This presentation describes how FramTack architected their IoT product to send and receive data between IoT devices and cloud databases using an open source REST API platform called DreamFactory. This was presented at the Bay Area Open Source Software meetup at SAP on October 29th, 2014 http://www.meetup.com/Bay-Area-Open-Source-Meetup/events/211202322/

1. dreamfactory
Ben Busse
@benbusse
benbusse@dreamfactory.com
Architecting IoT for the Cloud
A Case Study

2. About DreamFactory
Open Source Software
Apache license
Q4 2013 - Version 1.0
Q1 2015 - Version 2.0
Strong developer and cloud vendor adoption
REST API Platform
Run-time server software
Auto-generates APIs for SQL, NoSQL, file storage
Use Cases
REST APIs and server-side security for enterprise mobile apps
REST APIs for IoT data

3. Development Process
Install Connect Develop
DreamFactory
provides REST API
Services to your data
Build apps for phone,
tablet, desktop or IoT
device
+ =
Install DreamFactory
on IaaS cloud, PaaS
cloud, or server

4. Unified REST Interface
DreamFactory Fragmented APIs
Files NoSQL SQL
SQL
NoSQL
Files

5. FramTack IoT Case Study
Software Vendor
Solution Family Product for IoT
Solution Engine for processing IoT data
Solution Builder for configuring data collectors, rules, and
statistics
Reduces cost and time required to build IoT engine yourself
Building Automation Use Case

6. Solution Family Suite
6

7. Edge
Solution
Engine®
Data
Model
Clouds
Storage
Analytics
Appliances
IoT Data Flow
2. Analyze Data
Solution Builder®
1. Get Data
3. Send Data to/from Cloud
4. Control the Appliance
5. Build Dashboards

8. Building Automation Example
Pump Room Space Temps
Intel
Gateway
Temperatures Pressures
Intel Gateway + PLC
Solution
Engine®
Steam Room
Temperatures Pressures
Intel Gateway + PLC
APT1 Lobby
Electric Meter
Analytics

9. From Sensor to End User
Solution Builder
Solution Engine
Mobile App
Dashboard
DreamFactory Admin
Console
Service Platform
Solution Family
Products
IOT Data to Cloud via REST
Alerts and Analytics via REST
DB Connection, Schema, Data

10. Dashboard Builder
7/10/13 10

11. Discussion
Data explosion
What data is actually useful for end users?
• Transactional vs aggregated data
• Tolerance thresholds for alerts
• Learning from false positives and false negatives
Where does data processing occur (e.g. gateway vs cloud)?
• Complexity of analysis
• How transient is the data (e.g. one day vs one month)?
IoT trade-offs
Business Requirements – e.g. what data matters, what frequency?
Cost – e.g. API calls, bandwidth, storage
Speed – e.g. how “real-time” must the data be?
Scalability – related to data explosion considerations above

12. Thank You!
QUESTIONS
benbusse@dreamfactory.com
nat.frampton@solution-family.com