Watch this talk here: https://www.confluent.io/online-talks/building-an-enterprise-eventing-framework-on-demand Learn how Centene improved their ability to interact and engage with healthcare providers in real time with MongoDB and Confluent Platform. Centene is fundamentally modernizing its legacy monolithic systems to support distributed, real-time event-driven healthcare information processing. A key part of their architecture is the development of a universal eventing framework designed to accommodate transformation into an event-driven architecture (EDA). The business requirements within Centene's claims adjudication domain were solved leveraging the Kafka Stream DSL, Confluent Platform and MongoDB. Most importantly, Centene discusses how they plan on leveraging this framework to change their culture from batch processing to real-time stream processing.

1. 1
Building an Enterprise Eventing
Framework
Bryan Zelle, IT Manager, Centene
Robert Walters, Dir. of IoT, MongoDB
Jeff Bean, Solution Architect, Confluent
How Centene Improved their ability to interact and engage with healthcare
providers in real time with MongoDB and Confluent Platform

2. 2
Speakers
Bryan Zelle, IT Manager,
Centene
Jeff Bean,
Partner Solution Architect,
Confluent
Robert Walters,
Dir. of IoT,
MongoDB
Lisa Sensmeier,
Partner Marketing,
Confluent

3. 3
About Centene
Challenges with data integration and migration
Decision process
Centene architecture, use case and data flow
Confluent Platform
MongoDB
Q and A
Agenda

4. Building a Enterprise
Eventing Platform
Bryan Zelle

5. Centene Introduction
Mission Statement:
Transforming the health of the community, one person at a time
Medicaid:
Medicare (Part D):
Marketplace:
Medicare:
Other:
Total:
12,700,000
4,000,000
2,000,000
1,000,000
3,700,000
23,400,000
30 States
50 States
21 States
28 States
33 States
50 States
Membership Composition:
Industry:
Largest Medicaid and Medicare Managed Care Provider
0
5
10
15
20
25
Centene United Health
Group
Humana Anthem CVS
Membership(Millions)
Largest Managed Care Organizations
Medicaid Medicare & Medicare PDP OtherGovernment Marketplace

6. $-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
$80,000
$90,000
$100,000
2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005
TotalRevenus(millions)
Centene Yearly Revenue
Centene Revenue WellCare Revenue
Summary of Centene’s
Key Challenges in one
word…
Growth
$4.1 Billion Revenue to $96.9 Billion in 10 Years
$80.4 Billion in growth in past 5 years
$48.6 Billion in growth in past 2½ years
Envolve
Jan 2015
Wellcare
Mar 2019
Fidelis
Sep 2017
HealthNet
Mar 2016
?
?
Cause of the growth…
Mergers & Acquisitions
By the numbers:

7. Medicare
Medicaid
International
Federal
Marketplace
Addressable Market
Federal Medicare$860 B
40%
State Medicaid
International Market
Federal Services
Health Insurance Marketplace
$2,000,000,000,000 +
Centene Revenue
$97,000,000,000 +
Centene
Revenue
4%
Addressable
Market
96%
Additional Growth
Opportunities
$710 B
33%
$260 B
12%
$120B
6%
$115 B
5%
Centene Growth Outlook
Targeted
Pipeline
($270 Billion)

8. Medicare
Medicaid
International
Federal
Marketplace
Addressable Market
Federal Medicare$860 B
40%
State Medicaid
International Market
Federal Services
Health Insurance Marketplace
$2,000,000,000,000 +
Centene Revenue
$97,000,000,000 +
Centene
Revenue
4%
Addressable
Market
96%
Additional Growth
Opportunities
$710 B
33%
$260 B
12%
$120B
6%
$115 B
5%
Centene Growth Outlook
Targeted
Pipeline
($270 Billion)
Mergers
&
Acquisitions
Data Integration
&
Data Migration

9. Data Integration & Data Migration
1
Shared
Database
• Application Refactor
• Direct Schema Coupling
• Scaling Challenges
• Single Point of Failure

10. Data Integration & Data Migration
Shared
Database
Export
Import
• Application Refactor
• Direct Schema Coupling
• Scaling Challenges
• Single Point of Failure
File
2
File Transfer
(Batch ETL)
• Latent Data
• Direct Database Load
• Consistency Challenges

