See how our customers are using AWS IoT Analytics.

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Customer Showcase for AWS IoT
Analytics
Jeff Maynard
Principal Product Manager
AWS IoT Analytics & Applications
I O T 2 1 9
John Morkel
Sr. Software Development Manager
AWS IoT Analytics & Applications

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Agenda
AWS IoT Analytics walkthrough
Demo: Data from ingest to ML container
What are customers doing with AWS IoT Analytics?
Enabling scale: Verizon + City of San Jose
Powering ML: Luxoft + Vantage Power

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What is AWS IoT Analytics?
AWS IoT Analytics is a service that processes, enriches, stores, analyzes,
and visualizes IoT data for manufacturers and enterprises.
Filter, process,
transform, and enrich
your data
Ad hoc queries
or sophisticated IoT
analytics and visualization
Store raw data and
processed data
0 1 0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1 0 1
0 1 0 1 1 0 1 0 1 0 1 0
0 1 1 0 1 0 1 0 1 0 1 1
0 1 0 1 0 1 0 1 1 0 1 0
1 0 0 1 0 0 1 0
ENRICHMENT
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0
0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1
0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1
0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 1 0 1
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0
1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0
1 0 1
IoT data is noisy
and contains gaps
and false readings
10101 1 0 0 1 0
01010
01010
101001
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 01 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
0101001010
101001
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0
1 0 1 0 1 1 0 1 0 1
0 1 0 1 0 1 0 1 0 1
0 1 0 1 0 0 1 0 1 0
0 1 0 1 0 1 0 1 0 1
1 0 1 0 1 0 1 0 1 0

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What’s the workflow look like?
Things
Data Sources
Channel Pipeline
Data store Data set
Advanced
analysis
Visualization
AWS IoT Analytics
Messages AWS IoT
Core
Amazon Kinesis
Amazon Simple
Storage Service
(Amazon S3)

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Components: Channel
Entry point into AWS IoT Analytics
IoT data collection from multiple sources:
AWS IoT Core, Amazon Kinesis, Amazon S3, or other
source via API
Data format agnostic
*Binary, JSON
Elastically scalable
*Binary data formats will need to be decoded
in a pipeline
AWS IoT
Core
Amazon Kinesis
Amazon S3

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Components: Pipeline
Filter
Conditionally purge messages to remove outliers and
erroneous/irrelevant data
Transform
Mathematical and conditional transformations to
convert data (e.g., Celsius to Fahrenheit)
Enrich
Enrichment from MQTT Topic, device registry, & device
shadow
Custom Preprocessing
Use Lambda to add vital context to IoT data (e.g.,
geolocation, weather) or perform complex
transformations
Filter
Pipeline
Custom
Channel

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Components: Pipeline, Cont.
Batching
Messages are batched (default 30 seconds) prior to
enrichment; ensures scalability and optimizing Lambda
executions
Pipeline replication
Replicate your pipelines as your device fleets grow or
enable your Data Science team to test hypotheses in
minutes
Filter
Pipeline
Custom
Channel

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Components: Data store
Authoritative store of raw data from
multiple devices at channel
Immutably stores raw device data for easily
reprocessing using different logic (pipelines) if your
needs change
Managed and optimized processed data
store for time-series and IoT workloads
Partitioned by time
Improved performance on time-series data
More than a single database
Abstraction on top of several database
technologies in a single interface
Manageable data retention policies

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Components: Data set
Query data stores using SQL
Queries can be run ad hoc or scheduled
Popular tabular format
Visualize in Amazon QuickSight
Native Amazon QuickSight connector to easily build
metrics or inspect data sets
Available via API, console download, or
within Jupyter Notebooks and containers
Console Query Editor
Edit and schedule your queries from the console
Data sets

