1. Azure Cosmos DB Use Cases
2019

2. Modern apps face new challenges
Managing and syncing data distributed around the globe
Modern apps face new challenges
Delivering highly-responsive, real-time personalization
Processing and analyzing large, complex data
Scaling both throughput and storage based on global demand
*watch Rimma’s video for context and talking points <<need link>> Azure Cosmos DB vision is to be the database for all planet-scale applications and scenarios.
The picture I want you to take away is this.
Build planet-scale databases without worry.
Model the data the way your application needs it to be.
Use the API, tools and framework that you are familiar with.
Offering low-latency to global users
Modernizing existing apps and data

3. Azure Cosmos DB Core (SQL) API MongoDB Table API
Document
Column-family
Key-value
Graph
Turnkey global distribution
Elastic scale out
of storage & throughput
Guaranteed low latency at the 99th percentile
Comprehensive SLAs
Five well-defined consistency models

4. Azure COSMOS DB Use Cases
NoSQL modernization and migration to Azure Cosmos DB
Modernize and build new apps with real-time personalization
HA for reads and writes, extremely low latency at any scale worldwide
Handle peak sales periods with ease
Retail and e-commerce Apps
Modern apps that need to elastically scale to handle spikes in traffic
Deliver relevant real-time personalization
Any modern customer facing application
Leverage IoT telemetry to build differentiated experiences
Manage Device telemetry
Device Registry
Deliver high-quality App experiences globally at any scale
Multi-Player games
Social Clans / Guilds, Leaderboards and Messaging
Top sectors including Retail, IOT/ Manufacturing, Gaming, and ISV; emerging sectors include Financial Services and Health Care.

5. Azure Cosmos Industry Scenarios
Retail
Order Processing Pipeline
Product Catalog
Personalization
Real-time analytics
Financial Services
Audit Trail
Tax Forms
Risk Analysis
IoT + Manufacturing
Device Telemetry
Device Registry
Supply Chain Management
ISV
Content Management (CMS)
Data Interchange
Dev Ops Dependency Management
Knowledge Graphs
Gaming
Social Clans/Guilds
Leaderboards
Messaging
Healthcare
Data Interchange (HL7 FHIR)

6. NoSQL modernization and migration to Azure Cosmos DB

7. Easy to MIGRATE nosql apps to Azure Cosmos DB
Make data modernization easy with seamless migration of NoSQL workloads to cloud.
Azure Cosmos DB MongoDB API, Cassandra API, and SQL API bring app data from existing NoSQL deployments
Leverage existing tools, drivers, and libraries, and continue using existing apps’ current SDKs
Turnkey geo-replication
No infrastructure or VM management required
NoSQL wire protocol
Azure Cosmos DB:
MongoDB API Cassandra API SQL API
MongoDB
Couchbase
CouchDB
Neo4j
HBase
Cassandra
DynamoDB

8. Migrate Cassandra/DataStax workloads to Azure Cosmos DB
Questions to ask customers with Cassandra workloads
Does the database have high costs of infrastructure, licenses and database management
How much time is spent managing the database vs focusing on innovation? It is hard to manage and configure Cassandra database is hard and time-consuming including:
Capacity Management,
Performance Management
Availability Management
Are you trying to achieve Global scale ? – Building high performing scalable apps across multiple regions is difficult and time consuming
Top reasons for customers to migrate to Azure Cosmos DB
Competitive TCO - Up to 2- 6X in saving when moving from On-Premise/IaaS Cassandra to Cosmos DB
Offers a fully managed service - reduces the need to manage and configure the database
Cosmos DB guarantees high performance anywhere in the world - with industry leading SLAs for high availability and low latency
Target Audience:
ITDM
Head of development
Architects
High Potential Industries:
Retail
Manufacturing (IOT scenarios)
Automotive
Financial Services
Gaming
Top resources to support you with this scenario
NoSQL Migration to Azure SafePassage Program
NoSQL to DB Migration Guide
NoSQL Migration FAQ
Cosmos DB SI Partner List
FY19 NoSQL Migration Offer
Cosmos DB Infopedia Page
KEY SCENARIO CONVERSATIONS
Competitive TCO
Up to 2-6X in saving when moving from On-Premise/IaaS Cassandra to Cosmos DB
There is no DevOps or license fees
You don’t have to worry about high costs for hardware and database maintenance
Elastically scale up and down based on your requirements
Fully Managed Service
Born in the cloud database service and reduces the need to manage and configure the database
Cassandra developers can leverage existing drivers, libraries, and tools
Automatic indexing and partitioning
Elastic scale-out
Guarantees high performance worldwide
Enterprise-level SLAs that guarantee HA for reads and writes and millisecond latency worldwide
High performance at global scale - turnkey global distribution allowing developers to replicate data anywhere in the world in minutes
Multi-Master support across all Azure regions
Only database that offers choice of consistency models
Enterprise-grade security
3rd Party Tools & Services to support Migration
Inmanis Data
Striim
Seller opportunity - How many nodes in current Cassandra DB to identify opportunity size.
Successful Customers

