads: metrics, logs, events
• Learn patterns like downsampling, retention policies

[[Vector Databases]]

• Grasp embeddings and similarity search (kNN)
• Understand how vector DBs help with AI/ML and semantic search

🔗 Connections

• Reuse fundamentals from [[01 - Fundamentals]]: sharding, replication, CAP
• Compare NoSQL trade-offs with [[02 - RDBMS]]
• Connect to [[07 - Data Modeling]] for polyglot persistence and microservice patterns