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Components: Jupyter Notebooks + Amazon SageMaker
Jupyter Notebooks
Allows you to build ML models on IoT data using
popular libraries such as Scikit-learn and
TensorFlow
ML Notebook templates
Get started faster with pre-built notebook
templates for common IoT use cases:
Predictive maintenance
Anomaly detection
Fleet segmentation
Forecasting
Amazon SageMaker integration
Use your Amazon SageMaker notebook instances
to run notebooks on AWS IoT Analytics data sets
Jupyter
Notebooks
Predictive Maintenance
Anomaly Detection
Forecasting

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Components: Containers
Execute your own customer analysis,
packaged in a Docker container
Create executable containers from Jupyter
Notebooks in a single click
Visualize your container analysis in console

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Components: Customizable time windows
Incremental data scan capability
through the use of customizable
time windows
Precisely scan only the data you
need for your analysis:
Increased performance
Reduced costs
DeltaTime windows
Data store
t5
t1 t2 t3 t4
Incremental data sets

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Components: Scheduling queries + containers
Schedule your queries to create data sets
for your analytical workloads and/or
container execution
Automate your analytical workflows for
continuous insights and model training
SQL
data set
Container
data setChannel Pipeline Data store

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Summary: AWS IoT Analytics
Channels Data SetsPipelines Data Stores Jupyter Notebooks
& Amazon SageMaker
Query data stores
using standard SQL
Scheduled or ad hoc
queries
Tabular format
Visualize in Amazon
QuickSight
Available via API,
console, Amazon
SageMaker or
containers
Console Query Editor
Managed and
optimized data store
for processed time-
series data
Partitioned by time:
improved
performance on time-
series data
Single interface
Manageable data
retention policies
Entry point to AWS
IoT Analytics
Multiple Sources: AWS
IoT Core, Amazon
Kinesis, Amazon S3, or
other sources through
APIs
Binary, JSON
Elastically scalable
Raw data store
attached
Jupyter Notebooks:
Build ML models using
popular libraries such
as Scikit-learn and
TensorFlow
ML Notebook
templates: predictive
maintenance, anomaly
detection, fleet
segmentation,
forecasting
Integrated with
Amazon SageMaker
Filter
Transform
Enrich
Custom processing
Batched messages
Pipeline replication

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Energy + oil & gas
Customers are using AWS IoT Analytics to better understand the behavior
of their field equipment, apply the appropriate context to their data, and
take action from those insights to gain efficiencies

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Energy + oil & gas
Predictive maintenance on general purpose rotating pumps
Battery cell analysis and conditioning
Smart grid + secondary sensor network analysis
Detecting energy consumption or production anomalies

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Consumer
Customers are using AWS IoT
Analytics to understand the
behavior of their consumer
customers so they can deliver more
unique experiences.
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Consumer
Product Support Management
Anomaly detection -> Support Ticket
Device Telemetry Analysis
Understand consumer behavior/interaction with devices
Power Machine Learning across device fleets

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Manufacturing
Customers are using AWS IoT
Analytics to perform macro analysis
across their manufacturing plants
and identifying anomalies.

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Manufacturing
At Scale Quality Control
Multi-site/region Analytics
Identify trends, anomalies, and provide contextual visibility
Manufacturing Data Lake
Refined outputs feed into larger corporate/reporting data lake

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Smart buildings and cities
Customers are combining previously siloed data to gain insights and
provide data to third parties
Exploring existing data to determine what data is most valuable and
worth deploying physical hardware to capture

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Smart Buildings, Communities, and Cities
Centralize building mgmt. data
Run analysis for each room, floor, and building
Monitor and forecast power grids
Forecast power generation and consumption
Emergency Vehicle Pre-emption
Optimize routes for emergency vehicles

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David Tucker
Head of Product Management,
Smart City, Verizon
Kip Harkness
Deputy City Manager
City of San Jose

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35%
Asian
32%
Latino
3%
Black
26%
White
3%
Two+ races
39% born outside the United States
San Jose

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11.5 million airport passengers per year
9.2 million library items checked out
565,000 police emergency calls per year
69,000 streetlights
38,000 building permits
3,500 acres of parkland
3,000 fires per year
2,400 miles of streets
178 zoo animals
6,200
employees
A staggering
range of City
services…

