Downloaded 17 times
![45
AGILITY
• Flexible Data Model
• Native Drivers for Major Languages
• Single Technology Platform
• Continuous Delivery/Integration
• Ease of application development
Morphia
MEAN Stack
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location: [45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
RDBMS](https://image.slidesharecdn.com/acceleratingthepathtodigitalwithaclouddatastrategy-170821150416/85/Accelerating-the-Path-to-Digital-with-a-Cloud-Data-Strategy-45-320.jpg)
Uploaded byMongoDB
Accelerating the Path to Digital with a Cloud Data Strategy
This document discusses accelerating digital transformation through a cloud data strategy using MongoDB. It begins by outlining MongoDB's capabilities as a cloud data platform, including its use by over 3000 enterprises. The document then discusses how time to market has replaced cost as the primary driver for cloud adoption. It also outlines considerations for choosing a cloud data platform like deployment flexibility, reducing complexity, agility, resiliency, scalability, cost, and security. The document then provides an overview of MongoDB's cloud offerings, including MongoDB Atlas on public clouds, MongoDB Ops Manager for private clouds, and MongoDB Stitch for backend services. It also discusses best practices for replatforming applications from relational databases to MongoDB in the cloud.
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Accelerating the Path to Digital with a Cloud Data Strategy
- 1. Accelerating the Pathto Digital with a Cloud Data Strategy Accelerating the Path to Digital with a Cloud Data Strategy
- 2. Agenda Executing on acloud data strategy to accelerate the path to digital Customer case study : TEG Like vs Live : Emotional Data at scale A route to the cloud: Best practices in replatforming from relational databases to cloud-native MongoDB
- 3. Multi-model Built formission critical workloads Adoption & Growth 3000+ Enterprise Customers 2500+ Atlas Customers Scalable and Resilient Who Is MongoDB?
- 4. 40% 6% 30% 23% Align operational expenditurewith value Scale to new geographies and markets Lack the required in-house expertise Reduce time to market and faster app delivery “What is your primary reason for using cloud technologies?” While cost was once widely-cited as the driver of cloud adoption, time to market has taken its place.
- 5. 5 Key decision criteria DeploymentFlexibility On-premise, Private, Public, or Hybrid without vendor lock-in Reducing Complexity Broad use case applicability to avoid additional complexity Agility Accelerate time to market and speed of change for the business Resiliency Engineered for high availability across distributed architectures Scalability Elastically grow with demand Cost Aligned to actual demand and value but with predictability Security Leverage best in class and appropriate security controls What to think about when choosing a cloud data platform
- 6. MongoDB Cloud Data Strategy SahirAzam VP, Cloud Products & GTM sahir.azam@mongodb.com @sahirazam
- 7.
- 8. Software is disruptingevery industry Source: Bureau of Economic Analysis Manufacturing Retail Transportation Publishing, Broadcast Education, Healthcare, Social Assistance Finance, Insurance, Real Estate Arts, Entertainment, Food $1.6T $1.1T $1.5T $6.2T $5.3T $2.4T $1.2T
- 9. Churn in theFortune 1000
- 10. 10 Technology Landscape Transitions Platform1: Mainframes Platform 2: Client/Server Platform 3: Cloud/Mobile 1960s-1980s 1990s-2000s 2010-Beyond
- 11. 11 Architecture is shifting Onpremises / self-hosted Monolithic Proprietary Fat Client / Web v1 Cloud Microservices Open source Mobile
- 12. 12 ...as is theOrg Structure Centralized IT Hierarchical Specialized Process heavy DevOps Small, autonomous teams Cross functional Agile (a la Amazon, Google, Netflix)
- 13. 13 API Access Layer OperationalData Customers Products Accounts ML Models Shared Physical Infrastructure App1 App2 App3 1. Development Agility – UI/API for self-service provisioning & scaling 2. Reusable / Flexible Data – Each service’s data is physically isolated into its own database instance – Federated across services with appropriate access controls – Easily store and manipulate varied data 3. Governance and Control – Logically managed as one service – Audited and Compliant Cloud Data Strategy Standardized, On-Demand Database Service Cloud Agnostic Any Cloud, Any Where
- 14. 14 MongoDB Purpose-built for large-scalecloud data Enterprise-Grade Security Highly Available/Global Deployments Robust Monitoring, Automation, and Backup Distributed, Self-Healing Transactional & Analytical Workloads in a Single Platform Multi-Platform, Multi-Cloud
- 15.
- 16. Ops Manager &Cloud Manager MongoDB Atlas On Premises / Self Hosted MongoDB Cloud Technologies Public Cloud Self-Managed Fully Managed DBaaS
- 17.
- 18. 18 MongoDB Ops Manager ManagementPlatform for Private Cloud Monitoring • Deploy, resize, and upgrade your deployments with just a few clicks • RESTful API to integrate with your enterprise orchestration tools • Allocate and create pre- provisioned server pools; Cloud Foundry Integration Automation Backup • Continuous backups to minimize your exposure to data loss • Restore to precisely the moment you need with point- in-time recovery • Dozens of charts tracking key performance indicators • Custom alerts that trigger when key metrics are out of range • RESTful API to integrate with your existing APM tools
- 19.