11. Data Integration & Data Migration
Export
Import
Shared
Database
File Transfer
(Batch ETL)
• Application Refactor
• Direct Schema Coupling
• Scaling Challenges
• Single Point of Failure
• Latent Data
• Direct Database Load
• Consistency Challenges
File
API
API
Function Call
Response
3
• Direct Coupling
• Application Refactor
• Availability Concerns
• Scaling Concerns
Remote Procedure
Invocation

12. Data Integration & Data Migration
Shared
Database
File Transfer
(Batch ETL)
Export
Import
• Application Refactor
• Direct Schema Coupling
• Scaling Challenges
• Single Point of Failure
File
• Latent Data
• Direct Database Load
• Consistency Challenges
API
API
Function Call
Response
• Direct Coupling
• Application Refactor
• Availability Concerns
• Scaling Concerns
Remote Procedure
Invocation
4
Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor
• Highly Availability
• Highly Scalable
• Real-Time Data

13. Data Integration & Data Migration
Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor
• Highly Availability
• Highly Scalable
• Real-Time Data
Shared
Database
File Transfer
(Batch ETL)
Export
Import
• Application Refactor
• Direct Schema Coupling
• Scaling Challenges
• Single Point of Failure
File
• Latent Data
• Direct Database Load
• Consistency Challenges
API
API
Function Call
Response
• Direct Coupling
• Application Refactor
• Availability Concerns
• Scaling Concerns
Remote Procedure
Invocation
*
What is a Event?
Definition: “A significant change in state”
• Statement of fact (immutable)
• Expects no response (or call to action)
• Has a defined “timepoint”
Persistence
• Stateless: Notification Event
• Stateful: Event-Carried State Transfer
Synthesized / Composite Events
E1 E2 E3+• Combination
of Events
E1 E3+• Absence of
an Event

14. Data Integration & Data Migration
Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor
• Highly Availability
• Highly Scalable
• Real-Time Data
Shared
Database
File Transfer
(Batch ETL)
Export
Import
• Application Refactor
• Direct Schema Coupling
• Scaling Challenges
• Single Point of Failure
File
• Latent Data
• Direct Database Load
• Consistency Challenges
API
API
Function Call
Response
• Direct Coupling
• Application Refactor
• Availability Concerns
• Scaling Concerns
Remote Procedure
Invocation
*
What is a Event?
Definition: “A significant change in state”
• Statement of fact (immutable)
• Expects no response (no call to action)
• Has a defined “timepoint”
How do we
publish / consume
meaningful
events?

15. Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor*
• Highly Availability
• Highly Scalable
• Real-Time Data
Change Data Capture (CDC)
Broker Topology
Subscribe to Event Channels (Topics)
Self-Defined Event Routing
Partial Coupling of Event Channels
Reduced Complexity at cost of reduced
coordinating of event execution
Advantages:
• Mature 3rd Party Products / Tooling
• Limited Database Load
• Fast Implementation
• No refactoring of source system
Disadvantages:
• No consistent data structure
• No data governance
• Direct technology coupling

16. Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor*
• Highly Availability
• Highly Scalable
• Real-Time Data
Example Event Payload (Informatica CDC Publisher v1.2)
{
"INFA_SEQUENCE":{"string":"2,PWX_GENERIC,1,,2,3,C7084816514A5D260"}
,"DTL__CAPXUSER":{"string":"USER1"}
,"DTL__CAPXTIMESTAMP":{"string":"201803051315400000000000"}
,"INFA_OP_TYPE":{"string":"UPDATE_EVENT"}
,"INFA_TABLE_NAME":{"string":"d8amisou6p.MEMBER_CONTACT"}
,"MEMBER_PCP":{"string":"Dr. Bryan Zelle"}
,"MEMBER_PCP_Present":true
,"MEMBER_PCP_BeforeImage":{"string":"Dr. John Smith"}
,"MEMBER_PCP_BeforeImage_Present":true
}
Transaction
Metadata
Event
Body
Who - Who changed the data ?*
What - What data changed ?
When - When the data changed ?
Where - Where was the data changed ?
What Event
information are we
capturing?
Change Data Capture (CDC)

17. Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor*
• Highly Availability
• Highly Scalable
• Real-Time Data
Mediated (Orchestrated) Eventing
Mediator Topology
Mediator transfers events to assigned
event channel (Topic)
Centrally Coordinated Event Routing
Complete Decoupling of Event
Channels
Increased Complexity at cost of
increased coordination of event
execution
Advantages:
• Consistent / Common Framework
• Enforce Data governance
• Economy of Scale Advantage
• Technology abstraction / decoupling
Disadvantages:
• External bottleneck (Mediator Owner)
• Single Point of Failure
• Duplicative data storage

18. Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor*
• Highly Availability
• Highly Scalable
• Real-Time Data
Mediated (Orchestrated) Eventing
Example Event Payload (JSON vis REST)
“Metadata” : {
“Transaction ID” : “C7084816514A5D260”,
“User ID” : “USER1”,
“Time Stamp” : “201803051315400000000000”,
“Transaction Type” : “UPDATE”,
“Source System” : “d8amisou6p.MEMBER_CONTACT” } ,
“Event Body” : {
“Event Type” : “Member-PCP-Change”,
“Previous Value” : “Dr. John Smith”,
“Updated Value” : “Dr. Bryan Zelle”,
“Event Source” : “Inbound-Member-Call”,
“Caller Information” : {
“Name” : “Jane Doe”,
“Inbound Number” : “1-614-847-0982”,
“Call Resolution Status” : “5 - Highly Satisfied”,
“First Call Resolution” : “Success”,
“Internal Representative” : “CN-10238381”,
”Call Duration (Minutes)” : “8:19” }
Transaction
Metadata
Who - Who changed the data ?*
What - What data changed ?
When - When the data changed ?
Where - Where was the data changed ?
Why - Why was the data changed ?
What Event
information are we
capturing?
Event
Body
*

19. Pub / Sub Messaging
(Streaming ETL)
Event
MessageBus
• Loosely Coupled
• No Application Refactor*
• Highly Availability
• Highly Scalable
• Real-Time Data
Why is the “Why”
so important?
“Event Body” : {
“Event Type” : “Member-PCP-Change”,
“Previous Value” : “Dr. John Smith”,
“Updated Value” : “Dr. Bryan Zelle”,
“Event Source” : “Inbound-Member-Call”,
“Caller Information” : {
“Name” : “Jane Doe”,
“Inbound Number” : “1-614-847-0982”,
“Internal Representative” : “CN-10238381”,
“Call Resolution Status” : “5 - Highly Satisfied”,
“First Call Resolution” : “Success”,
”Call Duration (Minutes)” : “8:19”
}
If Inbound Number not currently associated
with Member -> Create event to add it
to the Member’s Profile
New Events can be
Created or Derived
If Resolution Status >= 4 -> Create event to
assign call rep to member for call-back
If two separate “Member-PCP-Change”
events happen within 2 weeks window ->
Create event for Case Manager to Review
If first call resolution < > “Success” and call
duration > 15 min -> Create event to
escalate call to Supervisor for Audit

20. Recap Recap
1
Centene’s Core Challenge is Growth
cause by Mergers & Acquisitions;
causing us to revaluate our Enterprise
Data Integration and Data Migration
Strategies…
Event
MessageBus
2
Async Pub / Sub Eventing through
Kafka provides us valuable capabilities:
- Highly Scalable
- High Autonomy / Decoupling
- High Availability & Data Resiliency
- Real Time Data Transfer
- Complex Steam Processing
“Metadata” : {
“Transaction ID” : “C7084816514A5D260”,
“User ID” : “USER1”,
“Time Stamp” : “201803051315400000000000”,
“Transaction Type” : “UPDATE”,
“Source System” : “d8amisou6p.MEMBER_CONTACT” } ,
“Event Body” : {
“Event Type” : “Member-PCP-Change”,
“Previous Value” : “Dr. John Smith”,
“Updated Value” : “Dr. Bryan Zelle”,
“Event Source” : “Inbound-Member-Call”,
“Caller Information” : {
“Name” : “Jane Doe”,
“Inbound Number” : “1-614-847-0982”,
“Call Resolution Status” : “5 - Highly Satisfied”,
“First Call Resolution” : “Success”,
“Internal Representative” : “CN-10238381”,
”Call Duration (Minutes)” : “8:19” }
3
Leveraging a Mediator Topology
enables the creation of meaningful
events; which provide insight into why
things are happening, so we can react
to them in real time…

