"In this session we will uncover the concepts of
Kafka, the API that Kafka offers, followed by a basic introduction to Kafka
streams and then its use cases."
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3. Our Agenda
Introduction to Apache Kafka
A Brief Introduction to Apache Kafka
01
Kafka Core Concepts
The Components and Basic terminologies used in Kafka
Core and their relations.
02
Connect Core Concepts
What it is, how to use it and uses cases.
03
Kafka Streams, A Real Time Processing
What actually real time processing is and how Kafka
Streams works.
04
Kafka streams Core Concepts
What Kafka Streams is and it’s degrees.
05
Demo
A Short demo on how to implement Kafka Stream.
06
5. Data, Data, and Data everywhere !!!
A Real Life Scenario !
Messaging Log
Aggregation
Metrics
Stream
Processing
Website
Activity
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What is Apache
Kafka
● Distributed Streaming Platform.
● Client library for building applications
and microservices and unbounded
data.
● Interact with the clusters to process a
stream of data.
● Data is represented in it is as key-value
records
7. How does it Look Like
Creating a Stream Kafka Server Processing Stream
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Kafka
Connect
● A System which can be placed in
between the data source/ Sink and
cluster.
● Just need to configure it.
● Source Connector and Sink Connectors
● Component of Kafka for connecting and
moving data between Kafka and
systems.
13. A View to Kafka Connect
Kafka
Connector
Kafka
Connector
Source Connector
Sink Connector
15. Stream Processing in Kafka
Kafka Streams is a library
for building streaming
applications
Streams API
Applications that retrieve
data from Kafka servers
inside which Kafka
producers publish real-time
messages.
Consumer API
Offers an interactive SQL
like interface for
streaming processing.
KSQL
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Kafka
Streams
● Kafka Streams is a library for building
streaming applications.
● Input Data must be streamed into Kakfa
Topic.
● Parallel Processing, Scalability and Fault
tolerance etc.
● No Cluster needed i.e. Deploy anywhere.
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Degree of
Kafka Streams
● Interoperable with streams and tables.
● Fault-tolerant, scalable and efficient
local states.
● Unit Testing Tool.
● Deploy in Containers and manage using
Kubernetes.
● Grouping and Continuously updating
aggregates.
● Flexible windowing capability.