Converting production databases into live data streams for Apache Kafka can be labor intensive and costly. As Kafka architectures grow, complexity also rises as data teams begin to configure clusters for redundancy, partitions for performance, as well as for consumer groups for correlated analytics processing. In this breakout session, you’ll hear data streaming success stories from Generali and Skechers that leverage Qlik Data Integration and Confluent. You’ll discover how Qlik’s data integration platform lets organizations automatically produce real-time transaction streams into Kafka, Confluent Platform, or Confluent Cloud, deliver faster business insights from data, enable streaming analytics, as well as streaming ingestion for modern analytics. Learn how these customer use Qlik and Confluent to: - Turn databases into live data feeds - Simplify and automate the real-time data streaming process - Accelerate data delivery to enable real-time analytics Learn how Skechers and Generali breathe new life into data in the cloud, stay ahead of changing demands, while lowering over-reliance on resources, production time and costs.

1. Data Streaming
Success Stories with
Apache Kafka
How Qlik and Confluent keep your data
fresh
June 2021
Adam Mayer - Qlik
Rankesh Kumar - Confluent

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About your speakers
Rankesh Kumar
Partner Solutions Engineer, Confluent
Rankesh helps grow and scale the Confluent technology ecosystem by working
with partners to integrate different technologies with Kafka and Confluent.
Rankesh has over 15 years of experience in data engineering, building streaming
applications, data integration, metadata management, application integrations
and partner management. He built a bulk payment gateway at one of the largest
global financial institutions, integrating multiple payment and audit systems.
Previously, he was a product specialist with market iPaaS leader, where he helped
the presales team and leading partners with data engineering solutions.
Adam Mayer
Senior Technical Product Marketing Manager, Qlik
Adam is responsible for CDC Streaming product marketing, in addition to
delivering Qlik’s Internet of Things (IoT) and GDPR go-to-market strategy. He has a
strong technical background in computing spanning over 20 years and is an avid
follower of new technology especially IoT, particularly on the data streaming and
analytics side.

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• Physics applies to data
• Set Your Data in Motion
• Customer Success Stories
• Wrap Up
Agenda

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Physics 101
As It Applies to Data

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An object will not change its motion unless
acted on by an unbalanced force.
• If it is at rest, it will stay at rest
• If it is in motion, it will remain at the same
velocity
Newton’s First Law of
Motion
Inertia
Corollary: Data in motion tends to stay in
motion until it comes to rest on disk.
Similarly, if data is at rest, it will remain at
rest until an external “force” puts it in
motion again.

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Get Landed Data Moving
Overcoming Storage “Friction”
File I/O (reads)
• Parsing challenges
• No deltas
Database Queries
• Not real-time
• Added database load
Database Triggers
• Added database load
• Doesn’t scale
ETL Tools
• Not real-time
• Added database load
• Getting deltas is hard
Qlik CDC Streaming
• Real-time
• Reads the DB logs
• CDC provides delta processing

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Deliver Any Source To Kafka
• One solution for all sources
• Easy to use GUI
• Log-based change data capture
• Supports on-premise and cloud
• Transform data in flight
• Filter at both table and column level
• Proven solution that supports
production loads
Amazon RDS
Azure SQL
Managed Instance Amazon Aurora
Amazon RDS
Google Cloud SQL
Amazon Aurora
Amazon RDS
Google Cloud SQL
Amazon RDS
Amazon RDS
DB2 for zOS
DB2 for iSeries
DB2 LUW

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Stream
Processing
Set Data in Motion
Kafka
Connect
CONNECTORS
APP
APP
DB
Qlik Replicate
Confluent
Cloud
(Managed
Kafka)
Confluent
Machine
Learning
Self-Managed
Kafka
Schema
Registry
Confluent
REST Proxy
Confluent
Control Center
Kafka
Connect
ksqlDB
KStreams
Stream from any source to sinks
Oracle
Mainframe
SAP
. . .
Qlik Sense

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BI and
Visualization
Apps
Cloud
Storage
Database
Applications
MAINFRAME
Qlik Replicate
TARGET SCHEMA
CREATION
BATCH TO CDC
TRANSITION
HETEROGENEOUS
DATA TYPE MAPPING
FILTERING
DDL CHANGE
PROPAGATION
TRANSFORMATIONS
IN-MEMORY
Confluent Cloud
(Managed Kafka)
Confluent
ksqlDB
Machine
Learning
Self-Managed
Kafka
Schema
Registry
Confluent
REST Proxy
Confluent
Control Center
Kafka
Connect
Full Load
Log-Based
CDC
Qlik and Confluent
Automated Real-Time Data Delivery

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Customer
Success Stories
Keeping Data in Motion with Qlik &
Confluent

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Conrad Electronic
YOUR SOURCING PLATFORM
Challenges
• Many data silos
• Limited business intelligence
• Slow data delivery
• Inaccurate data
10 million
shipments p.a.
6 million
product offers
210 million
online shop visits
p.a.