21. Generic Event
Mediator
Common Core
Architecture:
1) Event Source
2) Event Intake
3) Event Channel
4) Event Router
5) Event Subscription
6) Event Destination
Event
Channel
Event
Router
Event
Subscription
Event
Destination
Event
Intake
Event
Source
Event Mediator
321 4 5 6
Event
Bridge
Event
Grid
Apache
Camel
Knative
Eventing
Mule
ESB
Mediator
Alternatives?

22. Generic Event
Mediator
Required Features & Functionality
Event
Channel
Event
Router
Event
Subscription
Event
Destination
Event
Intake
Event
Source
Event Mediator
321 4 5 6
Design Criteria
1) AVRO Event Serialization
2) JSON Validation of Event Body
3) Centralized Event Registry
4) Distributed Tracing of Events
5) Sensitive Data Redaction
6) Turn / Key Self-Service
7) Cloud Agnostic
8) Permanent Event Storage
9) Flexible Ingestion Intake
10) Pre-built Monitoring / Dashboards
11) Synthetic Events
Reduced Message Size -> Reduced Storage Cost in Cloud
Data Validation -> Clean Data
Easily Find Events -> Prevents Event Duplication & Increases Adoption
Tracing -> Provides Event Lineage and Auditability
Data Restriction -> Protects HIPPA data (including PHI/PII)
Automated Configuration -> Reduced manual administrative burden
Multi-Cloud Strategy -> No Reliance on Single Cloud Provider
Event Persistence -> DR Strategy + Event Sourcing / Hydration
Legacy Systems Limitations -> Offer REST, gRPC, SOAP Interfaces & API’s
Universal Metrics -> Consistent / Granular Event Visibility
Fictitious Event -> Blue/Green Deployments, Prod Smoke Testing, Etc.
Business Value

23. Generic Event
Mediator
Required Features & Functionality
Event
Channel
Event
Router
Event
Subscription
Event
Destination
Event
Intake
Event
Source
Event Mediator
321 4 5 6
Design Criteria
1) AVRO Event Serialization
2) JSON Validation of Event Body
3) Centralized Event Registry
4) Distributed Tracing of Events
5) Sensitive Data Redaction
6) Turn / Key Self-Service
7) Cloud Agnostic
8) Permanent Event Storage
9) Flexible Ingestion Intake
10) Pre-built Monitoring / Dashboards
11) Synthetic Events
Reduced Message Size -> Reduced Storage Cost in Cloud
Data Validation -> Clean Data
Easily Find Events -> Prevents Event Duplication & Increases Adoption
Tracing -> Provides Event Lineage and Auditability
Data Restriction -> Protects HIPPA data (including PHI/PII)
Automated Configuration -> Reduced manual administrative burden
Multi-Cloud Strategy -> No Reliance on Single Cloud Provider
Event Persistence -> DR Strategy + Event Sourcing / Hydration
Legacy Systems Limitations -> Offer REST, gRPC, SOAP Interfaces & API’s
Universal Metrics -> Consistent / Granular Event Visibility
Fictitious Event -> Blue/Green Deployments, Prod Smoke Testing, Etc.
Business ValueLeverage 3rd Party Frameworks
or Build Custom?
Assessment:
Majority of frameworks focused engineering effort on
how to get data into framework as easily as possible
• Higher Data Ingest = Increased Billables (SaaS)
• Too many gaps with current features*
Decision:
Build Centralized Eventing Framework for
Enterprise use across all Centene Domains
*
*
*
*
*
*

24. CentEvent
Architecture
Docker Container
Kubernetes
Intake Application
Axway
Gateway
Serialize
Deserializer
Confluent Schema
Registry
Caffeine
Cache
Authorization Tokens
Event Types
Routing Rules
Routing HASH
Firehose
Topic
Docker Container
Kubernetes
Router Application
Consumer
Topics
Client UI
Event
Discovery UI
Admin UI
Docker Container
Kubernetes
Admin API