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Central Emergency Vehicle Pre-emption

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Verizon Smart Communities
Confidential and proprietary materials for authorized Verizon personnel and outside agencies only. Use, disclosure or
distribution of this material is not permitted to any unauthorized persons or third parties except by written agreement.
Developers
145M
Verizon
citizens
Security
Cyber security & data privacy
Platforms & ecosystem
End-to-end services
Telematics SMB consumer
Advertising
Fiber
Small cell4G/5G
Wi-Fi
Creating a positive
impact on communities
with innovative solutions
designed to drive citizen
engagement, economic
development, and
innovation while
improving safety,
efficiency,
environmental
sustainability, and
bridging the digital
inclusion.
Smart Communities

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Smart City customer pain points
“We need to make citizens happy
& get re-elected in next cycle”
“We need to attract innovation
and economic development to
stay competitive”
“I have citizens that are
disadvantaged by not being
included in the digital world”
“We have aging infrastructure
that needs monitoring &
upgrading”
“We have a high crime rate and
limited resources”
“Traffic is impacting citizen quality
of life”
“We need to maximize and protect
our natural resources”
“We have limited resources to
educate our youth”
“Our revenues are declining and
costs are increasing”
“We need new revenue streams”
“We don’t have strong tech
expertise or resources to
manage ecosystem”
“Budget, resources, & systems
are siloed”
“Regulatory and political
constraints often limit our ability
to be creative with solutions”

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33
Simplifying & Enhancing City Data Analysis
Decision making enablement
through generation of insights &
inferences
Cross department collaboration
and sharing of data
Single secure repository for
all city data
Secure data access governed by
strict access control
Easy data movement – import
and export
Easy visualization of data for ad-hoc
analysis , reporting, dash boarding, etc
Citizen engagement through data
access and transparency
Verizon Smart Communities
solution data – lighting, parking,
traffic
Hosted and
managed service
101010
1010
0101010
101

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NetSense platform architecture
This is how the team built the original test architecture, utilizing Kafka to stream
data and EMR/Spark to clean data before it was sent to the raw Amazon S3
Bucket. Amazon Cognito and the Amazon API Gateway were used to support the
Verizon Smart City portal.

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Solution architecture: Future

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Solution Summary
City of San Jose needed a solution that would accurately measure emergency response times,
optimize response routes to assist the City in acquiring full compensation under its ambulance
contract.
Emergency Response Fleet data Traffic Congestion data Traffic Signal Phase data
911 / Dispatch data Historical response data Land Use
Key Data
Sources:

37. “With public safety as a key initiative for my
office, we engaged with Verizon to build an
emergency vehicle route optimization
system that leverages AWS IoT Analytics to
reduce the time to scene for our emergency
services vehicles. This is just one of many
examples in which we can use technologies,
like AWS IoT Analytics and others, to
improve the lives of the citizens of the City
of San Jose.”
Mayor Sam Liccardo
City of San Jose, CA

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About: Vantage Power
Mission: Electrify and
connect heavy-duty vehicles
to radically reduce harmful
emissions.

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About: Luxoft
A global consulting partner for end-to-end digital solutions
that drive business change
Luxoft value proposition:
• Solving complex business challenges at a global scale
• Enabling business transformation
• Driving operational efficiency
Luxoft differentiators:
• Deep domain expertise combined with engineering excellence
• Bespoke attention to your needs, with global scale capabilities
• Two decades of consistent, on-time delivery and management of
complex projects

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The problem

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The solution

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Solution Architecture: Now

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Solution architecture: Future

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Why use AWS IoT Analytics?

46. “Using real-world data
from in-service batteries,
we’ve developed a model
in the cloud that detects a
failure months earlier
than we can today.” Toby Schulz
CTO, Vantage Power

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How this helps Vantage Power

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What’s next?

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51. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Jeff Maynard
jefmay@amazon.com
John Morkel
jmorkel@amazon.com

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