Adopting Modern Application Architecture with AWS and Project Flogo (Sponsored by TIBCO) - AWS Summit Sydney

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Adopting Modern Application Architecture With
AWS and Project Flogo
Anshul Sharma
Solutions Architect
TIBCO Software Inc.

Agenda
© Copyright 2000-2019 TIBCO Software Inc.
• What’s an event, anyway?
• Making sense of events
• The event-driven ecosystem:
Project Flogo®
• Real-time streaming & ML
demo: training with Amazon
SageMaker

3
Event-driven by design
e·vent
əˈvent/
noun
noun: event; plural noun: events
- a thing that happens, especially one of
importance.

4
Event-processing
The simple notion is that an event is just that, an event. Whether
it be one or millions, it’s how you process them that differs.
Stream: Aggregate/Filter Noise Integrate Correlate & Rules
If (….)

5
Sensors Everywhere

6
Does this mean that ML is
always the answer?
Streaming: Data Aggregation
• Median, mean, time weighted
averages, variability/robustness
• Sometimes streaming data analytics
in real time is sufficient for your
problem!
Machine Learning
• Classify large quantities data,
such as, images, text, etc
• Broad set of patterns needs to
be detected
• Sufficient data must be available

Project Flogo®: Event Driven Ecosystem

8
Event-driven Ecosystem
10-50x lighter than
Java, .NET or Node.js
100% Open Source
Stack for all things
event-driven
Machine LearningDeploy as serverless
functions, containers or
to IoT edge devices
Common core for all
event-driven capabilities
Project Flogo®
Open Source Ecosystem for Event-driven Apps

Integration Flows
Stream Processing
Microgateway
Contextual Rules

Powerful event-driven
programming model based
on triggers and actions
Internal channel for action
chaining. Bundle all app
needs into a single bin
Common contribution model
enabling you to leverage 100s
of activities & triggers
Simple interface to extend event
processing capabilities
⚡
️
Flogo® Core
Event-driven App Kernel for Serverless & IoT Edge

Conditional control flowUltralight process engine for
application integration
Powerful step-back debugging
capabilities in web browser
Integration Flows
Ultra-lightweight App Integration for Serverless and Edge

12
Flogo Flows Web UI
Low friction UX
• Express app logic using rich
flows, not just data or
request pipelines
• Inline data transformations
• Built-in web-based debugger
• Build for target platform
directly from UI
• Available on Docker HubⓇ or
Flogo.io

Lightweight stream process
for edge devices
f(x)
Aggregation capabilities Join streams from multiple
event sources
Filter out the noise
Flogo Streams
Stream Pipeline for Edge & Cloud-native

14
Grouping & Joining Events Across Triggers
With FlogoⓇ Streams you can group & join events across multiple
different streams of data
Joins occur against pipeline input/output
Aggregate Filter
Inference
AI/ML
Apache
KafkaⓇ
MQTT
GPIO
Aggregate Filter
Inference
AI/ML
Aggregate Filter
Inference
AI/ML
group: $input.deviceId

15
Aggregation
Tumbling Windows
Sliding Window
Accumulate f(x)
Operations:
• Tumbling
• Time Tumbling
• Sliding
• Time Sliding
Functions:
• avg, sum, min, max,
count, accumulate

Real-time Decisions to
determine next Best
Action/Outcome and
Experience
Open Source/Core
complete with
extensions, APIs and
Interfaces
Declarative Rules/Rapid
Application
Development
Stateful/Contextual
Reasoning across time
and space
Rules Action for Flogo
Real-time Contextual Decisions

17
Machine Learning Capabilities
Execute TensorFlow™
Models
100% Open Source
with zero lock-in
Native Machine Learning

18
Why ML @ the Edge?
• Data Volume / Generation
• Data collection exceeds ability to transport
• Intelligent Aggregation
• Reduces transfer & storage costs
• Smarter, more efficient networks
• Predictions
• Smarter Device Actions -> Less Network Latency
• Actions resilient to network connectivity issues
The Issues:
• Prediction Lag
• Massive Data Transfers
• Connectivity Requirements
..ML Challenges Amplify the Issues of IoT Integration!
Gateway Cloud
Devices
Store and
train models
here
Analytics can
happen here
...Or here

The Demo Scenario

20
Outlier Detection of Event Streams
• Industrial IoT
• Predictive maintenance
• Detect broken products to stop faulty
production lines
• Networking / Cyber Security
• Detect intruders / bandwidth variability
• Healthcare
• Patient monitoring and alerting
• Transportation
• Financial services
• etc...

21
Streaming Structure
Machine Learning
Sensors
Join and Filter
Streams
Action

Demo

23
Key Takeaways
• Machine Learning and Events changing software
• Project Flogo provides ML and Streams to handle real-time events
• Combining Flogo with AWS SageMaker and AWS Lambda provides
tremendous opportunities

24
Getting Started with Flogo &
Amazon SageMaker
https://www.flogo.io/ Amazon SageMaker Free Tier*:
• 250 hours ml.t2.medium notebooks
• 50 hours ml.m4.xlarge training
• Combined total of 125 hours
ml.m4.xlarge hosting
* For terms and conditions, please visit
: http://aws.amazon.com/free
github.com/TIBCOSoftware/flogo
github.com/awslabs/amazon-
sagemaker-examples

DISCLAIMER
During the course of this presentation, TIBCO or its representatives may make forward-looking statements
regarding future events, TIBCO’s future results or our future financial performance. Although we believe that the
expectations reflected in the forward-looking statements contained in this presentation are reasonable, these
expectations or any of the forward-looking statements could prove to be incorrect and actual results or financial
performance could differ materially from those stated herein.
TIBCO could experience factors that could cause actual results or financial performance to differ materially from
those contained in any forward-looking statement made in connection with this presentation. TIBCO does not
undertake to update any forward-looking statements that may be made from time to time or on its behalf.
This document (including, without limitation, any product roadmap or statement of direction data) illustrates the
planned testing, release and availability dates for TIBCO products and services. This document is provided for
informational purposes only and its contents are subject to change without notice. TIBCO makes no warranties,
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particular purpose. This document may not be reproduced or transmitted in any form or by any means without
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The material provided is for informational purposes only, and should not be relied on in making a purchasing
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functionality. The development, release, and timing of any features or functionality described for our products
remains at our sole discretion.

Adopting Modern Application Architecture with AWS and Project Flogo (Sponsored by TIBCO) - AWS Summit Sydney

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