Date post: | 18-Dec-2014 |
Category: |
Technology |
Upload: | mongodb |
View: | 1,843 times |
Download: | 0 times |
Key Data Management Requirements for the IoT
Register to Reserve your Seat
Webinar 1: Vision & Use Cases
Webinar 2: Data Management Requirements for the IoT
Webinar 3: From Concept to Code
Joint Webinar Series
Today’s Speakers
Dirk Slama, Director of Business DevelopmentBosch Software Innovations
20+ years of experience in large-scale distributed application projects including M2M and IoT projects
Joe Drumgoole, Director Partner Technical Services MongoDB
20+years experience of commercial software delivery
• IoT Vision from Bosch: Recap of Part 1
• Data Management Requirements in IoT
• Next Steps
Agenda
5
7,1tn IoT Solutions Revenue | IDC
Some Big Numbers:
1,9tn IoT Economic Value Add | Gartner
309bn IoT Supplier Revenue | Gartner
50bn Connected Devices | Cisco
14bn Connected Devices | Bosch SI
Some small numbers:
http://postscapes.com/internet-of-things-market-size
Peter Middleton, Gartner:“By 2020, component costs will have come down to the point that connectivity will become a standard feature, even for processors costing less than
$1“
IoT Predictions by 2020 - 22
Ten
s
Hu
nd
red
s
T
ho
us
an
ds
Mil
lio
ns
B
illi
on
s
Co
nn
ec
tio
ns
Internet of Things
Machine-to-Machine
Monitored
Smart Systems (Intelligence in Subnets of
Things )
Telemetry and
Telematics
Smart HomesConnected Cars
Intelligent BuildingsIntelligent Transport
SystemsSmart Meters and
GridsSmart Retailing
Smart Enterprise Management
Remotely controlled and managed
Building automation
ManufacturingSecurityUtilities
Internet of Things
SensorsDevicesSystemsThings
ProcessesPeople
IndustriesProducts Services
Source: Machina Research 2014
Growth in connections generates an unparalleled scale of data
Data
Big data
Changing data models
Real-time Processing
Aggregation
Internet of Things
Large estates of devices
Evolving applications
All forms of data
Data streaming and processing
Pre-IoT (M2M)
Limited estate of devices
Single purpose applications
Structured / Semi-structured
Data transfers (sensors and
actuators)
Evolution from M2M to IoT and Big Data
Source: Machina Research 2014
Data
Big data
Changing data models
Real-time processing
Aggregation
Databases will need to address new requirements
Scalability
Flexibility
Analytics
Unified View
Source: Machina Research 2014
IoT Foundation: Bosch Suite for IoT
A
D
C
B
Scale
Flexibility
Analytics
Unified View
IoT Foundation: Bosch Suite for IoT
A
D
C
B
Scale
Flexibility
Analytics
Unified View
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
Yesterday’s Tool for Today’s Data?
IoT Data Management Requirements
Rich Applications Single View
Operational Insight
Real-Time
Business Agility
Continuous Innovation
Enterprise-Ready
Secure & Reliable
Multiple Data Sources Process Convergence
Building Rich Applications
Farm to Fork:Track production through supply chain
Quality Assurance:Proactively reduce product wastage
Fleet Management:Compare drivers & vehicles
• IoT apps generate multi-structured data
• Modeled more efficiently as JSON documents
• Exposed to powerful analytics
• Developers more productive– Less time wrestling ORMs
– More time creating apps
Modeling Complex Data
{ vehicle_id: ‘123abc’, vehicle_driver: ‘Miller’, base: ‘London’, tracking: [ { timestamp: ‘2014-01-17-12:00:00’, location: [51.123,-0.232], speed: 55, … }, { timestamp: ‘2014-01-17-12:15:00’, location: [51.224,-0.238], speed: 5, … } }}
Unlocking Business Agility
Customer Insight:Optimize in-store product placement
Smart Factory:Flexible assembly lines, autonomous production modules
Fleet Management:Extend to fuel efficiency, driver safety
Continuous Integration
ID PSI Temp Loc
New Column
3 months later…
• Dynamic database schema
• No need to define upfront
• Enables agile methodologies– Evolves as the application changes
– Eliminates teams co-ordinating ALTER TABLE operations
Creating a Single View
Single View of the Customer:Across channels
Single View of Production Across multiple lines
Single View of the FleetAcross real-time and service history
Business Process Convergence
New Table
New Table
New Column
• Aggregate data from multiple source systems– Real time sensor data blended
with enterprise data
• Define single schema, update whenever the source systems change…or JOIN hundreds of tables!
• Document model & dynamic schema makes single view a reality
Real-Time Operational Insight
Inventory Management:Track stock levels, predict demand
Optimize Production LinesAnalyze robotic performance
Preventative MaintenanceCorrelate baselines to diagnostics
Powerful Analytics on Live Data
Enterprise Ready Platform
Secure Customer DataPrivacy & compliance
Continuous AvailabilityMaximize production capacity
Scale Data VolumesMore sensors, more vehicles
Scalable, Reliable, Secure
Automatic Sharing & Replica Sets
Defense in Depth
Case Study
Field Data Capturing
Project SCFD Structured Capturing
of Field Data Components: Car
brakes, power steering, etc.
Usage patterns: temperature, voltage, etc.
Predictive maintenance, product optimization
Why MongoDB: Constantly evolving
system, from a data capturing and a data analytics point of view
Large amount of streaming data
Asset Management
Stream Processing
Big Data Manageme
ntAnalytics
BRM BRM
Next Steps
Services to Support IoT Apps
TRAININGTraining for developers and administrators – online and in-person
CONSULTINGExpert resources for all phases of IoT implementations
• Listen On-Demand Part 1: IoT Vision & Use Cases, Bosch & Machina
Research
• Register for Part 3: From Concept to CodeRegister Now
• Download the Bosch SI & MongoDB Whitepaper IoT & MongoDB
Learn More
For More Information
www.mongodb.com
www.bosch-si.com
Any Questions