From Relational Databases to NoSQL
summary
Compares relational databases and NoSQL by linking concrete use cases to concrete stores: transactional banking favors RDBMS with ACID, email and document-like content favor document stores; MongoDB reduces impedance mismatch by storing and querying native JSON, CouchDB uses HTTP/REST and offline sync, Cassandra targets Facebook‑scale distributed clusters, and Redis serves as a fast key‑value cache. Argues against a single “golden hammer,” shows when denormalization and embedding improve agility, and recommends mixing stores when each tool fits a distinct requirement.
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chapters (20)
- 0:00Introduction
- 4:20The Goldilocks Problem
- 11:18Relational Databases
- 19:20Email
- 22:43MongoDB CouchDB
- 25:41Cassandra
- 30:47MongoDB
- 34:03Redis
- 36:48Tools
- 38:21Quote
- 39:29Language Characteristics
- 44:43Truthy Equations
- 47:58MongoDB in the MEAN Stack
- 50:05Query Language
- 51:02Insert
- 51:53MapReduce
- 52:51MongoDB Release
- 53:49DBAs
- 56:32Denormalization
- 58:02Conclusion
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