See how the Elastic Stack enables Postbank Systems to drive transformation from monitoring bank branches and ATMs and creating an anti-fraud system, to ensuring that the Postbank Group is GDPR compliant. Watch the video: https://www.elastic.co/elasticon/tour/2019/munich/powering-postbank-groups-data-driven-strategy

1. Seite 1Munich, 15th of October, Lucas Meyer
BigData and DevOps:
Powering Postbank’s Data-Driven
Strategy
Lucas Meyer
Oct 15 2019, Application Manager - Big Data | DevOps, Postbank Systems

2. Seite 2Munich, 15th of October, Lucas Meyer
„We are Postbank Systems!“
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Postbank Systems is a full-service IT provider that centrally bundles the IT
expertise of the Postbank. We offer stable, secure and high-quality IT
systems and services and see ourselves as the backbone of modern
banking.
The goal: to offer our customers reliable IT solutions that are convincing overall.
As part of the Postbank Group, we cover the entire value chain in application
development and operation with around 1,500 colleagues. We master the full
range of technological services
Unique full-service IT provider for the Postbank

3. Seite 3Munich, 15th of October, Lucas Meyer
Who I am?
Lucas Meyer
• 30 years
• happily married
• 2 children aged 1 and 2 ¾
My main hobbys:
• making music with my band
• OpenSource Smart Home Technologies
Main career steps so far:
▪ With the company Postbank Systems AG since 2008
▪ Operation as system integrator with the focus on provision monitoring solutions
▪ Degree in Business Informatics; Thesis in the Context of BigData Architectures
▪ Internal change ~ 4 years ago from IT – Ops to IT – Project.
Start with the strategic mission to build up BigData skill.
▪ Meanwhile: Lead of the "Elastic Squad".
One of the first, 7 member strong DevOps team’s
▪ Active as "DevOps Evangelist" in various transformation initiatives

4. Seite 4Munich, 15th of October, Lucas Meyer
2015 2016 2017 2018 2019
Getting started What about a Goal? Technology is one aspect... IT4IT
A small team emerges
and launches the first Use
Case
A Data Lake architecture
is adopted and more Use
Cases are added
Collaboration, organization,
and the General Data
Protection Regulation
(GDPR)
Container technology and
the issue with the logs - a
central logging service
takes its away
Summary & Lessons
Learned
Transformation needs
commitment

5. Seite 5Munich, 15th of October, Lucas Meyer
2015 – Let‘s give it a try: first use cases, small team
Challenges
Challenges with new technologies
Skill
IT – Operation
Acceptance
Governance

6. Seite 6Munich, 15th of October, Lucas Meyer
Sales data categorization & cash flow analysis
2015 – Let‘s give it a try: first use cases, small team
Use Case: Smart Banking Analytic Workplace
The idea of a digital
household diary was born
Analysis Tools
Revenues and
purpose of use information

7. Seite 7Munich, 15th of October, Lucas Meyer
Core NetworkDMZ
KBS DB
MergeCoreData
Apache FarmLoadbalancer InfoneaLogstash ElasticSearch
2015 – Let‘s give it a try: first use cases, small team
Use Case: Monitoring Self Service Terminals
Classical monitoring
ATM Cash Box status

8. Seite 8Munich, 15th of October, Lucas Meyer
2015 2016 2017 2018 2019
Getting started What about a Goal? Technology is one aspect... IT4IT
A small team emerges
and launches the first Use
Case
A Data Lake architecture
is adopted and more Use
Cases are added
Collaboration, organization,
and the General Data
Protection Regulation
(GDPR)
Container technology and
the issue with the logs - a
central logging service
takes its away
Summary & Lessons
Learned
Transformation needs
commitment

9. Seite 9Munich, 15th of October, Lucas Meyer
100101001001010010010010
010001010110100101010010
101110010100101001100110
Data Lake
Digital Account Book
& Credit – Worthiness
What is a Data Lake?
Data pool for storing raw data for analysis
and evaluation purposes.
2016 – A Data Lake is born
…on which we could build data driven products

10. Seite 10Munich, 15th of October, Lucas Meyer
2016 – Business Process Monitoring
Apache Farm Loadbalancer Logstash Elasticsearch Kibana
Availability and performance
Monitoring of the branch client
Dashboards for analytical purposes