9. Migrating Cassandra Workloads
What was the app they migrated?
SPOC is a notification service for Symantec endpoints.
Every Symantec products (SEP, Norton security product’s) endpoint will register with SPOC and they open a constant long poll to the SPOC server.
For every write done SPOC, there will be a subset of reads happening from clients based on channel.
Whenever there are new changes/updates come to SPOC, they are propagated to all connected eligible devices.
Symantec is migrating multiple workloads from DSE Cassandra.
Leveraging multiple APIs depending on the workload requirements
Chose Azure Cosmos DB because it offers fully managed service, reduces pain of managing and scaling the database and SLAs around high availability and low latency.

10. Migrating from Mongo DB
12/19/ :28 AM
Migrating from Mongo DB
Current challenges that while using Mongo DB on a VM
Ingesting and managing different data from multiple sources with multiple models
Manageability and scalability became a major DevOps concern
Trying to data can be ingested from several products and then consolidated into a single persistent storage was time consuming and challenging.
Bentley is an ISV with several cloud services for manufacturing organizations. As part of this they need to ingest construction data from several products and then consolidated into a single persistent storage.
They turned to Azure Cosmos DB for its fully managed and globally scalable service and its compatibility with MongoDB.
case study here
Why they migrated to Azure Cosmos DB
A highly performant and globally scalable database service
Fully manage service allow Bentley to be more agile and reduced need for data management
Azure Cosmos DB offers a dynamic schema which allowed Bentley to ingest data from multiple sources and creating maps between the various schemas in it.
Why Bentley moved from Mongo to Cosmos DB
Bentley has more than 60 major products and services with multiple data models. Construction data can have information corresponding to architectural, mechanical, structural, electrical, and other disciplines. For example, steel beam is described and referred to in five or six different software packages, where its attributes can be completely different (e.g., structural data would contain physical analysis properties like weight while electrical might not).
Bentley has been working on creating cloud services that offer rule-setting mechanisms where data schema can be inferred upon ingestion. That way, construction data can be ingested from several products and then consolidated into a single persistent storage. Bentley concluded that it could address this discrete data problem by using a database with a dynamic schema and creating maps between the various schemas in it. Initially, Bentley had MongoDB clusters running on virtual machines. But manageability and scalability quickly became a major DevOps concern. That is when Bentley turned to Azure Cosmos DB for its fully managed and globally scalable service that is compatible with MongoDB. Now the company uses Azure Cosmos DB as a single source of truth for various representations of construction data and for maps to the schemas used by other products.
‘Building a flexible, scalable data layer with Azure Cosmos DB will enable us to deliver actionable insights to our users.” says Phil Christensen, Senior Vice President for Reality Modeling & Cloud Services at Bentley Systems.
View the case study here
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

11. Retail

12. Handle peak sales periods with ease
Azure API Apps (backend)
Azure Cosmos DB (database)
Apache Spark
(analytics)
Azure Notification Hub (Push notifications)
Azure Functions
Azure CDN
Azure Storage (files)
Offer customers fast and reliable service quality during seasonal and other high-traffic sales periods.
Instant, elastic scaling handles traffic and sales bursts
Provisioned throughput ensures predictable performance for mission critical microservices (e.g. shopping cart)
Low-latency data access from anywhere in the world for fast, robust user experiences
High availability across multiple data centers
Walmart Labs (aka jet.com) ensures reliable app experience for customers on Black Friday, Cyber Monday, and other high traffic periods