25. Docker Container
Kubernetes
Intake Application
CentEvent
Architecture
Axway
Gateway
Serialize
Deserializer
Authorization Tokens
Event Types
Routing Rules
Routing HASH
Caffeine
Cache
Docker Container
Kubernetes
Admin API
Confluent Schema
Registry
Client UI
Event
Discovery UI
Admin UI
Firehose
Topic
Docker Container
Kubernetes
Router Application
Consumer
Topics
0
Configuration / Admin
Collections

26. Event Types
Routing Rules
Routing HASH
Authorization Tokens
Event Types
Routing Rules
Routing HASH
Configuration / Admin
Collections

27. K-Streams Application Code
1) Fetch the Event Key --> Key = N:V:O
2) Join Hashed Routing Rules: maps N:V:O to Consumers that have Subscribed
3) Discard Events if no Consumer has Subscribed
4) Generate Duplicate Event message; one for each subscribed Consumers
5) Validate Consumers effective date is within Event date
6) Validate the Event contains tags the Consumer specifies
7) Validate the Consumers PHI permissions are appropriate
8) Place the Event destination topic onto the Kafka message header (metadata)
9) Send the event to the destination topic specified in the message header

28. VM Provisioning / Management
Infrastructure as Code (IaC)
Configuration Management
Service Administration API
Service Dashboards / Monitoring
Incident / Change Management
Service Alerting / Triggers
Service Logging (Audit / Troubleshooting)
Self-Service Portal
Automated Performance / Load Tests (SRE)
Disaster Recovery / Backup Strategy
SLA / SLO (Defined and Captured)
Binary Repository Management
Incident Response Management (IROC)
Ongoing Production Support
Eventing as a Service
- CentEvent
Incident Response
Management
IROC
Binary Repository
Management
Artifactory
Service Dashboards
/ Monitoring
Mongo Charts
Logging - Audit /
Troubleshooting
ELK Stack
Internal
Customers
External
Customers
Gateway DMZ
Unified Self-Service
Portal
Vue JavaScript
Service Alerting /
Triggers
Pager Duty
Incident / Change
Management
ServiceNow
Performance / Load
Testing (SRE)
Java Micro-Service
Disaster Recover /
Backup Strategy
Mongo Replication
SLA / SLO
Java MS -> Mongo
Configuration
Management
Configuration
Management
Configuration
Management
Internal Cloud
Infrastructure
Traditional
Infrastructure
Cloud
Infrastructure
Centene Cloud
Platform (CCP)
Traditional
VMs (Manual)
Amazon Web
Services (AWS)
Unified Service Administration API
IaC IaC IaC
Ongoing
Production Support
CentEvent Team
Internet Download Ansible
Java Admin API
Axway
Complete
Incomplete
In-progress
AWS Fargate

29. SLA-SLO (Mongo Charts)
Event RTR – Connector + Pipeline
IROC – Run Books + APIs
Regression Testing - Producer
ELK Dashboards - Phase I
Consumer Notifications / Alerts
AWS POC - Phase I
Lambda Function POC – Phase I
Self-Service Portal MVP – Phase I
Event Sourcing – Hydration Pipeline
Oct Nov Dec Jan Feb Mar Apr May June
Q4 - 2019 Q1 - 2020 Q2 - 2020
Operations Product Maturity DemandCentEvent Roadmap
Phase II : Consumer
ELK Dashboard - Phase II
AWS MVP - Phase II AWS Deployment - Phase III
Lambda Deployment - Phase II
Phase II : Deployment

30. Streaming ETL Pipeline
Sharded
MongoDB Cluster
EventRTR
API
1
2
3
4
5
Key Components:
1) CDC Streaming ETL Pipelines
2) CentEvent (Event Mediator)
3) MongoDB Connector (modified)
4) MongoDB Cluster
5) Event RTR API
Universal Centene
Integration Pattern
Framework Features:
Synthetic Events
PHI Filtering
Event Tagging
Distributed Tracing
Event Routing
ELK Monitoring
Self-Service Portal
Event Discovery UI
Real-Time
Repository
Legacy
Source
CentEvent

