Downloaded 212 times
![Instead Match the Data in your
Application
Relational MongoDB
{!
!"IssID" : 65134,!
!"IssuerName" : "First European
! ! ! ! !Growth Inc.",!
!"actions" : [!
! !{!
! ! !"Company_Meeting" : "ISCHG",!
! ! !"EventID" : 20056,!
! ! !"SecID" : 75128!
! !},!
! !{!
! ! !"Company_Meeting" : "LSTAT",!
! ! !"EventID" : 2716296,!
! ! !"SecID" : 75128!
! !}!
!]!
}!
12](https://image.slidesharecdn.com/1303-referencedatawebinar-130319102338-phpapp01/75/Webinar-How-Banks-Manage-Reference-Data-with-MongoDB-12-2048.jpg)
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Webinar: How Banks Manage Reference Data with MongoDB
1. MongoDB is well-suited for reference data solutions due to its dynamic and flexible schema, built-in replication and high availability features, and tag aware sharding which allows for geographic distribution of data. 2. A case study of a global broker dealer showed how MongoDB could replace expensive and complex ETL processes for distributing reference data, saving over $40 million over 5 years. 3. Key benefits included real-time data distribution, faster querying of local data, and avoiding regulatory penalties from delays in data distribution.
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Webinar: How Banks Manage Reference Data with MongoDB
- 1. Managing Reference Data with MongoDB
- 2. Daniel Roberts Solution Architect 10gen
- 3. Introduction 10gen is thecompany behind MongoDB – the leading next generation database Document- General Open- Oriented Purpose Source 3
- 4. MongoDB functionality MongoDB Relational Key/Value or Column Stores 4
- 5. Database Landscape • No Automatic Joins • Document Transactions • Fast, Scalable Read/Writes 5
- 6. Relational Database Challenges DataTypes Agile Development • Unstructured data • Iterative • Semi-structured • Short development data cycles • Polymorphic data • New workloads Volume of Data New Architectures • Petabytes of data • Horizontal scaling • Trillions of records • Commodity servers • Tens of millions of • Cloud computing queries per second 6
- 7. Financial Services UseCases 1. Risk Analysis & Reporting 2. Tick Data Capture & Analysis 3. Portfolio and P&L Reporting 4. Product Catalog and Trade Lifecycle Management 5. Trade Repository 6. Quantitative Analysis & Automated Trading 7. Order Capture 8. Reference Data 7
- 8. Reference Data • Howdo you globally distribute reference data? – Polymorphic data • Price / Products / Securities Master • Counterparty information - KYC • Corporate Actions • Golden / Single source truth – Often changing in structure, • e.g. new products – Often High volume • How is this typically solved today? 8
- 9. Current Implementations • Whatdo reference data solutions look like today? • Storage – Relational Database or Caching Technologies • Replication – ETL or Messaging • Complex, Costly and Brittle – Maintenance • schema changes • infrastructure – Multiple technologies 9
- 10. Why MongoDB? • Whatfeatures in MongoDB are ideally suited for Global replicated reference data systems? 1. Dynamic and flexible schema 10
- 11. Relational: All Datais Column/Row IssID IssuerName PVCurrency 117883 DWS Vietnam Fund USD 69461 Independence III Cdo Ltd USD 102862 Zamano Plc EUR 73277 Green Way BMD 65134 First European Growth Inc. CHF SecID EventID Company_Mee9ng IssID 762288 407341 AGM 117883 81198 243459 SDCHG 69461 422999 410626 AGM 102862 422999 243440 SDCHG 102862 75128 20056 ISCHG 65134 11
- 12. Instead Match theData in your Application Relational MongoDB {! !"IssID" : 65134,! !"IssuerName" : "First European ! ! ! ! !Growth Inc.",! !"actions" : [! ! !{! ! ! !"Company_Meeting" : "ISCHG",! ! ! !"EventID" : 20056,! ! ! !"SecID" : 75128! ! !},! ! !{! ! ! !"Company_Meeting" : "LSTAT",! ! ! !"EventID" : 2716296,! ! ! !"SecID" : 75128! ! !}! !]! }! 12
- 13. Benefits of MongoDB’sDocument Model • Expressiveness of Data Modeling – A single document can express and encompass a wide variety of noTons • Flexible Modeling – No need to migrate for simple extensions • Simplifica9on of Data Modeling – Fewer collecTons as most data can be encapsulated in a single document • Easier Development – Developers understand documents as it maps well to their data structures • Faster Time to Market – Agile development means faster results And enables beEer data locality => faster performance and scaling 13
- 14. Why MongoDB? • Whatfeatures in MongoDB are ideally suited for Globally replicated reference data systems? 1. Dynamic and flexible schema 2. Built in replication and high availability 14
- 15. High Availability • Automatedreplication and failover • Multi-data center support • Improved operational simplicity (e.g., HW swaps) • Data durability and consistency 15
- 16.
