Competitive edgewithmongod bandpentaho_2014sep_v3[1]
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The document discusses how MongoDB and Pentaho can help financial services gain a competitive edge by addressing industry transformation drivers like regulatory changes, competition, and operational efficiencies. It highlights the importance of maximizing customer engagement, leveraging automation, and utilizing agile data management to respond to changing market needs. Various capabilities of MongoDB and Pentaho, including data integration and analysis, are demonstrated to support financial services' evolving requirements.
![Rich Querying & Indexes Required
13
Objects + Rich Querying
Multiple Fields • Select John’s holdings
• Select everyone holding MSFT
Geospatial • Find the nearest branch right now
Text Search • Find all customers that mention China
in their call activity (for a new product)
Aggregation • Calculate the value of John’s portfolio
• Show holdings by customer
Map Reduce
• For those that hold greater than 50,000
shares of each sector, what is the next
largest sector they hold?
{ !
customer_id: 100,!
customer_name: ‘John Smith’ !
as_of_date: 2014-06-11,!
last_updated_location: !
[45.123, 47.232],!
phone: [‘212-555-1212’, !
‘917-111-2222, …]!
holdings: [ !
{ symbol: “MSFT”,!
quantity: 10000, … },!
{ symbol: “IBM”,!
quantity: 20000, … }, …!
]!
}!](https://image.slidesharecdn.com/competitiveedgewithmongodbandpentaho2014sepv31-141022155232-conversion-gate02/85/Competitive-edgewithmongod-bandpentaho_2014sep_v3-1-13-320.jpg)
![MongoDB capabilities
ApplicaGon
Driver
Mongos
Shard
2
14
Shard
1
Primary
Secondary
Secondary
Primary
Secondary
Secondary
…
Shard
N
Primary
Secondary
Secondary
db.customer.insert({…})!
db.customer.find({ !
name: ”John Smith”})!
1. Dynamic
Document
Schema
{ name: “John Smith”,!
date: “2013-08-01”),!
address: “10 3rd St.”,!
phone: [!
{ home: 1234567890},!
{ mobile: 1234568138} ]!
}!
2.
Na>ve
language
drivers
4.
High
performance
- Data
locality
- Indexes
- RAM
3.
High
availability
- Replica
sets
- Strong
Consistency
5.
Horizontal
scalability
- Sharding](https://image.slidesharecdn.com/competitiveedgewithmongodbandpentaho2014sepv31-141022155232-conversion-gate02/85/Competitive-edgewithmongod-bandpentaho_2014sep_v3-1-14-320.jpg)
Competitive edgewithmongod bandpentaho_2014sep_v3[1]
- 1. Gaining a Competitive Edge in FS with MongoDB and Pentaho Matt Kalan Business Architect, Financial Services at MongoDB [email protected] @matthewkalan Bo Borland Vice President, Field Technical Sales at Pentaho [email protected] @boborland
- 2. • Financial Services Industry Drivers • Traditional and Desired User Scenarios • Data Management Requirements • MongoDB Capabilities • Pentaho Capabilities • Pentaho BA Demo - Analyzing MongoDB data • Pentaho DI Demo - Blending Disparate Data • Questions 2 Agenda
- 3. FS Industry Transformation Drivers of change • Lost revenue (fees, prop 3 trading) • Better risk management • Regulatory change and uncertainty • New competitors • Emerging markets opportunities • Proliferation of channels • Globally distributed operations • Faster market movements Requirements • New products and new markets • Increase wallet share • Agility to respond to competitors & regulators • Firm-wide, cross-silo reporting • Cost savings • Cross-channel and global integration • Intraday decision support • Operational efficiencies
- 4. How to Respond to Transformation 4 How to Address • Maximize customer engagement • Enable cross-silo regulatory and operational reporting • Leverage automation and tools for analytics and notifications • Based on agile, comprehensive, and timely data management Requirements • New products and new markets • Increase wallet share • Agility to respond to competitors & regulators • Firm-wide, cross-silo reporting • Cost savings • Cross-channel and global integration • Intraday decision support • Operational efficiencies
