8. Data Has Changed
• 90% of the world’s data was
created in the last two years
• 80% of enterprise data is
unstructured
• Unstructured data growing
2x faster than structured
10. Requirements of a Modern Database
Flexible Data Model Rich Queries
Scalable
Dynamic Schema
Grow Fast
Secondary Indexes,
Aggregations, Search
Agile
Continuous Innovation
Enterprise-Ready
Secure & Reliable
11. MongoDB: Powering Modern Applications
Big Data Product & Asset
Catalogs
Security &
Fraud
Internet of
Things
Database-as-a-
Service
Mobile
Apps
Customer Data
Management Single View
Social &
Collaboration
Content
Management
Intelligence Agencies
Top Investment and
Retail Banks
Top Global Shipping
Company
Top Industrial Equipment
Manufacturer
Top Media Company
Top Investment and
Retail Banks
13. Aggregated data from 70
source systems to create
single customer view
Live in 3 Months
“Introducing technology like
MongoDB to our
development teams…
empowered them to deliver
work in months that would
typically take years.”
Gary Hoberman,
CIO and Senior Vice President,
Regional Application Development, MetLife
14. • Fastest growing messaging app in India
• Requirements
– Scale fast, run real time analytics, ensure enterprise SLAs
• Scaled to 15m+ users in just 9 months on MongoDB
– MongoDB sharding and replica sets
– MongoDB query framework
– MongoDB subscriptions for support, best practices and advanced
security features
15m Users in 9 Months
15. • Create single
subscriber profile
across multiple
services
• Tried Oracle,
couldn’t scale
• Switched to
MongoDB
– Built the project 4x
faster with 50% of
the development
team.
– 10x lower latency
4x Faster to Market
17. Compatibility Matching System
used to match potential
partners
“With our...SQL-based system, the
entire user profile set was stored on
each server, which impacted
performance and impeded our ability
to scale horizontally.
MongoDB supports the scale that our
business demands and allows us to
generate matches in real-time.”
Thod Nguyen, CTO, eHarmony
95% Faster Matches
18. 10x Higher Performance
“Replacing our legacy database with MongoDB helps position us for
future growth.
MongoDB improves our ability to support continued customer demand
and the growth of our vehicle history database..”
Joedy Lenz, CTO, CARFAX
19. • Extend HR apps to mobile
devices
• Why MongoDB
– Developer agility: document
model & dynamic schema
– Functional: Powerful query
framework
– HA: Replica sets
• Results
– Service Continuity: MongoDB
subscriptions & MMS
Continuous Availability
21. Tier 1 Bank $40m Savings
Golden
Copy
Batch
Batch
Batch
Batch
Batch
Batch
Batch
Batch
Reference Data
Management
• Built on RDBMS
• Up to 36 hours to
replicate globally
Impacts
• Regulatory fines for using stale data
• High licensing costs
• Expensive hardware
23. • Personalized digital photo products
– Built on Oracle, hit the scalability wall
• Why MongoDB
– Rich data model supporting new query
patterns
– New features built in weeks not months
– MMS for monitoring & issue resolution
• Results
– 9x higher performance from simplified
code base
– 80% cost reduction
80% Lower Cost
26. • Automate (beta)
– Provision in minutes
– Hot upgrades
• Backup
– Continuous incremental
backups
– Point-in-time recovery
• Monitor
– Visualize 100+ system metrics
– Custom alerts
MongoDB Management Service
27. We’re Your Partner
TRAINING
Certification & training for developers and
administrators – online and in-person
CONSULTING
Expert resources for all phases of MongoDB
implementations
28. 1,000+ Customers
OF THE WORLD’S LARGEST
GOVERNMENTS3
OF THE WORLD’S LARGEST
TECH COMPANIES8
OF THE WORLD’S LARGEST
ELECTRONICS MANUFACTURERS10
OF THE WORLD’S LARGEST
MEDIA AND ENTERTAINMENT COMPANIES10
OF THE WORLD’S LARGEST
FINANCIAL INSTITUTIONS10
OF THE WORLD’S LARGEST
RETAILERS10
OF THE WORLD’S LARGEST
TELECOMM COMPANIES10
32. For More Information
Resource Location
MongoDB Downloads mongodb.com/download
Free Online Training education.mongodb.com
Webinars and Events mongodb.com/events
White Papers mongodb.com/white-papers
Case Studies mongodb.com/customers
Presentations mongodb.com/presentations
Documentation docs.mongodb.org
Additional Info info@mongodb.com
Resource Location
33.
