Apache Kafka users who want to leverage Google Cloud Platform's (GCPs) data analytics platform and open source hosting capabilities can bridge their existing Kafka infrastructure on-premise or in other clouds to GCP using Confluent's replicator tool and managed Kafka service on GCP. Using actual customer examples and a reference architecture, we'll showcase how existing Kafka users can stream data to GCP and use it in popular tools like Apache Beam on Dataflow, BigQuery, Google Cloud Storage (GCS), Spark on Dataproc, and Tensorflow for data warehousing, data processing, data storage, and advanced analytics using AI and ML.

1. Hybrid Streaming Analytics
for Apache Kafka Users

2. Cody Irwin
Solution Manager, Smart
Analytics
Google Cloud
Josh Treichel
Sr. Manager, Partner
Solutions
Confluent
Jeff Ferguson
Global Google Cloud
Alliance Lead
Confluent

3. Agenda:
1) Reference Architecture (5 min - Jeff Ferguson, Confluent)
2) Stream Analytics on GCP (10 min - Cody Irwin, Google Cloud)
3) Bridging your Kafka Deployment to GCP (10 min - Josh Treichel, Confluent)
➢ Customer Architectures and Stories
➢ Demo, Quickstart Guide, & Free Trial

4. Apache Kafka is an Event Streaming Platform
Hadoop ...
Device
Logs ... App ...MicroserviceMainframes
GCP Data
Services Splunk ...
Data Stores Logs 3rd Party Apps Custom Apps / Microservices
Same Day
Transactioning
(Account Open)
Fees Charges
& Billing
Real-time
Customer 360
Machine
Learning
Models
Real-time Data
Transformation
Real-time Fraud
Detection
(1) App modernization
(2) Data modernization
(3) Bidirectional hybrid

5. On-premises or Other Cloud
Hybrid Kafka Reference Architecture
Dataflow
BigQuery
Cloud
Storage
Data
Studio
Cloud
Functions
AI
Platform
Bigtable
Confluent Replicator
KSQL
MySQL HDFS Teradata,
Netezza
Mainframe

6. Business is transforming
Businesses have to anticipate and
act on risks and opportunities
faster than ever before
The data and events needed for
analysis are increasing in velocity,
volume, and type
Companies that are able to quickly identify and capitalize on insights within this
changing landscape have a strategic advantage.

7. Why Enterprises
choose Google Cloud
for Streaming Analytics
Serverless Architecture
Robust ingestion services
Unified batch and stream processing
Comprehensive set of analysis tools
Flexibility for users

8. Serverless data analytics
From infrastructure to platform for insights
Performance tuning
Monitoring
Reliability
Deployment &
configuration
Utilization
improvements
The traditional data analytics platform
Analysis and insights
Resource provisioning
Handling growing scale
Analysis and
insights
The serverless data
analytics model

9. Right-time Action
Dashboard
Visualize and share anomalous events in
your data.
Alerts
Manage by exception through
condition-based notifications.
Actions
Automatically trigger workflows in other
systems using conditions.
1
2
3
Looker
Blocks

10. Comprehensive set of analysis tools
BigQuery
Cloud Data
Warehouse
Easy setup
Directly integrated with
streaming Dataflow and
Confluent Cloud
Real time
Fast insights and action
powered by BigQuery’s
Streaming API
Intelligent
Built-in ML for
out-of-the-box predictive
insights
Cloud AI
Platform
AI & ML Tools
Plug-and-play
Easily experiment and
collaborate with Google’s
AI Hub
Building blocks
Tools for sight, language,
conversation, and
structured data
Fast deployment
Code-based AI platform
quickly moves ML ideas
to deployment
Tensorflow
Extended (TFX)

11. Improve the customer experience with Real-time AI
TFX uses Dataflow and Apache Beam as the distributed data processing engine to enable several
aspects of the ML life cycle, all supported with CI/CD for ML through Kubeflow pipelines.
Predictive
Analytics
Fraud
Detection
Real-time
Personalization
More!