- 17. Why MongoDB? • Whatfeatures in MongoDB are ideally suited for Globally replicated reference data systems? 1. Dynamic and flexible schema 2. Built in replication and high availability 3. Tag Aware Sharding (Geo) 17
- 18.
- 19. Tag Aware Sharding EMEA NA APAC 19
- 20. 1. Case Study:Global Broker Dealer - Reference Data Management ETL ETL ETL ETL ETL Feeds & Batch data ETL • Pricing Source • Accounts Master Data ETL • Securities Master (RDBMS) • Corporate actions Each represents • People $ • Hardware $ Destination • License $ Data • Reg penalty $ (RDBMS) • & other downstream problems 20
- 21. Solution with MongoDB Real-time Real-time Real-time Real-time Real-time Feeds & Batch data Real-time • Pricing • Accounts Real-time MongoDB • Securities Master Primary • Corporate actions Each represents • No people $ • Less hardware $ • Less license $ • No penalty $ MongoDB • & many less Secondaries problems 21
- 22. Case Study: Globalinvestment bank Distribute reference data globally in real-time for fast local accessing and querying Problem Why MongoDB Results • Delays up to 20 hours in • Dynamic schema • Will save about distributing data via ETL management: update $40,000,000 in costs and • Had to manage 20 immediately & in one penalties over 5 years distributed systems with place same data • Greater throughput means • Auto-replication: data charging more to internal • Incurring regulatory distributed in real-time groups penalties from missing SLAs • Both cache and database: • Network and disk speed is • Stale data caused cache always up-to-date the bottleneck, not operational issues software and applications • Simple data modeling & analysis: easy changes and understanding 22
- 23. Summary • Why MongoDBfor Reference Data solutions? 1. Dynamic and flexible schema 2. Built in replication and high availability 3. Tag Aware Sharding (Geo) 23
- 24. Q&A Up And Coming FSwebinar in April - Tick database • http://www.10gen.com/webinar/using-mongodb-as-tick-database FS webinar in April - Risk • http://www.10gen.com/webinar/mitigate-risk-with-mongodb MongoDB Days - London, San Francisco, and NYC • http://www.10gen.com/events MongoDB 2.4 Release • http://www.mongodb.org/downloads
- 25. Key Features JSONData Model with Auto-Sharding for Dynamic Schema Horizontal Scalability Rich, Document-Based Flexible, Full Index Support Queries Built-In Replication and Fast, In-Place Updates High Availability Aggregation Framework and GridFS for Large File Storage Map/Reduce 25
- 26. For More Information Resource User Data Management Location MongoDB Downloads www.mongodb.org/download Free Online Training education.10gen.com Webinars and Events www.10gen.com/events White Papers www.10gen.com/white-papers Customer Case Studies www.10gen.com/customers Presentations www.10gen.com/presentations Documentation docs.mongodb.org Additional Info info@10gen.com 26