This document contains information about Pooja Rallabhandi, including that she is a software engineer at Ticketmaster in Arizona who works on web and streaming applications and is passionate about women in technology. It also provides an overview of Apache Kafka and Confluent Kafka, including how Ticketmaster uses Confluent Kafka. Key concepts discussed include distributed event streaming, producers, consumers, topics, partitions, Kafka streams, and Kafka Connect.

1. Pooja Rallabhandi
04-29-2019

2. Pooja Rallabhandi
 Software Engineer @ Ticketmaster, Arizona
 Work on Web and Stream based applications
 Passionate about Women in Technology
 LinkedIn : www.linkedin.com/in/poojaralla
 Twitter: @pzralla With Confluent CTO, Neha Narkhede @
Kafka Summit 2018

4. DATA
STORE
CLIENT A
Create Concert
CLIENT B
Sell Inventory
CLIENT C
Create
Inventory
A
P
I
G
A
T
E
W
A
Y
Concert
APIs
INVENTORY
APIs

5. DATA
STORE
CLIENT A
Create Concert
CLIENT B
Sell Inventory
CLIENT C
Create
Inventory
A
P
I
G
A
T
E
W
A
Y
Concert
APIs
INVENTORY
APIs

6. DATA
STORE
CLIENT A
Create Concert
CLIENT B
Sell Inventory
CLIENT C
Create
Inventory
A
P
I
G
A
T
E
W
A
Y
Concert
APIs
INVENTORY
APIs

7. DATA
STORE
CLIENT A
Create Concert
CLIENT B
Sell Inventory
CLIENT C
Create
Inventory
A
P
I
G
A
T
E
W
A
Y
Concert
APIs
INVENTORY
APIs

8. Poor performance Increase in Dependencies
Data Not Available
Data
Server
Copyright © 2015 -2018 Confluent, Inc. All rights reserved

9.  A data log that keeps track of changes as they happen
 Maintains the order in which events occurred
 Data is persisted in the log
 Clean-up is configurable
 An approach to reactive programming
 Process data as it is being generated
 Helps in real-time decision making
Copyright © 2015 -2018 Confluent, Inc. All rights reserved

10.  Distributed Event Streaming Platform
 Uses commit log
Features
 Scalability
 Data persistence
 High throughput
 Low latency

11.  Created @ LinkedIn in 2010
 Open source, top-level Apache project since 2012
 Founders of Kafka started Confluent in 2014
 Enterprise platform solutions
 Ticketmaster uses Confluent Kafka

12.  Key/Value message format
 Producer
 Consumer
 Topics
 Partitions

14. CENTRALIZE
D DATA
STORE
Copyright © LinkedIn. All rights reserved

15. CENTRALIZE
D DATA
STORE
Copyright © LinkedIn. All rights reserved

16. CENTRALIZE
D DATA
STORE
Copyright © LinkedIn. All rights reserved

17. CENTRALIZE
D DATA
STORE
Copyright © LinkedIn. All rights reserved

18. TICKETING
HOST
CLIENT A
Create Concert
CLIENT B
Sell Inventory
CLIENT C
Create
Inventory
A
P
I
G
A
T
E
W
A
Y
Concert
APIs
INVENTORY
APIs

19. Copyright © Ticketmaster. All rights reserved

20. Copyright © Ticketmaster. All rights reserved

21. Copyright © Ticketmaster. All rights reserved

22. Copyright © Ticketmaster. All rights reserved

23. Copyright © Ticketmaster. All rights reserved

24. BEFORE NOW

26. PRODUCER
APPLICATION
DATA
TRANSFORMATIO
N
map(), filter()
CONSUMER
APPLICATION

27. PRODUCER
APPLICATION
CONSUMER
APPLICATIONDATA
SOURCE
DATA
SINK
DATA
COPY

28.  Kafka streams the commit log in real-time
 Uses pull based model
 Provides scalability & high throughput
 Available Kafka platforms
Kstreams
Kafka Connect

30.  Download Apache Kafka:
https://kafka.apache.org/downloads
 QuickStart guide:
https://kafka.apache.org/quickstart
 Tutorials:
https://docs.confluent.io/current/tutorials/inde
x.html
 Confluent published Use Cases
https://www.confluent.io/resources/

31. LinkedIn : www.linkedin.com/in/poojaralla
Twitter: @pzralla