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Conrad Electronic
YOUR SOURCING PLATFORM
Solution
• Migrate SAP data into Apache Kafka
• APIs for other sources
• Consolidate in Google Big Query
Results
• Real-time data delivery
• Improved customer experience
• Faster analysis (days not months)
• Huge time savings = more automation

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"The connection of Qlik Replicate to
SAP systems and Confluent Kafka
works without any problems and is
very stable, which enables us to
realise our Real Time Use Cases".
Martin Iffländer, Head of Big Data Platform at Conrad

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Generali Switzerland
Global Insurance Leader
Challenges
• Traditional processes disrupting operations
• Siloed, Inconsistent data
• Lack of integration
1800
people
56
locations
1 million
customers

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Generali Switzerland
Building a Game-Changing Platform with Event Streaming
Solution
• Build and deploy a new IT architecture
based on event streaming with Qlik &
Confluent Platform
Results
• Better integration
• Faster deployment
• Faster processes, higher efficiency
• Reduced data replication from days to
just seconds

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Generali Architecture
Connection Platform (CoPa)
CA API GATEWAY
SERVICE LAYER
SalesForce
Customer
Portal
…
CI / CD
OpenShift
Landing
Zone
Integration
Model
Shipping
Model
KAFKA / CONFLUENT
Redhat
Openshift
Core
Systems
IoT …
CDC Streaming
(Qlik Replicate)
Data processing with dockerized
Kafka Streams Microservices in
OpenShift

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We can replicate and stream data in
just a few seconds. This could have
taken days before. It’s of significant
value to our business
Christian Nicholl
– Director of Platform Engineering & Operations

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Skechers USA
American lifestyle and performance footwear company
Challenges
• On premise legacy systems
• Time consuming batch cycles
• Long time to value in development
cycles
170
Countries
$5
Billion Sales
163
Million pairs

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Skechers USA
Modern Cloud Architecture for changing business requirements
Solution
• Rethink existing platform to have very
short development cycles
• Hydrate data lake in near real time
Results
• Immediate visibility to the business
• Better cost management
• Easier to manage scalable
infrastructure

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Skechers Data Interaction Framework
includes several SaaS/PaaS tools.
• Qlik Replicate
• Confluent Cloud Kafka
• Databricks Delta
• Talend Engines
• Snowflake
Data Interaction Framework
Elevated Visibility

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Ingestion
Informix, MariaDB & SQL Servers are
primary data sources. Data is Ingested
from respective Transaction/Binary logs
to Confluent Cloud Kafka.
Data Pipelines
Spark Streaming jobs extract events
from Kafka topics and persist data in
Databricks Delta tables. Incremental
datasets are identified, validated,
normalized and loaded to Snowflake
staging using Talend.
Skechers CDC
Qlik Replicate CDC Data Flow
Qlik Replicate
Real-time data ingestion

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“A very, very important
thing which you have to
realize is that Qlik makes all
that very easy.”
- Siva Veera, Data Engineering Manager at Skechers USA

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Wrapping Up

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Key Takeaways
Qlik & Confluent
sets
data in motion
Qlik Data Integration
platform delivers real-time
data
to
Confluent
“Modern” architectures want
data to be in motion
Confluent
is key for this
Physics applies to
data

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Summarizing Key Points
Physics applies to data
Qlik Data Integration
platform delivers real-time
data
to
Confluent
“Modern” architectures want
data to be in motion
Confluent
is key for this
Qlik & Confluent
sets
data in motion

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See Qlik and Confluent for Kafka in Action
https://go.qlik.com/2021-Q1-Tech-Partner-Demo-Confluent.html

27. www.qlik.com/confluent
go.qlik.com/2021-Q1-Tech-Partner-Demo-Confluent