Presentation at Hadoop Summit Europe, Brussels 16 April 2015

1. Destroying Data Silos
Hellmar Becker
Senior IT Specialist
Hadoop Summit 2015, Brussels

2. Who am I?
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3. 3
Datalake in ING NL
Integrate all data sources
within the bank into
one processing platform
• Batch data streams
• Live transactions
• Model building for customer interaction
Open source software where possible!

4. Zoom in: Datalake Archive
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Today, let’s focus on one specific part of the story:
• Collect data in a unified format
• Store these data secure from manipulation and
• unauthorized access
• Make data available to analytical applications
• Business Intelligence, Data Science
Hadoop based cluster is a good solution
to address these targets

5. Circa 2000: Data Warehouse
• Based on relational database technology (Oracle, DB2, …)
• Challenge 1: Data model is difficult to adapt after the fact
• Challenge 2: Resilience and fault tolerance are not built in
• Challenge 3: Scaling proves difficult and expensive (specialized hardware)
• Challenge 4: RDBMS brings a lot of overhead – e. g. referential integrity
Modern data platforms (Hadoop, Spark, Cassandra) address many of these issues
Old world vs. New World
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Operational
data
Staging
Files
ETLOperational
data
Data Mart
Data Mart
Data Mart
Metadata
Detail data
Aggregated
data
Reporting
Analytics
Predictive
Modeling

6. 6
Target: Data Lake Architecture

7. Pick your battles
• Toolset in the bank has grown around RDBMS and mainframe
• We cannot sweep out everything, have to handle legacy
• Plant a seed: Replace one component and connect it to all legacy interfaces
• Grow from there!
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Operational
data
Staging
Files
ETLOperational
data
Data Mart
Data Mart
Data Mart
Metadata
Detail data
Aggregated
data
Reporting
Analytics
Predictive
Modeling

8. Challenges
• Zero Touch Deployment
• Risk issues with deployment tools that require admin (root) access to servers
• Policies within the organization
• Example: The unit of consideration is a single server, but we need to look at entire
clusters
• Legacy protocols – Mainframe data formats, e. g. character sets
• Security is paramount – protect sensitive data
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9. Security Concept
Authentication Management
• Using Kerberos – proven technology, secure but hard to configure
• Need to align access with HR database – connect to corporate directory
Authorization Management
• Uniform views across all components of a cluster
• Using Ranger to secure all services with a uniform set of policies
Auditing
• Ranger logs all interactions in order to exterminate threats
Connecting the Pieces
• Sideline challenge: Linux world and Windows world need to be connected
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10. Security Concept
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11. Agile Working
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• Setup of this kind of project requires interdisciplinary
cooperation
• DevOps teams provide a lot of the required skills
with short communication paths
• Cooperation across department boundaries can be a
challenge
• Agile delivery vs. Expectations and timelines
• Manage external dependencies in a Scrum setting

12. Shaping the Future
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Existing standards do not always fit our goals and tools
Work with interdepartmental teams – DevOps, Infra,
DBAs, Business, Risk, Legal
We are influencing the standards that the bank will set
for coming systems!

13. Attributions
• Hellmar in Nîmes / With Python in Mindanao, by the author
• Domtoren in het oranje licht by helena_is_here is licensed under CC BY 2.0
• Data Pipeline, ING OIB Image Bank
• Data Pipeline, ING OIB Image Bank, edited (cropped) by the author
• Baby Elephant with mother by David Rosen is licensed under CC BY 2.0
• Bruarfoss Waterfall in winter, Iceland by Diana Robinson is licensed under CC BY-
ND 2.0
• Elephants at Pinnawala by Jan Arendtsz is licensed under CC BY-NC 2.0
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