11. Seite 11Munich, 15th of October, Lucas Meyer
2015 2016 2017 2018 2019
Getting started What about a Goal? Technology is one aspect... IT4IT
A small team emerges
and launches the first Use
Case
A Data Lake architecture
is adopted and more Use
Cases are added
Collaboration, organization,
and the General Data
Protection Regulation
(GDPR)
Container technology and
the issue with the logs - a
central logging service
takes its away
Summary & Lessons
Learned
Transformation needs
commitment

12. Seite 12Munich, 15th of October, Lucas Meyer
2017 – Challenges
The team grows and the different perspectives collide
DevelopmentBusiness Operations
We have nearly no
experience with these
technologies, we cannot
guarantee the business a
SLA?
Big Data technology does not fit into
our existing data center design.
We can't just ignore valid backup
policies just because the data is
supposedly replicated in the cluster.
Before the first line of code is
written, extensive concepts are
written, discussed, reviewed,
changed and reviewed again,
again, again …. that's stress.

13. Seite 13Munich, 15th of October, Lucas Meyer
2017 – Breaking the Status Quo
Previous project organization is supplemented by platform parts
Elastic Squad Team
We have been developing agilely with
Scrum for some time now. DevOps was
now a real step forward.
I receive immediate feedback as to
whether functional and non-functional
requirements have been met.
As guardians of stability, we have
always seen ourselves as the antithesis
of agile software developers.
Meanwhile I know that this is not true.
I need to become an enabler and not
as a barrier. People like me are needed
more than ever, because: I make sure
that infrastructure is provided as a
service.
What the team shows me at
the end of the sprint are not
only features that work on
their screen but they are also
built in such a way that we
can be productive with them
at any time.

14. Seite 14Munich, 15th of October, Lucas Meyer
2015 2016 2017 2018 2019
Getting started What about a Goal? Technology is one aspect... IT4IT
A small team emerges
and launches the first Use
Case
A Data Lake architecture
is adopted and more Use
Cases are added
Collaboration, organization,
and the General Data
Protection Regulation
(GDPR)
Container technology and
the issue with the logs - a
central logging service
takes its away
Summary & Lessons
Learned
Transformation needs
commitment

15. Seite 15Munich, 15th of October, Lucas Meyer
2018 – DSGVO / GDPR
Use Case – Read Access Logging
Elastic Stack for compliant reasons:
Read access log with 2-year retention period
Data Protection Officer access only
Data Lake export functionality of the RALs
Data Lake interface acknowledges after
complete receipt of the data to SAP

16. Seite 16Munich, 15th of October, Lucas Meyer
Alert:
Alerting in case of anomalies in
the data
2018 – Central Logging Service
Collect & Ship Transform & Enrich Store & Analyze Visualize
Archive & Backup:
Archiving of raw data for
subsequent transformation,
reprocessing in other systems
and/or recovery.
Secure:
Role- and attribute-based access
restriction to the data

17. Seite 17Munich, 15th of October, Lucas Meyer
2015 2016 2017 2018 2019
Getting started What about a Goal? Technology is one aspect... IT4IT
A small team emerges
and launches the first Use
Case
A Data Lake architecture
is adopted and more Use
Cases are added
Collaboration, organization,
and the General Data
Protection Regulation
(GDPR)
Container technology and
the issue with the logs - a
central logging service
takes its away
Summary & Lessons
Learned
Transformation needs
commitment

18. Seite 18Munich, 15th of October, Lucas Meyer
Reengineering a Cluster that doesn‘t fit the requirements
Today - Anti Fraud Management
Endpoint Malware
Detecion
Filebeat
EMD
Fraud Detection
System
Filebeat
FDS
Key Components:
Fraud Detection System
Endpoint Malware Detection System
Fraud Management System for Analytics

19. Seite 19Munich, 15th of October, Lucas Meyer
why elastic
Conclusion
Instances: 20
Pipelines: 20
Logstash
Instances: 50
DataVolume: 70 TB
• 00_Admin: 600 GB
• 01_ICFEM: 150 GB
• 02_SBD: 2,5 TB
• 03_FMSA:65 TB
• 04_RAL: 1,7 TB
• 05_CLS: 500 GB
Elasticsearch
Instances: 20
User: 180
Kibana
Instances: 150
• Metricbeat: 60
• Filebeat: 60
• Packetbeat: 30
• Heartbeat: 10
Beats Kafka

20. Seite 20Munich, 15th of October, Lucas Meyer
„Start anywhere and improve from there“
(Douglas Adams)
THANK YOU