this document (including, without limitation, any product … · 2018. 9. 18. · this document...

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. It is for informational purposes only and its contents are subject to change without notice. © Copyright 2014-2018 TIBCO Software Inc. All rights reserved. TIBCO Proprietary Information.


Next Generation Salesforce Integration Leveraging ML and Serverless

Abram Van Der Geest, Kalyan Sripathi & Rahul Kamdar

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DISCLAIMERDuring 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.

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Shifting Patterns Event-driven SaaS connectivity

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Business Users triggering actions, flows & applications

Events trigger SaaS actions, flows & applications


Shifting Patterns Technology vs. Persona driven

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Focused on the tool & improving capabilities around an existing

pattern of development

Focused on the user using the capability and building tooling

around her/his preferences


Shifting Patterns Algorithmic vs. Learning Driven

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Conventional programming approach executes a series of steps based on defined flow

Learns with user and machine inputs to improve results for

given data

InputData

Flow of steps

Static generated

Output

InputData

Sample Data

Dynamicpredicted

Output


User Persona Amber

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• Responsible for marketing campaigns

• Is Sassy, savvy and smart

• Likes ease of use

• Wants to be independent

• Spends time on Twitter

• Uses Marketo, Salesforce, Microsoft 365

• Doesn’t care about APIs, REST, Web Services

Wants to connect SaaS apps and data

quickly..


Most common integration problem?

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• Move and extract data across apps

• Create filters, rules and relationships

between objects across SaaS

• Not too technical, not overwhelming

• Do it quickly, independently

• Less cost, fast results


Introducing TIBCO Cloud™ Integration for Salesforce

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Standalone Service Offering

Targeted for

availability in Salesforce®

AppExchange

Built on Salesforce®

Lightning Platform


Let’s walk through the capability…

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Salesforce Lightning

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• Event-based triggers on Salesforce objects

• Persisting users selected objects

• Persisting map definition

• Creating dynamic flows and communicating with AWS Lambda™


AWS Lambda

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• Uploading a Flogo®

Golang binary to Lambda

• Flow of flows (Creating flows on the fly)

• Passing data to the dynamic flow

• Pushing data to selected SaaS app

• Relaying the response back to Salesforce


Marketo®

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What do we do on the Marketo side?

• Fetching access token from Marketo (IDM)

• Invoking REST APIs to fetch metadata and required fields

• Pushing data to Marketo


How does this all work together?

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Mapping Service

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• Starts with using natural language processing techniques, such as the mathematical representation of both the source and target fields to identify primary matches

• DNN Classifier Estimator from TensorFlow that allows us to learn with user input and improve results gradually over a period of time


Smart Mapper: NN to map Salesforce to Marketo

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● Email● Last Name● First Name● Company● Street● City● State/Province● Country● Status

● Email Address● First Name● Last Name● Company Name● Address● City● State● Country● Lead Status

DB 1 DB 2


● Levenshtein distance○ How many changes to transform

string 1 into string 2○ Changes: substitution, add

character, remove character○ i.e. cat -> dog : distance of 3

● Booleans of whether source and target are substrings of the other○ i.e. dog & hotdog: True○ i.e. slept & sleep: False

Smart Mapper: Two Types of Features Used

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Numerical Representation of Label Meaning

String Comparison

● GloVe vectors word embedding● 25 dimensional vector from 25B

Tweets● Difference between field labels and

the 2 vectors themselves used

*https://www.tensorflow.org/tutorials/representation/word2vec


Smart Mapper: Model Structure

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● DNN Classifier Estimator from TensorFlow● Hidden Layers: [100,40,2]● Trained on data constructed from known

Salesforce and Marketo objects● Each pair of object fields determined if it is

a mapping or not● Return a score of how likely a mapping● Highest score selected as mapping● Manual Mapping stored to improve smart

mapping model

clf = tf.estimator.DNNClassifier(model_dir=model_output_loc,hidden_units=[100,40,2],feature_columns=feat_cols,n_classes=num_labels,

n_classes=num_labels,label_vocabulary= label_names,optimizer=

tf.train.ProximalAdagradOptimizer(learning_rate=learn_rate,l1_regularization_strength=0.001))

clf.train(input_fn=get_input_fn_from_pandas(train),steps=10000)

● Email, Email Address, 0.98● Email, First Name, 0.10● Email, Last Name, 0.03● ...● City, Company Name, 0.2● City, Address, 0.31● City, City, 0.89● …● Status, Lead Status, 0.89

● Email● Last Name● First Name● Company● Street● City● State/Province● Country● Status

● Email Address● First Name● Last Name● Company Name● Address● City● State● Country● Lead Status

DNN Model

Example output


● Built using Flogo with REST API trigger● Why API and not Lambda?

○ Dictionary of word embeddings (5s to load)● Deployed on EC2 instance (docker image)

● API POST provides lists of source and targets● Loop over each target and source

○ Prepare features○ Calls Activity/Tensorflow Inference

● Outputs json where each source has list of targets and corresponding “matching score”

● Golang code available on Github (link below)

Smart mapper deployed as REST API using Flogo

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https://github.com/abramvandergeest/flogo-components/tree/master/apps/smartmapper

● An open source tool for deploying event driven apps across platforms

● Interfaces: web based UI, CLI, and Golang Lib● Full stack of tools with Flows, Streaming, and

Contextual Decisioning● Uses:

○ Triggers to start a reaction to an event○ Activities to perform an action in response to

trigger● Create Apps for: Serverless, Cloud APIs, Edge● Several related sessions:

○ Serverless Integration - 1pm○ Connected Intelligence at the Edge - 1pm○ Building Smart Edge App - 2pm

Project Flogo Smart Mapper - REST API


What’s particularly valuable?

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Smart Mapping

• Allows us to learn from the user and capture relationships based on business domains that otherwise only the users would know

• Loosely coupled – so can be reused in other Integration capabilities

• Can enable org-specific data access, training

Powered by FaaS

• Extracts compute of the flows outside of the SaaSapps itself thereby removing lock-in and tight coupling

• Leverages benefits of AWS Lambda – speed, cost, scale

• Can be moved to other FaaS solutions

• Can be org-specific

Ubiquitous SaaS UX

• Empower business users in UX they are most familiar with

• Starting with SalesforceApp to other SaaS targets

• Loosely coupled architecture allows us to replicate capability across major SaaS apps

• Interested in demand/feedback


Key Takeaways

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• Nex-gen integration for SaaS Apps – embeddable in YOUR apps

• Leverages Deep Learning for smart mapping

• Application of AWS Lambda & Project Flogo Golang API



Questions?

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