34. Migrated product catalog
from RDBMS
“Quite frankly we were
blown away by the
performance of
MongoDB. Between the
move to Amazon and by
opting for MongoDB,
we’ve saved about £2m
over the last three years”
Neil Jennings,
Lead Architect, Orange Digital
Commodity Economics
35. Demand for new types of application
Adapt to handle rapidly changing, multi-structured data
36. Unlock value from new data
Process and analyze multi-structured data in real-time
37. More data than ever before
Seamlessly scale database capacity & performance
38. Smaller windows of market
opportunity
Enable agile development methodologies
39. New architectures increase flexibility,
decrease cost
Ride the economies of commodity & cloud computing
Maintain strict enterprise SLAs
Hinweis der Redaktion
Database typically deeply embedded in a tech stack. Users never really get to see it – so does the choice really matter, and can you measure what it gives you
Give you specific examples – will explore these and others in more detail later
Examples: Deliver apps that weren’t previously possible – build in 3 months what couldn’t be built in previous 8 years
Drive 80% cost reduction
Why is the database so important
At the heart of the change is data, and the role in plays in modern apps
• 90% of the world’s data was created in the last two years
• 80% of enterprise data is unstructured
• Unstructured data growing 2x faster than structured
In the digital economy, data is the raw currency. How you stores, manages, analyzes and uses data has a direct impact on the your success.
RDBMS was only real database option up until relatively recently – great for structured data, but no good for multi-structured, polymorphic data generated by todays applications
Even historically, the RDBMS only held 15-20% of an organisation’s information assets. We now have the tools and technologies that can harness the other 80%
To summarise the requirements of a modern database to meet needs of a modern apps, you need:
Flexible data model to store multi-structured, rapidly changing data
Run rich analytics
Need to support agile dev methdologies to accommodate constantly changing requirements
Scalable on commodity hardware to handle growth
Cannot sacrifice enterprise quality – uptime or security
Over 100k production deployments who have adopted MongoDB to support these requirements – across a wide range of use cases
Lets start to drill into some examples of where the choice of database has had a tangible business outcome – start with TTM
MetLife is one of the world’s largest insurance companies.
Masses of data around 100m customers and 100+ products and policies stored in 70 different source systems
Wanted to bring that together to create single view for better customer experience when they called into a CSR. Also identify risk of churn and cross-sell, upsell
Started project back in 2005, most recent initiative working on a RDBMS had taken 2 years, cost $10ms, still not successful. Developing a single schema that could take data from 70 systems wasn’t possible, as soon as changes to source, then schema broke
Realised needed to change assumptions
Started with MongoDB – built a poc in 2 weeks, and went into production 3 months later
Key was flex schema
Used MongoDB subscription gaves access to expertise used in dev phase to get them on the right path, and stands with them in production. Also use MMS for proactive monitoring and alerts so can maintain uptime
Launching an app in the worlds 2nd highest population – 1.3bn (17%), need to know it can scale
Hike is India’s fastest growing messaging app – joint venture between Bharti and Softbank – 2 huge multinational companies
Using MongoDB, scaled to over 15m users in 9 months. Key for them
– MongoDB sharding and replica sets for scale and availability
– MongoDB query framework for running complex analytics
– MongoDB subscriptions for support, best practices and advanced security features
Final example in this section
One of world’s largest telecoms providers with operations across Europe and Latam
Much like Metlife, needed to build single view of their subscriber
Specifically focused on landline, mobile, IPTV, app store and location- based services. Like many telcos, these services all had their own databases, making it impossible to get real time view of the subscriber
Started initial dev on Oracle database.
To build single view, needed a schema with 20 separate tables, typical operations requiring 35 separate JOINs. Auth alone was an operation that joined 5 tables
Didn’t scale. Also loads to EDW were taking too long. 3 versions of the prototype over 15 month period
Knew needed a new approach – evaluated MongoDB
More flex data model meant could represent data in 5 collections, rather than 20 tables, most operations hit 1 or 2 collections at most
They engaged early, used MongoDB Dev Subscriptions and Training to get them on right path
Result – compressed dev cycle by 4x – used 50% of the dev resource. Storage reduced by 4x, query latency by 10x
Looked at time to market – look at perf and availability – has direct impact on customer experience
one of the world’s leading relationship service providers,
relies on compatibility matching system to introduce potential partners,
relies on analyzing a user’s traits and preferences.
To run matching across their entire use base taking 15 days on RDBMS – too long.
Looked for alternatives – found using flex data model and rich queries, along with ability to shard to scale out, they could reduce matching time to 12 hours – 95% improvement
Use combination of consulting and subscriptions to put dev on right path and simplify their operations
Maintain vehicle history database – enables potential buyers of used cars to verify provenance of a vehicle – service history, previous owners, damage reports
Originally built on a K/V store,
As database grew, hit scalability limits. Also very complex to run DR across multiple DCs, impact service availability
So evaluated a range of solution – chose with MongoDB
Couldn’t tolerate eventual consistency – added to much complexity to their apps
MongoDB met their scalability requirements, got a cluster with around 50 nodes, distributed across 2 datacenters, serving 12.5bn docs – 10x faster than their existing K/V store
final example in this section
ADP one of the world’s leading HR and payroll outsourcing solutions provider,
Wanted to extend access to their core HR apps to mobile devices
Uptime and functionality were critical
After extensive evaluation of multiple databases, ADP selected MongoDB
MongoDB’s document model and dynamic schema made it fast for developers to build the application.
