1. Kafka Connect and Kafka Streams
The Rise of Apache Kafka as Streaming Platform
Kai Waehner
Technology Evangelist
kontakt@kai-waehner.de
LinkedIn
@KaiWaehner
www.confluent.io
www.kai-waehner.de

2. 2https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63921
https://qconlondon.com/london2018/presentation/cloud-native-and-scalable-kafka-architecture
(2018) (2018)
Apache Kafka
A Distributed, Scalable Commit Log

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Apache Kafka
A Distributed, Scalable Commit Log

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Apache Kafka
A Distributed, Scalable Commit Log

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1.0 “Enterprise
Ready”
A Brief History of Apache Kafka and Confluent
0.11 Exactly-once
semantics
0.10 Data processing
(Streams API)
0.9 Data integration
(Connect API)
Intra-cluster
replication
0.8
2012 2014
Cluster mirroring0.7
2015 2016 20172013 2018
CP 4.1
KSQL GA

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Apache Kafka
The Rise of a Streaming Platform

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Orders Customers
Payments
Stock
Apache Kafka
Single Shared Source of Truth for (Micro)Services

8. 8
Independent Dev / Test / Prod

9. 9
No Matter Where it Runs

10. Kafka Connect
Declarative Data integration for Apache Kafka

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Apache Kafka as Central Nervous System

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Kafka Connect

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Standalone Mode

14. 14
Distributed Mode

15. 15
Scalable Consumption

16. 16
Certified Connectors

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(Distributed) Workers

18. 18
Converters

19. 19
Avro Converter

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Single Message Transforms

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Single Message Transforms
•Mask sensitive information
•Add identifiers
•Tag events
•Lineage/provenance
•Remove unnecessary
columns
•Route high priority events to
faster data stores
•Direct events to different
Elasticsearch indexes
•Cast data types to match
destination
•Remove unnecessary
columns
Modify events before storing in Kafka: Modify events going out of Kafka:

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• InsertField – Add a field using either static data or record metadata
• ReplaceField – Filter or rename fields
• MaskField – Replace field with valid null value for the type (0, empty string, etc)
• ValueToKey – Set the key to one of the value’s fields
• HoistField – Wrap the entire event as a single field inside a Struct or a Map
• ExtractField – Extract a specific field from Struct and Map and include only this field in results
• SetSchemaMetadata – modify the schema name or version
• TimestampRouter – Modify the topic of a record based on original topic and timestamp. Useful
when using a sink that needs to write to different tables or indexes based on timestamps
• RegexpRouter – modify the topic of a record based on original topic, replacement string and a
regular expression
• „Build your own“ – A Transformation is just a Java Class
Built-in Transformations

23. Kafka Streams
Stream Processing natively on top of Apache Kafka without an additional big data cluster

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Kafka Streams - Part of Apache Kafka

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Stream Processing
Data at Rest Data in Motion

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Key concepts

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Kafka Streams - Processor Topology
1) Read input from Kafka
2) Operator (directed acyclic graph):
• Filter / map / aggregation / joins
• Operators can be stateful
3) Write result back to Kafka

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Kafka Streams - Runtime

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Kafka Streams - Distributed State

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Kafka Streams - Scaling

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Kafka Streams - Streams and Tables

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Kafka Streams - Streams and Tables

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// Example: reading data from Kafka
KStream<byte[], String> textLines = builder.stream("textlines-topic", Consumed.with(
Serdes.ByteArray(), Serdes.String()));
// Example: transforming data
KStream<byte[], String> upperCasedLines= rawRatings.mapValues(String::toUpperCase));
KStream

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// Example: aggregating data
KTable<String, Long> wordCounts = textLines
.flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("W+")))
.groupBy((key, word) -> word)
.count();
KTable

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Kafka
Streams
A complete streaming microservices, ready for production at large-scale
App configuration
Define processing
(here: WordCount)
Start processing

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What if you are NOT a Java Coder?
Population
CodingSophistication
Realm of Stream Processing
New, Expanded Realm
BI
Analysts
Core
Developers
Data
Engineers
Core Developers
who don’t like
Java
Java
KSQL

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KSQLis the
Streaming
SQL Engine
for
Apache Kafka

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KSQL – The Streaming SQL Engine for Apache Kafka

40. Questions?
Kai Waehner
Technology Evangelist
kontakt@kai-waehner.de
LinkedIn
@KaiWaehner
www.confluent.io
www.kai-waehner.de