- 5. Traditional Interactions 5 Customer Customer has rela>vely limited value from advisor Phone/email/IM 3. Calls client 4. Client having a baby and looking for a new house 5. Already got mortgage and set up ESA 6. Wants to talk again in 3 months Investment Advisor Investment Analysis Has minimal customer intelligence Mostly just publishing out informa>on Fundamentals Pricing News 1. Decide to check-‐in aDer a quarter 2. Review porKolio, research, and past correspondence
- 6. Desired Interactions 6 Customer Phone/email/IM Investment Advisor Investment Analysis 1. Uses online savings guide w/ 1 child Much greater and frequent customer intel Customers benefit from high value app(s) and more relevant advice Richer and relevant informa>on and engagement Data Analysis Fundamentals Pricing News Twi8er Blogs/RSS Facebook Central Bank info Pa8ern analysis 2. No>fica>on 5. Timely call to check-‐in 3. Uses mobile app 6. Client is having a to research Tesla baby and wants a mortgage 4. No>fica>on 7. You suggest an ESA and mortgage proposal 8. You also discuss Tesla and ba[ery technology Single view of Customer
- 7. How to Manage All This Data
- 8. RDBMSs not engineered for these modern applications Data Types • Unstructured data • JSON & Digital • Polymorphic data 8 Volume of Data • Petabytes of data • Trillions of records • Millions of queries per second Agile Development • Iterative • Short development cycles • New workloads New Architectures • Horizontal scaling • Commodity servers • Cloud computing Single Views • Disparate data • Intraday • Cross-channel/silo • Global
- 9. Many shapes of investment and customer data symbol: “TSLA”, type: ”news”, headline: “Tesla…”, url: ”http://...”, } 9 {symbol: “TSLA”, eps: -1.11} tweet: “Nice car…”, type: ”tweet”} {symbol: “TSLA”, type: “fundamental”, mktCap: 34.93, eps: -1.11} {symbol: “TSLA”, type: “price”, bid: 280.31, offer: 280.51, date: 2014-08-23, bidQty: 300, offerQty: 100} Investment and Market Data custID: 1000, type: ”mResearch”, symbol: “TSLA”, sector: ”Auto”, ...} {custID: 1000, type: ”call”, ....} custID: 1000, type: ”researchPaper”, doc: ”AutoOverview”, ...} {custID: 1000, type: “email”, date: 2014-09-14, subject: “Tesla”} {custID: 1000, type: “savingApp”, income: 200000, mthSvngs: 10000, date: 2014-08-15, numChild: 1, offerQty: 100} Customer Activity Data
- 10. Differently shaped data are spread across many systems … Bank mobile app 10 Website app Wealth Mgmt App Banking CRM app Investment Banking CRM app ONE COMMON MODEL CustID | Activity ID | Date | Type | 100s or 1000s fields mostly agreed up front
- 11. Need to aggregate it in one dynamic database … Bank mobile app 11 Website app Wealth Mgmt App Banking CRM app Investment Banking CRM app COMMON FIELDS CustomerID | Activity ID | Type… Single View DYNAMIC FIELDS Can vary from record to record
- 12. Easy Horizontal Scaling Required ApplicaGon One Logical Database ParGGon 2 12 ParGGon 1 • No impact to application • Minimal impact to operations ParGGon N • Elastic capacity as you need it • Automatic balancing Primary Primary Primary …
- 13. Rich Querying & Indexes Required 13 Objects + Rich Querying Multiple Fields • Select John’s holdings • Select everyone holding MSFT Geospatial • Find the nearest branch right now Text Search • Find all customers that mention China in their call activity (for a new product) Aggregation • Calculate the value of John’s portfolio • Show holdings by customer Map Reduce • For those that hold greater than 50,000 shares of each sector, what is the next largest sector they hold? { ! customer_id: 100,! customer_name: ‘John Smith’ ! as_of_date: 2014-06-11,! last_updated_location: ! [45.123, 47.232],! phone: [‘212-555-1212’, ! ‘917-111-2222, …]! holdings: [ ! { symbol: “MSFT”,! quantity: 10000, … },! { symbol: “IBM”,! quantity: 20000, … }, …! ]! }!
- 14. MongoDB capabilities ApplicaGon Driver Mongos Shard 2 14 Shard 1 Primary Secondary Secondary Primary Secondary Secondary … Shard N Primary Secondary Secondary db.customer.insert({…})! db.customer.find({ ! name: ”John Smith”})! 1. Dynamic Document Schema { name: “John Smith”,! date: “2013-08-01”),! address: “10 3rd St.”,! phone: [! { home: 1234567890},! { mobile: 1234568138} ]! }! 2. Na>ve language drivers 4. High performance - Data locality - Indexes - RAM 3. High availability - Replica sets - Strong Consistency 5. Horizontal scalability - Sharding