Maintain functionality due to rich query model
Replica sets and MMS application give them service continuity – through replication and proactive monitoring and alerting
Final section – driving cost out
Slide 25 –
Tier1 bank migrated reference data mgt app from RDBMS to MongoDB
App had a master copy of the data generated in New York, replicated to 12 global data centers consumed by local apps, and used for reconciliation and reporting. But took up to 36 hours for data replicate because of the complexity of the schema. Bank was operating on old data – hit with fines. Also high licensing and maintenance costs, high expensive h/w.
Moved to MongoDB, using built in DC-aware replication, replicate across DCs in minutes
Moved expense model from capex to opex
In total, bank estimates saved $40m over 5 years
Another RDBMS user, Shutterfly, built platform supporting digital photo products on Oracle.
Need to release new products to market faster, run deeper analytics, reduce cost.
Trying to represent complex objects in Oracle wasn’t scaling, so looked at alternatives, chose MongoDB
Reduced dev sprints from months to weeks. They’ve simplified their code base by eliminating ORM – improved average query response time by 9x
Reduced cost – 80% lower than Oracle
In addition to the technology, use MMS for proactive monitoring. Speeds up issue resolution as all key metrics can be accessed by support team at MongoDB, means cut down going backwards and forwards with logs
MongoDB subscription – bundle of services and features designed to make you successful faster
Typically value starts before production, because consultative support can provide assistance in schema design, data migration H/W selection, sharding, testing. Then there with you in production – more than just break/fix – regularly check in to proactively address issues. Access to training and consulting
Get access to advanced security features, including Kerberos auth, LDAP integration and auditing for compliance
Also got access to Automation, DR and Monitoring
MMS is the application to manage your MongoDB environment – ops teams love
Automation – provision anything from single replica sets to large sharded clusters spanning regions in a single operation. Not just the database, provision underlying h/w on prem and in the cloud. Used to automate online upgrades – again, click of a button. Tech preview
MMS Backup provides DR – only solution providing continuous incremental backup, point in time recovery and snapshots of sharded clusters
MMS Monitoring – tracks over 100 variables including operations counters, queues, system utilisation. Can create alerts – sent to pagerduty, hip chat, email and text
MMS is a free hosted service provided by MongoDB – so you connect your systems to it. Or you can deploy on-prem as part of a subscription
How we hAlso dedicated consulting and training service delivered remotely or on site – brings MongoDB expertise to your project
elp
Now have over 1k customers of these services – includes nearly 1/3 of Fortune 100, can see strong use across multiple industry sectors
What I hope I’ve demonstrated is that a database can deliver quantifiable biz advantage
What I hope I’ve demonstrated is that a database can deliver quantifiable biz advantage
– final example is telecoms operator Orange who operate mainly in UK, Germany and France
Running product catalog on a RDBMS, on-premise, but growth in users and content meant started to look to new app, DB and hosting capabilities
Decided to move DB layer to MongoDB and rehost environment to AWS where they could scale on demand and take advantage of lower pricing. Architecture of AWS fits well with MongoDB. Saved $2m
Key to success was working closely with MongoDB engineers to optimize app design and production playbooks
Applications are getting much more sophisticated – Handle and aggregate mobile, social, sensor data and real-time analytical applications are essential for remaining relevant.
Semi-structured and unstructured data does not lend itself to be stored and processed in the rigid row and column format imposed by relational databases, and cannot be fully harnessed for analytics if stored in BLOBS or flat files. It is critical to select a database that can not just store complex data, but also enables rich query and analytics capabilities in order to increase business visibility across a variety of data assets.
Traditional s/w dev methodologies predicated on defining all the requirements at start of the project – any changes often meant changing the data model – so things get very slow. Reality is todays apps are developed much more iteratively where requirements change frequently, using agile methodologies. Need a database that can handle those changes, without having to change your schemas. Can do it in dev, and do it production, without downtime
Rise of commodity servers and cloud computing changes the cost model of infrastructure, companies keen to ride that economic curve. RDBMS designed for scale up, rely on ever larger hardware. Can scale them out, takes huge engineering efforts and lose lots of benefits of relational model – denormalise, lose JOINs, lose trx that cross nodes
To take advantage of the economies, you need databases that can can scale out natively, with in built replication so they can take advangtage elasticity of the cloud and handle fact commodity servers do fail
Need to ensure security of the data