31. Application Refactor
Use Case
Member Insight Platform Migration
- Refactor from self-managed Kafka to KaaS
- Refactor from Member Insight API to CentEvent
Total Events
Volume
191,000,000
YTD Inbound
Calls
16,000,000
YTD
Correspondence
10,250,000
YTD Member
Services
4,350,000
YTD Outbound
Calls
4,070,000
Member Event Volume continues to grow significantly -
increasing the importance of a stable and scalable
underlying infrastructure of services

32. Event
Providers Intake Processing
Outbound
Channels
Softeon
OMNI
UMV
OCOE
Web
Self-Managed
Kafka
Member Insight
API
Standardize
Data Validation
Reconciliation
Member Events Services
Kafka
Spark
Cassandra
Solr
RestFul
Real-Time
Master Data
Management
Member Insight Process Flow: Before

33. Event
Providers Intake Processing
Outbound
Channels
Softeon
OMNI
UMV
OCOE
Web
Standardize
Data Validation
Reconciliation
Member Events Services
Kafka
Spark
Cassandra
Solr
RestFul
Real-Time
Master Data
Management
Member Insight Process Flow: After
Refactor Required No Refactor RequiredNo Refactor Required No Refactor Required
Managed Service
KaaS
CentEvent

34. 4
Without Confluent

35. 5
Real-Time
Inventory
Real-Time
Fraud
Detection
Real-Time
Customer 360
Machine
Learning
Models
Real-Time
Data
Transformation
...
Contextual Event-Driven Applications
Universal Event Pipeline
Data Stores Logs 3rd Party Apps Custom Apps/Microservices
TREAMSSTREAMS
CONNECT CLIENTS
With
Confluent

36. 6
Confluent Platform
Operations and Security
Development & Stream Processing
Support,Services,Training&Partners
Apache Kafka
Security plugins | Role-Based Access Control
Control Center | Replicator | Auto Data Balancer | Operator
Connectors
Clients | REST Proxy
MQTT Proxy | Schema Registry
KSQL
Connect Continuous Commit Log Streams
Complete Event
Streaming Platform
Mission-critical
Reliability
Freedom of Choice
Datacenter Public Cloud Confluent Cloud
Self-Managed Software Fully-Managed Service

37. 77
Apache Kafka™ Connect API – Streaming Data Capture
JDBC
Mongo
MySQL
Elastic
Cassandra
HDFS
Kafka Connect API
Kafka Pipeline
Connector
Connector
Connector
Connector
Connector
Connector
Sources Sinks
Fault tolerant
Manage hundreds of
data sources and sinks
Preserves data schema
Part of Apache Kafka
project
Integrated within
Confluent Platform’s
Control Center

38. 8
Schema Registry: Make Data Backwards
Compatible and Future-Proof
Deploy with reliability
● Validate data compatibility and get warnings
● Let developers focus on deploying apps
App 1
!
Schema
Registry
Kafka
topic
Scale with confidence
● Store a versioned history of all schemas
● Enable evolution of schemas while
preserving backwards compatibility for
existing consumers
!
Serializer
App 1
Serializer

39. Intelligent Operational Data Platform
Best way to work
with data
Intelligently put data
where you want it
Freedom to run
anywhere

40. MongoDB Connector for Apache Kafka
Build robust data pipelines for Microservices and Event Driven Architectures
Developed with the community and supported by MongoDB engineers, verified by Confluent
Supports MongoDB as a sink and a source for Kafka
Integrate with Change Streams and Atlas triggers to create fully reactive, event driven pipelines
Available on GitHub - https://github.com/mongodb/mongo-kafka
Confluent Hub - https://www.confluent.io/hub/mongodb/kafka-connect-mongodb
https://www.mongodb.com/kafka-connector

41. https://mongodb.com/local

42. 10
Additional Information
Download Confluent
confluent.io/download
Download MongoDB connector
confluent.io/hub/mongodb/kafka-connect-mongodb
Bryan Zelle’s talk at Kafka Summit SF
kafka-summit.org code KS19Online25
Q and A
Please type your
questions in the
Question Panel

43. 11