13. Reference architecture
Product catalog
Reference architecture
Item​
Color​
Microwavesafe​
Liquidcapacity​
CPU​
Memory​
Storage​
Geek mug​
Graphite​
Yes​
16ox​
???​
Coffee
Bean mug​
Tan​
No​
12oz​
Surfacebook​
Gray​
3.4 GHzIntelSkylakeCore i7-6600U​
16GB​
1 TB SSD​

14. Reference architecture
Order processing
Reference architecture

15. Reference architecture
Order processing
Reference architecture

16. Deliver relevant Real-Time recommendations
Online Recommendations Service
HOT path
Help customers discover items they’ll love with real-time personalization and product recommendations.
Machine learning models generate real-time recommendations across product catalogues
High volumes of product data can be analyzed in milliseconds
Low-latency ensures high app performance worldwide
Tunable data consistency models for rapid insight
Azure Service Fabric
(Personalization Decision Engine)
Azure Cosmos DB
(distributed model store)
Azure Data Factory
(scheduled job to refresh persisted models)
Shoppers
.com
(Product Details Page)
Azure Event Hub
Azure Data Lake Storage
(offline raw data)
Apache Spark
Offline Recommendations Engine COLD path
ASOS deliver personalized shopping experiences and real-time order updates to 15 Million customers. Helping them grow and win with millennial shoppers.

17. Reference architecture
Recommendation Engine
Reference architecture

18. Reference architecture
Real-time analytics
Reference architecture

19. IoT + Manufacturing

20. Leverage IoT telemetry to build differentiated experiences
Azure Cosmos DB
(Telemetry & device state)
Apache Storm on Azure HDInsight
Azure Storage (archival)
Azure Web Jobs (Change feed processor)
Logic apps
Azure IoT Hub
Diverse and unpredictable IoT sensor workloads require a responsive data platform
Real-time vehicle diagnostics
Instant elastic scaling
No loss in ingestion or query performance
Azure Cosmos DB was chosen due to its ability to ingest data at massive scale with high availability (99.99%) guarantee.

21. Reference architecture
Stream Processing
Reference architecture

22. Reference architecture
Stream Processing
Reference architecture

23. IoT, Big Data optimize operations at ExxonMobil subsidiary
Find a better way to monitor remote wells and collect data on performance
Must be cost efficient
Unified device management and streaming
Automate IOT and analytics
We had a team of five people working on this, and they built it from scratch. The ease of use of the Azure services and the support we got from Microsoft made that possible. .

24. Reference architecture
Stream Processing
Reference architecture

25. Reference architecture
Stream Processing
Reference architecture

26. Gaming

27. Deliver High-Quality Experiences At Any Scale Globally
Azure API Apps (backend)
Azure Cosmos DB (database)
Apache Spark
(analytics)
Azure Notification Hub (Push notifications)
Azure Functions
Azure CDN
Azure Storage (files)
Need a database that seamlessly responds to massive scale and performance demands
Multi-player game play with low latency
Instant capacity scaling from launch onward
Uninterrupted global user experience
The Walking Dead: No Man’s Land chose Azure Cosmos DB because of its extremely low latency and massive scale worldwide.

28. Reference architecture
Leaderboards
Reference architecture

29. Reference architecture
Game Analytics
Reference architecture

30. Financial Services

31. Fidelity build Mortgage Insurance App to enhance customer experience
Fidelity built a new application – EXOS – it is the only mobile digital mortgage application designed specifically to extend and enhance every critical consumer touchpoint throughout the entire mortgage lending life cycle.
EXOS offers a real-time personalized experience for customers across the entire mortgage process including
Appointment scheduling and communications – enhancing customer experience and process .
Ensuring consistent , personalized and accurate information for customer throughout the process.
EXOS Closing offers unmatched consumer satisfaction and transparency in to the closing process.
Fidelity chose Azure Cosmos DB due to the Ease global distribution, ability to scale and fully managed service reducing the database management overhead.

32. a financial trend saas engine for investors
Need a database that can handle any schema and adapt quickly to rapid changes
Financial SAAS engine with no dev ops
Super fast to handle financial data
Scalable on demand, globally distributed
Business models are under attack, especially in the financial industry. Azure Cosmos DB is a technology that can adapt, evolve, and allow a business to innovate faster in order to turn opportunities into strategic advantages.