12. AI for every level of expertise
APIs
Pre-trained Models
Infrastructure
AI Foundation
Building
Blocks
Platform
AI Platform
Development
Environment
AutoML
Custom
Models
Services & Solutions
Ease of Implementation
Structured DataSight Language Conversation
ServicesSolutions Collaboration
Structured DataSight Language
Integrated withBuilt-in Tools On-prem
FrameworksAccelerators
Document
Understanding AI
Talent
Solution
Contact
Center AI ASL
Professional
Services
Cloud AI
PartnersAI Hub
Video
Intelligence
Vision
Natural
Language
Translation Inference
Recommendations
AI
Speech-
to-Text
Text-to-
Speech
Dialogflow
Enterprise
Vision
Natural
Language Translation TablesVideo
Datasets Training DataprocDataflow Dataprep
Data
StudioBigQueryKubeflowPredictions
Data
Labeling
New
Pre-built
Algorithms
New
Notebook
New
VM
Images
New
GPUTPU CPU

13. Flexibility for users
Apache Beam
Open-source,
unified model and
set of SDKs for
defining and
executing data
processing
Open source programming
model
Serves as the SDK for
creating Cloud Dataflow jobs;
community development
increases flexibility
Choose your language
Java, Python, Scala, and GO are available;
join DA Spotlight
for news on languages
Portability
Program in Beam, and gain the ability to
move between
Spark, Flink, Dataflow, and more
Dataflow
Simplified stream and
batch data processing
Batch and Stream
Reduce complexity and reuse code by
driving batch and stream workloads
from the same tool
Reliable and consistent processing
Exactly once processing with built-in
support for fault-tolerant execution
Simplified operations & management
Performance, scaling, availability,
security, and compliance
handled automatically
Integrated
Integration with Kafka/Confluent Cloud,
the Google Data Analytics suite,
and GCP broadly
Unified stream and batch
processing

14. Ingest Transform Analyze
Ingest and distribute
data reliably
Fast, correct computations
quickly and simply
Machine learning &
data warehouse
Cloud Dataflow
Cloud MLPub/Sub BigQueryDataflow
Flexible stream analytics with OSS
KSQL

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The Business Case
Architectural Approach Business Solutions
FSI | Fraud Analytics, Trade Data Capture1
Retail I Recommendations, Inventory
Management, POS Processing2
Manufacturing | Anomaly Detection,
Edge-to-Cloud ML3
General | Real-time Clickstream. CDC4
Many more to come!∞

16. On-premises or Other Cloud
Kafka as the Real-Time Bridge Simplifies Cloud Migration
Dataflow
BigQuery
Cloud
Storage
Data
Studio
Cloud
Functions
AI
Platform
Bigtable
Confluent Replicator
KSQL
MySQL HDFS Teradata,
Netezza
Mainframe

17. Confluent Replicator Architecture
Kafka Broker
test-topic
Kafka Broker
test-topic
Replicator
consumer producer
eventsevents
Origin Destination
Make clusters globally available
Replicate clusters or a subset of
topics across any distance
Aggregate or migrate clusters anywhere
Aggregate many clusters together or
migrate entire clusters to a preferred
environment
Bridge self-managed clusters to
a fully managed Kafka service
Enable hybrid-cloud deployments
with Confluent Cloud

18. Schema Registry
Make data backwards
compatible and future-proof
KSQL
Develop real-time stream
processing apps writing only
SQL
Connectors
Easily send data to cloud
storage with BigQuery, GCS +
more
Confluent Cloud - Fully Managed Kafka and Much more!
! Schema
Registry
Kafk
a
topic
!
Serial
izer
Serial
izer

19. Kafka enables Unity’s massive GCP migration
Unity Monetization Platform &
Gaming Dev Platform
Confluent
Connector
Dataflow
Cloud
Storage
Confluent Replicator
BigQuery
Other Cloud

