The document discusses Apache Kafka as a leading open-source event streaming platform designed for real-time data processing and ETL/data integration. It highlights Kafka's features such as scalability, durability, and persistence, and its use in handling massive message volumes for companies like LinkedIn and Netflix. Additionally, the document introduces KSQL as a streaming SQL engine for Kafka, facilitating the creation and management of streams for applications such as ATM fraud detection.

1. 1
Apache Kafka an Open Source
Event Streaming Platform
Erfassung, Analyse und Auswertung von Datenströmen in Echtzeit

2. 22
Introduction
Event Streaming

3. 3
ETL/Data Integration Messaging
Batch
Expensive
Time Consuming
Difficult to Scale
No Persistence After
Consumption
No Replay
Highly Scalable
Durable
Persistent
Ordered
Real-time

4. 44
Highly Scalable
Persistent
ETL/Data Integration MessagingETL/Data Integration MessagingMessaging
Batch
Expensive
Time Consuming
Difficult to Scale
No Persistence After
Consumption
No Replay
Real-timeHighly Scalable
Durable
Persistent
Ordered
Real-time
Event Streaming

5. 55
Highly Scalable
Durable
Persistent
Maintains Order
ETL/Data Integration MessagingETL/Data Integration MessagingMessaging
Batch
Expensive
Time Consuming
Difficult to Scale
No Persistence After
Consumption
No Replay
Fast (Low Latency)Highly Scalable
Durable
Persistent
Ordered
Real-time
Event Streaming
What happened
in the world
(stored records)
What is happening
in the world
(transient messages)
What is contextually happening in the world (data
as a continually updating stream of events)

6. 66
Event-Driven App
(Location Tracking)
Only Real-Time Events
Messaging Queues and
Event Streaming
Platforms can do this
Contextual
Event-Driven App
(ETA)
Real-Time combined
with stored data
Only Event Streaming
Platforms can do this
Where is my driver? When will my driver
get here?
Where is my driver? When will my driver
get here?
2
min
Why Combine Real-time
With Historical Context?

7. 77
Event Streaming Paradigm
Highly Scalable
Durable
Persistent
Maintains Order
Fast (Low Latency)
Event Streaming

8. 88
STREAM
PROCESSING
Create and store
materialized views
Filter
Analyze in-flight

9. 9C O N F I D E N T I A L
Apache Kafka, the de-facto OSS standard for
event streaming
Real-time | Uses disk structure for constant performance at Petabyte scale
Scalable | Distributed, scales quickly and easily without downtime
Persistent | Persists messages on disks, enables intra-cluster replication
Reliable | Replicates data, auto balances consumers upon failure
In production at more
than a third of the
Fortune 500
2 trillion messages a
day at LinkedIn
500 billion events a
day (1.3 PB) at Netflix

10. 10C O N F I D E N T I A L 10C O N F I D E N T I A L
About Confluent We Are The Kafka Experts
30% of Fortune 100
Confluent founders
created Kafka
Confluent team wrote
80% of Kafka
We have over 300,000
hours of Kafka Experience

11. 11C O N F I D E N T I A L
Kafka Integration Architecture
PRODUCERCONSUMER

12. 12C O N F I D E N T I A L
Kafka Cluster
Connect API Stream Processing Connect API
$ cat < in.txt | grep “ksql” | tr a-z A-Z > out.txt
Stream Processing Analogy

13. 13C O N F I D E N T I A L
KSQLis the
Streaming
SQL Enginefor
Apache Kafka

14. 14C O N F I D E N T I A L
CREATE STREAM ATM_POSSIBLE_FRAUD_ENRICHED AS
SELECT t.account_id,
a.first_name + ’ ’ + a.last_name cust_name,
t.atm, t.amount,
TIMESTAMPTOSTRING(t.ROWTIME,’HH:mm:ss’) tx_time
FROM atm_txns t
INNER JOIN accounts a
ON t.account_id = a.account_id;
Simple SQL syntax for expressing reasoning along and across data streams.
You can write user-defined functions in Java
Stream processing with KSQL

15. 15C O N F I D E N T I A L
KSQL in Development and Production
Interactive KSQL
for development and testing
Headless KSQL
for Production
Desired KSQL queries
have been identified
REST
“Hmm, let me try
out this idea...”

16. 16C O N F I D E N T I A L
ATM Fraud Dataflow: Streaming ETL with KSQL

17. 17C O N F I D E N T I A L
What does KSQL look like?
● First load a topic into a stream
CREATE STREAM ATM_TXNS_GESS (account_id VARCHAR,
atm VARCHAR,
location STRUCT<lon DOUBLE, lat DOUBLE>,
amount INT,
timestamp VARCHAR,
transaction_id VARCHAR)
WITH (KAFKA_TOPIC='atm_txns_gess', VALUE_FORMAT='JSON‘,
TIMESTAMP='timestamp‘,
TIMESTAMP_FORMAT='yyyy-MM-dd HH:mm:ss X‘);

18. 18C O N F I D E N T I A L
What does KSQL look like?
● Create a table on topic for reference data
● Join stream to table for enrichment
CREATE STREAM ATM_POSSIBLE_FRAUD_ENRICHED AS
SELECT T.ACCOUNT_ID AS ACCOUNT_ID, T.TX1_TIMESTAMP,
T.TX2_TIMESTAMP, T.TX1_AMOUNT, T.TX2_AMOUNT,
T.TX1_ATM, T.TX2_ATM, T.TX1_LOCATION, T.TX2_LOCATION,
T.TX1_TRANSACTION_ID, T.TX2_TRANSACTION_ID,
T.DISTANCE_BETWEEN_TXN_KM, T.MILLISECONDS_DIFFERENCE,
T.MINUTES_DIFFERENCE, T.KMH_REQUIRED,
A.FIRST_NAME + ' ‚ + A.LAST_NAME AS CUSTOMER_NAME,
A.EMAIL AS CUSTOMER_EMAIL, A.PHONE AS CUSTOMER_PHONE,
A.ADDRESS AS CUSTOMER_ADDRESS, A.COUNTRY AS CUSTOMER_COUNTRY
FROM ATM_POSSIBLE_FRAUD T
INNER JOIN ACCOUNTS A
ON T.ACCOUNT_ID = A.ACCOUNT_ID;
CREATE TABLE ACCOUNTS
WITH (KAFKA_TOPIC='ACCOUNTS',VALUE_FORMAT='AVRO',KEY='ACCOUNT_ID');

19. 1919
Demo!

20. 20C O N F I D E N T I A L
Or use the Kafka Streams API
● Java or Scala
● Can do multiple joins in one operation
● Provides an interactive query API which makes it possible to query the state
store.

21. ATM Fraud Detection with Apache Kafka and KSQL
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22. 22
Realtime Operations View & Analysis

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26. ATM Fraud Detection with Apache Kafka and KSQL
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