Companies are finding accessing data from a variety of sources can be labor-intensive and costly. Oftentimes these companies are looking to cloud solutions, but are then finding the traditional architecture brittle when trying to move data to the cloud, which can drain organizations of time and resources. Join this webinar to hear several company success stories, the data-to-cloud issues they were encountering, and the steps these companies took to bring their cloud architecture to a successful, real-time analytic solution unlocking massive amounts of fresh enterprise-wide on a continuous basis. In addition, you will learn how to: • Modernize the ETL process to one that’s fast, flexible, and scalable • Supply users with up-to-date, accurate, trusted data • Increase your time to value with data in the cloud • Best practices on how to minimize resource overhead

1. REAL-TIME DATA TO
GOOGLE CLOUD
Top 3 Best Practices
April 29th 2021
Adam Mayer - Qlik
Michael Harding - Google

2. 2
About your speakers
Michael Harding
Partner Manager, SAP Strategy & Architecture, Google Cloud, Google
Mike leads the Tech Partnerships within the Google Cloud SAP Partnership team,
responsible for enabling a portfolio of partner software solutions to enhance the
SAP customer's experience on Google Cloud. With over 20 years of experience in
the SAP market, many of them as a customer, Michael has a passion for
modernizing the SAP customer's experience and driving agility and insights,
enabling a digitized IT ecosystem through the benefits of cloud services and
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.

3. 3
•Data Challenges in moving to Cloud
•Best Practices
•Google Cloud
•Customer Success Stories
•Wrap Up

4. 4
The Data Challenge
Most organizations struggle
to make actionable data available,
let alone turn it into business value.
10% 32%
of business
relevant data
is used for analysis
of executives say
they can create
value from data
Sources: IDC Worldwide Global DataSphere Forecast 2019-2023 (2019), Accenture Closing The Data-Value Gap (2019), Qlik How to Drive Data Literacy Within the Enterprise (2018)
24%
of business
decision makers
feel data literate

5. 5
The Data Pipeline Challenge
Manual scripting
Time consuming
Labor intensive
Slow, Lack of scale, Frustrated Users!
High Coding
environment
Zero Coding
environment
Automation
Scalability
Faster time to value
Timely, Accurate, Trusted Data

6. 6
IaaS
PaaS
Micro
Services
DB
MF
EDW
FILES
Best Practice 1: Develop in the cloud
CLOUD APPLICATION
DEVELOPMENT
CLOUD APPLICATION DEVELOPMENT
Legacy application modernization
Faster, easier new application
development
Higher scalability/elasticity
Infrastructure and maintenance cost
savings
Requires real-time data from
on-premise systems

7. 7
DWaaS
DB
MF
EDW
FILES
IaaS
PaaS
SaaS
Best Practice 2: Modernize your data warehouse
DATA WAREHOUSE MODERNIZATION
DATA WAREHOUSE MODERNIZATION
Reduce the costs associated with
legacy EDW’s and provide elasticity
Meet new business requirements
Support more advanced analytics
Data Warehouse Automation replaces
traditional ETL with modern self-service
capabilities
Requires real-time data from on-premise
systems and cloud platforms

8. 8
DB
MF
EDW
FILES
Data Lake
DATA
CONSUMPTION &
ANALYTICS
Best Practice 3: Power Next Generation Analytics
NEXT GENERATION CLOUD DLaaS
NEXT GENERATION ANALYTICS & DATA
MONETIZATION
Analyze a broader set of data structures
along with structured data
Needs organization and intuitive search
Leverage AI/ML, IoT and decision
automation for a competitive advantage
Requires Managed Data Lake Creation
and Big Data processing at scale
Requires real-time data from on-premise
systems and cloud platforms

9. 9
IaaS
PaaS
Micro
Services
DB
MF
EDW
FILES
CLOUD APPLICATION
DEVELOPMENT
DWaaS
DB
MF
EDW
FILES
IaaS
PaaS
SaaS
DATA WAREHOUSE MODERNIZATION
DB
MF
EDW
FILES
Data Lake
DATA
CONSUMPTION &
ANALYTICS
NEXT GENERATION CLOUD DLaaS
Best Practices Driving Optimal Digital Transformation