20. Unlock advanced AI/ML on GCP using data on prem
On-premise or Other
Cloud
Web
IoT
Mobile
Data
Store
Dataflow BigQueryEvents
Confluent Replicator
KSQL
Train Fraud Models
Deploy
Models
Tenso low Fraud
ApplicationCurated Data
Streams
Full Data
Stream
Fraud
ApplicationFraud
ApplicationsFraud
Applications
Fraud App Consumption and Production
Mainframe, Hadoop,
Oracle

21. On-premises
FinServ Fraud Analytics - On-prem to GCP
Confluent
Connector
Cloud
Dataproc
Cloud
Dataflow
BigQuery
Cloud
Storage
Cloud
Bigtable
Cloud Machine
Learning Engine
Confluent Replicator

22. Give it a try
https://docs.confluent.io/current/tutorials
/examples/kubernetes/replicator-gke-cc/
docs/index.html
Launch it form the GCP console.
Confluent Cloud $200/month for free for
3 months

23. Thank You!

24. Title Safe >
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On-premises or Other
Cloud
What does a hybrid Kafka architecture look like on GCP?
Web
IoT
Mobile
Data
Store
Dataflow
BigQuery
Cloud
Storage
1
Trigger &
Send
2
Ingest &
Prepare
3
Transform &
Enrich
4
Store &
Analyze
5
Share &
Activate
Data
Studio
Cloud
Functions
AI
Platform
Bigtable
Events
Confluent Replicator

25. Unlock the value of
event streaming

26. Title Safe >
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Unity leveraged Confluent Hybrid Kafka platform to build a massive data
infrastructure and migrated from AWS to GCP. This infrastructure is powering
Unity Gaming Dev Platform and Monetization Network, scaling to process
Million events per second with zero outages.Gaming & Media
Solution
Confluent was chosen for better
control, enterprise scale, Kafka
innovation, and guidance on Kafka
architecture and best practices.
Challenge
Bring together, unify, and modernize
all the different data pipelines and
technology stacks running in each
department of the company as well
as migrate from AWS to GCP.
“As a small team we have large responsibilities
that include managing the data infrastructure
that underpins the Unity platform and helping
make Unity a data-driven company. That’s
one of the reasons we built our data
infrastructure on Confluent Platform and
Apache Kafka. Today, this infrastructure
handles on average about a half million events
per second, with peaks of about a million
events per second. It also reliably handles
millions of dollars of monetary transactions.
In fact, since we went live with Confluent
Platform and Kafka a year ago we have had
zero outages that resulted in money loss.”
Oguz Kayral, Engineering Manager, Data
Platform, Unity
Results
● Completed a massive migration with
PetaBytes of data from AWS to GCP
● Scaled to handle Million events per
second and reliably handle Millions
of dollars of monetary transactions
with zero outages
● Well-proven data infrastructure
based on Confluent Platform & GCP
Dataflow, BigQuery analytics has
opened a lot of new possibilities for
product teams across Unity.
Unity-Confluent blog / Unity-Google blog

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Other Solution Opportunities
1) App modernization with Event-driven Microservices
(with Anthos option)
2) Data Lake & Data Warehouse modernization (Mainframe,
Oracle, Hadoop, Teradata, )
3) IoT (Manufacturing, Utilities, Smart Cars, etc)

28. Other Use Cases
Confluent Cloud provides other opportunities to help customers in unique
ways like...

29. Reference Architecture
Ingest Pipelines
Storage
Analytics
Application &
Presentation
App
Engine
Kubernetes
Engine
Cloud
Storage
Cloud
Dataflow
Cloud
Dataflow
Cloud
Datastore
Cloud
Bigtable
BigQuery
Cloud
Dataproc
Cloud
Datalab
Compute
Engine
colo / dc / on-premises / other cloud