Ready to take the plunge into a cloud data lake? Don’t dive in just yet. Most of the data filing your cloud data lake is raw, unstructured, and rife with data quality problems. If you’re going to get maximum value from your cloud data lake investment, data preparation is critical. We have created a checklist to guide you through six steps you should take to improve agility, productivity, and consistency when preparing data for a cloud data lake: - Choose a data preparation solution that natively integrates with your cloud platform - Use data preparation to increase efficiency and coordination among stakeholders - Don’t rebuild what works; choose the right use cases - Empower users with self-service while maintaining data governance - Ensure data quality with at-scale and continuous validation - Publish and operationalize preparation for downstream analytics and machine learning Join this tech demo and learn how to use Data Preparation to accelerate Cloud Data Lake adoption.

1. Jasper Staal - ABN AMRO
Lead Product Owner Data Marketplace
Victor Coustenoble - Trifacta
Director of Sales Engineering EMEA
vcoustenoble@trifacta.com

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4. Why Data Prep
and Trifacta

5. ABN AMRO
Turning data into value
Jasper Staal
Lead Product Owner Data Marketplace

6. ABN AMRO KEY FIGURES
INTRODUCTION
China Japan USA
Singapore Hong Kong Brazil
UAE Australia
Global Presence
EUR 9.3 bln annual operating
income
19,954 fte
Retail, private and corporate
banking clients

7. ABN AMRO JOURNEY
INTRODUCTION
Supporting our clients’
transition to sustainability
Building a future-proof bank
Re-inventing the client
experience
BANKING FOR BETTER
FOR GENERATIONS TO COME

8. MOVING BEYOND EXPERIMENTATION
DATA ANALYTICS JOURNEY
1
2012-2016: Big Data
Lab
leads the
transformation
2018: Improved central
enablement
• 3 Data grids with 36 scrum
teams
• Leverage cloud (all-in)
• Democratise the data
• Improve the governance
• Provide coaching & expertise
2017: Shift towards ‘business line
led’
• 25+ data analytics teams

9. 9
9
DIAL Architecture
Central Data Functions
(Big) Data Quality Data Lineage Data Governance Catalogue Security
Data ConsumersData Providers Digital Integration & Access Layer (DIAL)
ETLRAW Data Store
Streaming
Request
Response
ESB &
Apigee
Request
Response

10. 10
10
Our vision
for a single portal experience
self
service
secure
easy &
simple
anyhow
compliant
users can execute everything
themselves, start their journey
from one portal without help
users are able to access what
they are allowed to, throughout
the distribution chain, providers
are in control
our users get a seamless and intuitive
experience, which is self explanatory
so data is easily provided and used
we follow the latest
ABN AMRO guidelines and
standards, users get the (data)
quality they might expect
users can access the data in
the supported platforms
and tools
Data Marketplace
“Provide securely, use quickly”

11. 11
11
Data Marketplace
architecture reference
Golden source
systems
Data
Owner
Raw Data
Store
Data Consumption
& Security Engine
Integrated data
store
Virtual
Resources
Data
User
Data Provider Data ConsumerDigital Integration & Access Layer
Business
Intelligence /
ETL Tools
Self-Service Portal
Metadata & Security Store
Access Layer
Infrastructure
Provisioning Engine
Validation Engine
Business & Technical
metadata
Security
Policies
Data Sharing
Agreements
List of Golden Sources

12. 12
12
Azure Data Platform
Data Science Exploration Environment (DSEE)
Databricks SQL
Database
Virtual
Machines
Other
components
Data Science Production Environment (DSPE)
Databricks SQL
Database
Virtual
Machines
Other
components
Web
Apps
Bounded Context Specific Environment (BCSE)
Virtual
Machines
Other
components
Custom
Containers
Custom
Databases
Custom
Storage
Data Platform (Production)
DIAL
Non-DIAL
sources
SaaS
External Data
RDS RDS
Raw
Storage
Raw
Storage
Integrated Data
Store
SQL
Database
Integration
Tools
Databricks
Data Flow
Raw Data
Other Tools
Generic Data
Capabilities
Azure
Analysis
Service
Power BI
Other
components
Integrated Data Store
Integrated Data Store
Integrated Data Store
Bounded Context

13. Data Prep for
Cloud Data Lakes
Architecture &
Tech Demos

14. 14
DATA PLATFORMS
ANALYSIS & CONSUMPTION
DATA PREPARATION
Discover Structure Clean Enrich Validate Publish

16. ADLS Databricks
➔
➔
➔

17. AWS cloud
Amazon EC2
Amazon S3
Amazon S3
Amazon
EMR
Amazon
Redshift
Amazon
Redshift
Amazon
Glue
Access via
IAM Role
ML/AI
Reporting and
Analytics
Apps &
Services
Use
Cases
Data
Onboarding
Amazon
QuickSight
Amazon
SageMaker
Amazon AI

18. ML/AI
Reporting and
Analytics
Data
Onboarding
Cloud
Data Fusion
Big
Query
Cloud
Storage
Cloud
Dataprep
Cloud
Dataflow
Cloud ML
Engine
Data Studio
Cloud
AutoML
Cloud IAM
Big
Query
Cloud
Storage
Cloud Dataprep
by TRIFACTA

19. Microsoft Azure
Azure
HDInsight
Azure
Storage
Blob
Azure Data
Lake
Azure Data
Warehouse
Azure
Storage
Blob
Azure Data
Lake
Azure Data
Warehouse
Azure
Data
Factory
PowerBI
AzureML
ML/AI
Reporting and
Analytics
Microsoft Active
Directory
SQL
Azure SQL
Database
SQL
Azure SQL
Database
Apps &
Services
Use
Cases
Data
Onboarding

20. Tech Demos
Data Prep in Azure +
API deploy & run

21. SQL