10. 10
IaaS
PaaS
Micro
Services
DB
MF
EDW
FILES
CLOUD APPLICATION
DEVELOPMENT
DWaaS
DB
MF
EDW
FILES
IaaS
PaaS
SaaS
DATA WAREHOUSE MODERNIZATION
DB
MF
EDW
FILES
Data Lake
DATA
CONSUMPTION &
ANALYTICS
NEXT GENERATION CLOUD DLaaS
RDBMS Files
Mainframe
SAAS APPS
SAP
Data Warehouse Data Lake ODS
Qlik Data Integration
CDC
Streaming
Data Lake
Creation
Data Warehouse
Automation
Prepare
Qlik Catalog
Qlik Data Analytics
Conversational
Analytics
Mobile
Analytics
Guided
Analytics
Self-Service
Analytics
Reporting
& Alerting
Embedded
Analytics
Shop Publish
Best Practices Driving Optimal Digital Transformation
Other clouds
Qlik Cloud Services
On-premise / Private cloud
Google Cloud

11. 11
Qlik and Google for better cloud modernization
1. DL/DW Automation
Snowflake
OTHER DWaaS
BigQuery
DWaaS
Kafka
STREAMING
DataProc
HADOOP
GCS
STORAGE
All databases
DBaaS
 Oracle
 MS SQL Server
 DB2 iSeries
 DB2 LUW
 MySQL (+ MariaDB, Percona)
 PostgreSQL
 SAP Sybase ASE
 IBM Informix
 SAP HANA
 ODBC w/ CDC
 DB2 z/OS
 IMS/DB
 VSAM
 HP Nonstop (SQL/MP AIS,
Enscribe AIS)
 ECC (DB: Oracle, SQL Server,
DB2 z/OS, DB2 LUW, HANA)
 S/4HANA
 ERP
 CRM
 SRM
 GTS
 MDG
 SAP HANA
 SAP Extractors
Database Mainframe SAP
 Exadata
 Teradata
 Netezza
 Vertica
 Pivotal  Amazon RDS
(SQL Server, Oracle,
MySQL, MariaDB, PostgreSQL)
 Amazon Aurora (MySQL,
PostgreSQL)
 Azure SQL Server MI
 Azure Database (MySQL,
PostgreSQL)
 Google Cloud SQL (MySQL,
PostgreSQL*)
 Oracle on Oracle Cloud
 Salesforce
EDW
Cloud
SaaS
 Delimited
(e.g., CSV, TSV)
Flat Files
 MongoDB
NoSQL
SOURCES

12. 12
Business
Core Data
SAP HANA
Trends Data
Social Data
Ads Data
Geospatial
Competitive
Edge Data
POS Data
HR Data
Fin Data
Ops Data
SAP
BigQuery
Qlik Replicate
Qlik Compose
Qlik Sense
How Organizations move data today

13. 13
Michael Harding
Partner Manager, SAP Strategy &
Architecture, Google Cloud, Google

14. Proprietary + Confidential
Google is not just search ...
+1B users across 9 consumer products

15. Proprietary + Confidential
01
Understand the Customer Journey
02
Predict Marketing Outcomes
03
Personalize the customer experience
We help you create value from your data to get a 360 degree view of
customers...
● Trendspotting
● Self Service Analytics
● Customer segmentation
● Lifetime Value Prediction
● Purchase Prediction
● Sentiment Monitoring
● Data-Driven Segmentation
● Personalization Engine

16. Real-time insights over
streaming and batch data
BigQuery
An enterprise data
warehouse
Scale up to petabytes on-
demand
Encrypted, durable,
and highly available
Fully managed and serverless
Built-in machine learning for
predictive analytics
In-memory BI Engine for
blazing fast reporting

17. Our Focus - Innovation for SAP customers
Modernize &
Run SAP
Virtual machine
agility for SAP
Innovate with AI/ML
SAP
Modern API
Management for SAP
Secure, planet scale
network for SAP
Leading uptime with
infra. live migration
for SAP
Run SAP on GCP Google + Partner Solutions
Industry/LoB
Solutions
Deliver
New Insights
SAP
BigQuery
External/Google Data
Smart Analytics
Looker