- 20. 20 MongoDB Atlas: PublicCloud Flexibility Cloud Portability — Maximize flexibility and avoid lock-in
- 21. 21 MongoDB Atlas: Databaseas a Service Self-service, elastic, and automated Secure by default Comprehensive monitoring Global and highly available Continuous backups
- 22. 22 MongoDB Atlas Powering MicroservicesArchitecture Biotechnology giant uses MongoDB Atlas to allow their customers to execute & track experiments from any mobile device Problem Why MongoDB ResultsProblem Solution Results Over 35 different apps accessed by 10,000+ unique customers on AWS Each experiment produces millions of “rows” of data, which led to suboptimal performance with incumbent databases RDS & Aurora slow, added code complexity DynamoDB limited query functionality & expensive MongoDB Atlas managed database service Flexible document model allows storage of multi-structured data Expressive query language and secondary indexes allow ad-hoc analysis of experiment data Scalability to handle growing data volumes Thermo Fisher customers now obtain real-time insights from mass spectrometry experiments from any mobile device or browser; not possible before Improved developer productivity with 40x less code, improved performance by 6x Easy migration process & zero downtime. Testing to production in under 2 months
- 23. MongoDB Stitch Backend-as-a-Service Stitch Integrate servicesw/ complex, multi- stage workflows Native SDKs for Android, iOS, and JavaScript REST API Access
- 24. Use cases forMongoDB Stitch Add a feature to an existing application • Include customer reviews in an e- commerce site • Enrich profiles with uploaded images • Add comments to a blog Selectively expose existing data to new applications • Display a games leaderboard • Provide an API for internal apps to safely access your operational database • Build admin-only page with anonymized stats Integrate with services • Accept text messages from Twilio • Make a GitHub commit trigger a Slack • Send email, text, or Slack message Be your complete backend • Use MongoDB Stitch to orchestrate multiple services behind a unified, secure backend API
- 25. Conclusion 1 Cloud datastrategy is essential for digital transformation 2 It’s more than just a new platform: DevOps, microservices, CI/CD enable new ways of delivering apps faster 3 MongoDB is purpose-built for as flexible, cloud-native platform
- 26. 26 MATT CUDWORTH Group ChiefTechnology Officer
- 27. 27 5LIKE vs LIVE Emotionaldata at scale
- 28.
- 29. 29 35 Days 10 Games Crowd342,909
- 30. 30 Australian Open 2017 Melbourne 14Days, 573 players, 728,763 fans attended 5297 racquets restrung 36,000+ hats sold 70,000+ New York styled hot dogs
- 31.
- 32. 32 50m Fans Directly Engaged Globally 24m Tickets SoldGlobally 80% Arenas & Stadiums Across Australia 0 # Guns and Roses concerts actually went to!
- 33.
- 34. 34 CONTENT GENERATION Sun 27Aug 2017 Intel Extreme Masters BRAZIL Vs ARGENTINA
- 35. 35 AWS Kinesis AWS APIGateway AWS Lambda MongoDB Atlas
- 36.
- 37.
- 38. 38 Australian Open 2017 Melbourne 14Days, 573 players, 728,763 fans attended 5297 racquets restrung 36,000+ hats sold 70,000+ New York styled hot dogs
- 39.
- 40.
- 41. 41 11 Days 70 Nations 274Event Sessions Over 1,000,000 Tickets
- 42.
- 43. 43 Re-platforming to theCloud Mac McIntosh Director of Pre-Sales & Services, APAC mac.mcintosh@mongodb.com
- 44. 44 KEY DECISION CRITERIA DeploymentFlexibility On-premise, Private, Public, or Hybrid without vendor lock-in Reducing Complexity Broad use case applicability to avoid additional complexity Agility Accelerate time to market and speed of change for the business Resiliency Engineered for high availability across distributed architectures Scalability Elastically grow with demand Cost Aligned to actual demand and value but with predictability Security Leverage best in class and appropriate security controls What to think about when choosing a cloud data platform
- 45. 45 AGILITY • Flexible DataModel • Native Drivers for Major Languages • Single Technology Platform • Continuous Delivery/Integration • Ease of application development Morphia MEAN Stack { first_name: ‘Paul’, surname: ‘Miller’, city: ‘London’, location: [45.123,47.232], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } RDBMS
- 46. 46 RESILIENCY • Replica set:3, 5 or 7 copy options in Atlas • Replica sets make up a self-healing ‘shard’ • Built for distributed architectures • Replica sets address: • High availability • Data durability, consistency • Maintenance (e.g. HW swaps, upgrades) • Disaster Recovery Application Driver Primary Secondary Secondary Replication
- 47. 47 SCALABILITY • Increase ordecrease capacity as you go • Automatic load balancing • Three types of sharding • Hash-based • Range-based • Tag-aware Shard 1 Shard 2 Shard 3 Shard N Horizontally Scalable
- 48. 48 SECURITY • Strong technicalcontrols, e.g. authentication, authorization, auditing, encryption, … • Organisation & Processes, e.g. segregation of duties, views, redaction, … • Compliance & Regulations, e.g. data sovereignty, data lineage, data privacy, …
- 49. 49 Cloud Manager Ops Manager MongoDB Atlas Private Cloudor On- Prem Cloud hosted, self managed Public DBaaS: Fully Managed Same Code Base, Same API, Same Management UI DEPLOYMENT FLEXIBILITY
- 50. 50 • Migrate existingdatabase deployments with minimal impact to your applications: • Performs a data copy between the source database and the target database • Then syncs live data between the source database and the target database, continuously • Notifies you when now eligible to switchover Zerodowntimemigrationtocloud(&backagainifneeded) MONGODB LIVE MIGRATION
- 51. 51 REDUCING COMPLEXITY THROUGHAMULTI-MODELDB Document Rich JSON Data Structures Flexible Schema Global Scale Relational Left-Outer Join Views Schema Validation Key/Value Horizontal Scale In-Memory Search Text Search Multiple Languages Faceted Search Binaries Files & Metadata Encrypted Graph Graph & Hierarchical Recursive Lookups Geospatial GeoJSON 2D & 2D- Sphere
- 52.