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Big Data Day LA 2015 - NoSQL: Doing it wrong before getting it right by Lawrence Trinh of Fandango

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The document outlines goals for transitioning to a NoSQL environment, specifically highlighting the use of Node.js and AWS for scalability. It discusses challenges such as managing complex data rules and ensuring system reliability through microservices and a centralized data store. The focus is on improving content management and service ownership for enhanced flexibility and reduced risk.

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NOSQL: DOING IT WRONG BEFORE GETTING IT RIGHT 6-27-2015/Lawrence Trinh
Content
Goals • Accommodate new products – Content – Marketing – Schema changes • Start using Node.js for services and web tier • Move content out to AWS – Improve abilities to scale up/down
Background • Web App – Host website (www.fandango.com) • Backoffice tools – Map data – Modify existing content • SQL Server – Store data
Enter Couchbase • CDC – Monitor SQL Server changes • Export – Grabs changes and pushes to • Import – Contains rules to load data • Custom query language – Sit in front of couchbase to pull/join data
Challenges • Rules – Large number /data type – Import grows quickly • Breaking points – Lots of places things can go wrong • Second source of truth – New tools write directly to Couchbase
Enter Microservices • Data store – Simplified flow – Centralized • Flexibility – (Re)building should be less risky • Service ownership – Easily distributable
Q&A

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Big Data Day LA 2015 - NoSQL: Doing it wrong before getting it right by Lawrence Trinh of Fandango

  • 1. NOSQL: DOING IT WRONG BEFORE GETTING IT RIGHT 6-27-2015/Lawrence Trinh
  • 3. Goals • Accommodate new products – Content – Marketing – Schema changes • Start using Node.js for services and web tier • Move content out to AWS – Improve abilities to scale up/down
  • 4. Background • Web App – Host website (www.fandango.com) • Backoffice tools – Map data – Modify existing content • SQL Server – Store data
  • 5. Enter Couchbase • CDC – Monitor SQL Server changes • Export – Grabs changes and pushes to • Import – Contains rules to load data • Custom query language – Sit in front of couchbase to pull/join data
  • 6. Challenges • Rules – Large number /data type – Import grows quickly • Breaking points – Lots of places things can go wrong • Second source of truth – New tools write directly to Couchbase
  • 7. Enter Microservices • Data store – Simplified flow – Centralized • Flexibility – (Re)building should be less risky • Service ownership – Easily distributable
  • 8. Q&A