The document discusses building real-time serverless data applications with Confluent and AWS. It provides an overview of real-time data pipelines using event streaming and stream processing. It then discusses using Confluent Cloud for a managed Apache Kafka service and ksqlDB for stream processing. Finally, it reviews options for serverless stream processing using AWS Lambda with Confluent connectors and when to use ksqlDB, Kafka Streams, Kinesis Data Analytics, or Lambda.

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SPONSORED BY CONFLUENT
Building real-time serverless data
applications with Confluent and AWS
Ahmed Zamzam(he/his)
PAT036– AWS Summit London
Senior AWS Partner Solutions Architect
Confluent

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Why real-time and Serverless
Event streaming with Confluent
Serverless Stream processing with Confluent and AWS
Best practices
Agenda

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Faster time
to market
Automatic
Scaling
Lower total cost
of ownership
Eliminate operational
overhead
Built-in high
availability & security
Serverless accelerates innovation
Why Serverless?

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Why real-time?
Real-time Seconds Minutes Hours Days Months
Value
of
data
to
decision-making
Preventive/Predictive
Actionable Reactive Historical
Source: Perishable insights, Mike Gualtieri, Forrester
Data loses value quickly over time
Time critical
decisions
Traditional “batch” business
intelligence
Information half-life
in decision-making

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Typical real-time data pipeline
Data continuously
generated at a high
velocity from different
sources like IoT devices,
Application logs, Online
transactions, etc..
Source
Data captured and
stored in the order it
was received for set
duration of time and
can be replayed
indefinitely.
Event Streaming
Process, analyse and
action on the data as
soon as it is generated
and, in the order, it was
received
Stream Processing
Sink data different
destinations. Dara Lakes
(most common) and/or
different Databases
Presentation
Governance

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Typical real-time data pipeline
Data continuously
generated at a high
velocity from different
sources like IoT devices,
Application logs, Online
transactions, etc..
Source
Data captured and
stored in the order it
was received for set
duration of time and
can be replayed
indefinitely.
Event Streaming
Process, analyse and
action on the data as
soon as it is generated
and, in the order, it was
received
Stream Processing
Sink data different
destinations. Dara Lakes
(most common) and/or
different Databases
Presentation
Governance

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The Confluent Product Advantage
Everywhere
Be everywhere our
customers
want to be
Cloud-Native
Re-imagined
Kafka experience
for the Cloud
Complete
Enable developers
to reliably &
securely build next-
gen apps faster

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Leave Kafka reliability worries behind with
99.99% uptime SLA and 10x built-in durability
Never worry about Kafka storage limits again
with Infinite Storage that’s 10x more scalable
and performant
Scale and shrink to handle 0 to GBps+
workloads and peak customer demands
10x faster and easier
10x Kafka
Confluent Cloud offers a truly
fully managed, cloud-native
data streaming platform for
Apache Kafka, with 10x faster
scaling, infinitely more storage,
and built-in resilience
Resiliency
Storage
Elasticity

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Together Confluent and AWS empower Endless Use Cases across many
Industries
Retail
Healthcare
Finance &
Banking
Transportation
Common in all
Industries
Inventory
Management
Personalized
Promotions
Product
Development
& Introduction
Sentiment
Analysis
Streaming
Enterprise
Messaging
Systems of
Scale for High
Traffic Periods
Connected
Health
Records
Data
Confidentiality
& Accessibility
Dynamic Staff
Allocation
Optimization
Integrated
Treatment
Proactive
Patient Care
Real-Time
Monitoring
Early-On
Fraud
Detection
Capital
Management
Market Risk
Recognition &
Investigation
Preventive
Regulatory
Scanning
Real-Time
What-If
Analysis
Trade Flow
Monitoring
Advanced
Navigation
Environmental
Factor
Processing
Fleet
Management
Predictive
Maintenance
Threat
Detection &
Real-Time
Response
Traffic
Distribution
Optimization
Data Pipelines
Hybrid Cloud
Integration
Microservices
Security and
Fraud
Customer 360 Streaming ETL

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Data continuously
generated at a high
velocity from different
sources like IoT devices,
Application logs, Online
transactions, etc..
Source
Data captured and
stored in the order it
was received for set
duration of time and
can be replayed
indefinitely.
Event Streaming
Process, analyse and
action on the data as
soon as it is generated
and, in the order, it was
received
Stream Processing
Sink data different
destinations. Dara Lakes
(most common) and/or
different Databases
Presentation
Governance
Typical real-time data pipeline

11. ksqlDB at a glance
What is it?
ksqlDB is an event-streaming
database for working with
streams and tables of data
All the key features of a
modern streaming solution
Aggregations Joins
Windowing
Event-time
Dual query
support
Exactly-once
semantics
Out-of-order
handling
User-defined
functions
CREATE TABLE activePromotions AS
SELECT rideId,
qualifyPromotion(distanceToDst) AS
promotion
FROM locations
GROUP BY rideId
EMIT CHANGES
How does it work?
It separates compute from storage, and scales
elastically in a fault-tolerant manner
It remains highly available during disruption, even in
the face of failure to a quorum of its servers

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2. Stateless Stream processing with AWS Lambda
Event
source
mapping
Lambda service
Confluent Kafka sink
connector
• Sink connector polls Kafka partitions and
invokes your function
• Lambda can be invoked synchronously or
asynchronously
• At least once semantics
• Provides a dead letter queue (DLQ) for any
failed invocations
• Sink connector scales up to a soft maximum
of 10 connectors
• Lambda service polls the Kafka partitions and invokes
your Lambda function synchronously
• Starts with one concurrent poller and customer
function
• Scaling
○ Lambda service checks every 3 minutes if
scaling is needed
○ Starts with 1 poller and scales up to ≤
#partitions
• Batch records based on a BatchSize or Batchwindow

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Optimize batch-size/batch-window to
lower cost
Lambda
Function
instance
Poller
Lambda’s maximum execution time is 15 minutes
• Adjust the batch size (max 10,000) to ensure
execution time is optimal
• For sparse topics, consider batch window to
aggregate over a time period

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Capture and log exceptions
data
producer
Lambda
service
function A
(instance 1)
batch size =
200
300 records
✔
function A
(instance 1)
✔
Catch exceptions and log
to CloudWatch Logs
CloudWatch
Logs
Return successfully from
Lambda function
• Ensure processing moves forward by catching exceptions and returning successfully
!

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Enrich Transaction events for Fraud scoring
Customer
Transactio
n
Jay $10
ksqlDB

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Customer Transaction Avg 7 days Num trans 10m
Jay $10 $8.5 1
Enrich Transaction events for Fraud scoring

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Customer Transaction Avg 7 days Num trans 10m
Jay $10 $8.5 1
Amazon
SageMaker
AWS
Lambda
ksqlDB
Enrich Transaction events for Fraud scoring

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Which one to use?

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Which one to use?
CHOOSE THE RIGHT STREAM PROCESSING OPTION DEPENDING ON YOUR NEEDS
ksqlDB Kafka Streams
Kinesis Data
Analytics
Lambda
Fully Managed ✔ — ✔ ✔
TYPE
Stateful and
Stateless
Stateful and
Stateless
Stateful and
Stateless
Stateless
FAULT
TOLERANCE
Exactly once Exactly once Exactly once At-least once
UDF SUPPORT
✔
(self-managed)
✔
(self-managed)
✔ ✔
LATENCY FAST VERY FAST VERY FAST FAST

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Thank you!
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Ahmed Zamzam
linkedin.com/in/ahmed-saef-zamzam/