33. Real-time payments pipeline
Steady state - 10M transactions/day, peak hours - 3-4K transactions/sec
Financial SAAS engine with no dev ops
Super fast to handle financial data
Scalable on demand, globally distributed
Centralize payment pipelines, build real time processing, analytics. Goal to introduce a common pipeline accepting transactions from all different sources and distributing them to the right pipeline and also other sources like analytics.​

34. Reference architecture
Securities processing
Reference architecture

35. Reference architecture
Image classification
Reference architecture

36. ISV

37. maps out successful strategy with CosmosDB
World’s third largest mapping agency
Support for spatial queries and standards.
Identify every roof top in Britain.
Scalability and flexibility to handle millions of properties.
The solution can identify roof types of all 35.7 million properties in Britain in less than 24 hours with 95% accuracy.

38. Migration from Mongo DB
12/19/ :28 AM
Migration from Mongo DB
Current challenges that while using Mongo DB on a VM
Ingesting and managing different data from multiple sources with multiple models
Manageability and scalability became a major DevOps concern
Trying to data can be ingested from several products and then consolidated into a single persistent storage was time consuming and challenging.
Bentley is an ISV with several cloud services for manufacturing organizations. As part of this they need to ingest construction data from several products and then consolidated into a single persistent storage.
They turned to Azure Cosmos DB for its fully managed and globally scalable service and its compatibility with MongoDB.
case study here
Why they migrated to Azure Cosmos DB
A highly performant and globally scalable database service
Fully manage service allow Bentley to be more agile and reduced need for data management
Azure Cosmos DB offers a dynamic schema which allowed Bentley to ingest data from multiple sources and creating maps between the various schemas in it.
‘Building a flexible, scalable data layer with Azure Cosmos DB will enable us to deliver actionable insights to our users.” says Phil Christensen, Senior Vice President for Reality Modeling & Cloud Services at Bentley Systems.
View the case study here
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

39. Migration from Cassandra
What was the app they migrated?
SPOC is a notification service for Symantec endpoints.
Every Symantec products (SEP, Norton security product’s) endpoint will register with SPOC and they open a constant long poll to the SPOC server.
For every write done SPOC, there will be a subset of reads happening from clients based on channel.
Whenever there are new changes/updates come to SPOC, they are propagated to all connected eligible devices.
Symantec is migrating multiple workloads from DSE Cassandra.
leverage multiple APIs depending on the workload requirements
Chose Azure Cosmos DB because it offers fully managed service, reduces pain of managing and scaling the database and SLAs around high availability and low latency.

41. Appendix

42. Summary of top performing Industry Use Cases
Top Challenges
Use Cases
Why Azure Cosmos DB Won
Azure Cosmos DB Customers 
Retail /
e-commerce
Ensure high performing app regardless of seasonal demands and peak traffic
Deliver differentiated customer experiences with personalization
Ability to be agile and ensure faster time to market
Order and payment Processing
Retail-time Personalization
Inventory Management
Product Catalogs
Elastic scale to handle seasonal traffic
Guarantees high availability and low latency access across anywhere in the world
Schema-agnostic storage and automatic indexing to handle diverse product catalogs, orders, and events
Manufacturing /IOT
Leverage data from multiple devices to build differentiated experiences/ enhance processes or leverage for analytics
Ingest huge volumes of data from multiple sources worldwide
Ability to be agile and able to quickly respond to issues
Device Telemetry
Device Registry
Dependency
High scalability to ingest large # of events coming from many devices
Low latency queries and changes feeds for responding quickly to anomalies
Schema-agnostic storage and automatic indexing to support dynamic data coming from many different generations of devices
Guarantees high availability and low latency across multiple data centers
Gaming
Ensure high quality game experience for large volumes users and handle bursts of traffic
Create fast and responsive gameplay for users all over the world
Agility to allows teams to iterate quickly to fit a demanding ship schedule
Support leaderboards and social gameplay
Social Clans / Guilds
Leaderboards
Messaging
Low-latency queries to support responsive gameplay for a global user-base
Schema-agnostic storage and indexing allows teams to iterate quickly to fit a demanding ship schedule
Change-feeds to support leaderboards and social gameplay
Financial Services
Audit Trail
Tax Forms
Underwriting / Risk Analysis
To be updated !

43. Resources Solutions Architectures
ASOS case study (retail, real-time personalization)
Next Games case study (gaming, elastic scaling)
Johnson Controls story (IoT)