18. 18
Who we help
Customer Challenges, Solutions and Results.
Success Stories

19. 19
Jaguar Land Rover
Increase Agility with Data Ingestion as a Service
Challenges
Manual, error-prone and time-
consuming process to provide data
Increasing demand for up-to-the-
minute data
Difficult to unlock data from mission
critical systems
(mainframe, SAP and legacy sources)

20. 20
Jaguar Land Rover
Increase Agility with Data Ingestion as a Service
Solution
Accelerate real-time data integration
pipelines from all required sources to
Google Cloud for analytics projects and
strategic decision making
Results
• Faster rollout to production
• Streaming data architecture
• Increased agility

21. 21
“Qlik's Data Integration Platform has enabled JLR
to adopt a DataOps approach, vastly accelerating
delivery of real time data into our Cloud data
platform, liberating data from a large swathe of
enterprise sources to enable analytics at a scale
and scope not previously possible in JLR.”
Michael Cockbill, Product Manager, Cloud Datawarehouse at
Jaguar Land Rover

22. 22
Gordon Food Service
Delivers fresh data to Google Cloud
Challenges
Replace slow custom replication scripts
Speed up analytics
Understand their state of data

23. 23
Gordon Food Service
Solution
Selected Qlik Data Integration as the ‘right
tool for their toolbox’ delivering data into
Google Cloud Platform for analytics
Results
• Sales team ‘addicted’ to solution
• Reliably driving analytics program
• Moving thousands of objects per day
• A single admin for data pipelines
– “we would not be able do this with any other product out
there”

24. 24
“Qlik Products are powerful, we built a
solid pipeline into Kafka with Qlik
Replicate and it works great,
It's awesome!”
Tom Majeski, Data Services Manager, Information Technology at
Gordon Food Service

25. 25
Breuninger
Delivering SAP data to the Cloud at scale enabling high-end
business insight
Challenges
Fragmented data landscape – SAP and
other sources
Many solutions & KPIs split across the
business
Hard to discover the inventory of data in
company

26. 26
Breuninger
Solution
• Google Cloud & Big Query at heart of
strategy
• Data Delivery is Instantaneous
• Setup of Qlik Replicate took just hours, not
days!
• Data is formatted and analytics ready
Results
• Enables a ‘Fire & forget’ solution
• Cost Savings on average 6 digits annual sum
• Increase of 10x on survey responses
– “Receiving data points as close to the transaction as possible
creates much higher value – it’s a game changer”

27. 27
“Qlik Data Integration allows us to create robust
and reliable data pipelines that need least amount
of maintenance. Our Data ops teams were blown
away by lack of impact to their production
systems –
Qlik is our preferred method of data delivery now -
it just works!”
- Matthias Krenzel, Head of Data Platform Services
at Breuninger GmbH & Co

28. 28
Scale Data & Analytic Workloads on Google Cloud
RDBMS Files
Mainframe SAAS
APPS
SAP
Data Warehouse Data Lake ODS
Qlik Data Integration
Quickly GET Data to GCP
Database Replication /
CDC
(Change Data Capture)
Data Warehouse /Lake
Automation
Easily USE Data on GCP
Data Analytics
Google
Big Query
All databases
Google
Cloud Storage
Google
DataProc
Kafka
Google Cloud AutoML
Google Looker

29. 29
Summary of Best Practices & where to go from here
- Develop in the cloud
- Modernize your data warehouse
- Power next gen analytics
- Do your research!
- Gartner Magic Quadrant for Data Integration
- Trials / Test drives / Workshops
- Sources and Targets
- Use cases, do a SWOT analysis

30. 30
FREE 'SAP JUMPSTART’ Proof of Value
go.qlik.com/SAP-Jumpstart-Qlik-Google
SAP JumpStart on Google BigQuery
Accelerate the management and delivery of
SAP Data for analytics on Google BigQuery

31. qlik.com/dataintegration
go.qlik.com/SAP-Jumpstart-Qlik-Google