The document discusses the operation of the Smart Data Innovation Lab (SDIL) platform at KIT. It describes that the SDIL is funded by the German government to strengthen cooperation between industry and science through data innovation communities. The SDIL platform provides resources like IBM and SAP systems as well as software for data processing, analytics and more. It discusses challenges in operating the heterogeneous infrastructure and ensuring security, privacy and support for users.

1. KIT – Universität des Landes Baden-Württem ber g und
nationales Forschungszentrum in der Helmholtz-Gemeinschaft
Steinbuch Centre for Computing
www.kit.edu
Betrieb des Smart Data Innovation Labs
Vorstellung der Plattform
Nico Schlitter, 13. Oktober 2016

2. 2 13.10.2016 Betrieb des Smart Data InnovationLabs(SDIL) SteinbuchCentrefor ComputingSteinbuchCentrefor Computing
Partners

3. 3 13.10.2016 Betrieb des Smart Data InnovationLabs(SDIL) SteinbuchCentrefor ComputingSteinbuchCentrefor Computing
SDI-X funded by BMBF 09/2015 – 08/2018
to strengthen the cooperation between industry and science
self organizing data innovation communities
industry research partner meet at community meetings and define projects
SDIL strategy board approves project proposals within two weeks
project members get access to the SDIL platform hosted by KIT
Smart Data Innovation Lab
MedicineSmart Cities
Energy
Industry 4.0

4. 4 13.10.2016 Betrieb des Smart Data InnovationLabs(SDIL) SteinbuchCentrefor ComputingSteinbuchCentrefor Computing
Running the SDIL platform at KIT
Developmentof tools, best practices und operating concepts
Developing a legal framework
Research in the field of
data processing infrastructure
data integration
data analytics
data curation
data anonymization and pseudonymization
Tasks within SDIL

5. 5 13.10.2016 Betrieb des Smart Data InnovationLabs(SDIL) SteinbuchCentrefor ComputingSteinbuchCentrefor Computing
funded by the state of Baden-Württemberg 10/2014 – 09/2017
educating SME in regard to data analytics (~30 contacts to far)
free of charge
consulting for local SME in the field of data analytics
evaluating the potential of existing data
performing data analysis
recommending additionaldata collection
investigating further analysis steps
Smart Data Solution Center BW
• First contact
• Checking requirements
• Defining Objectives
• Legal Issues
• Data hand over
• Data analysis
• Evaluation
• Presenting results
• Recommendations
• Next steps
Preparation Realization Finalization

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SDIL Platform Overview

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#Cores RAM
[GB]
Disk Space
[TB]
Network Software
IBM Watson
Foundation
Power 8
7 x 20
= 140
4096 300 40Gbit/s
Ethernet
IBM Open Platform with
Hadoop/Spark
SPSS Modeler
SPSS Analytic Server
DB2 with BLU Acceleration
SAP HANA 4 x 80
= 320
4096 80 10Gbit/s
Ethernet
SAP HANA
PredictiveAnalysis Library
Business Function Library
Software AG
Terracotta
( * on request * ) BigMemory Max
HTCondor 32 x 4
= 128
1024 1Gbit/s
Ethernet
RapidMiner, Python, R, Matlab,
Virtualization 3 x 12
= 36
576 6 10Gbit/s
Ethernet
Red Hat Enterprise
Virtualization
on GlusterFS
SDIL Platform Resources

8. 8 13.10.2016 Betrieb des Smart Data InnovationLabs(SDIL) SteinbuchCentrefor ComputingSteinbuchCentrefor Computing
security is important due to storing of sensitive data
strict access control of the server room
multiple state-full firewalls are in place
access via dedicated login server
fine granular permission managementfor all SDIL components
unified storage solution enables users to use several different
technologies w/o the need to migrate large volumes of data
home and project directories with snapshots
encrypted backup on tape for disaster recovery
ticket system (xGUS) for user support
SDIL Operations – user perspective

9. 9 13.10.2016 Betrieb des Smart Data InnovationLabs(SDIL) SteinbuchCentrefor ComputingSteinbuchCentrefor Computing
legal issues are much bigger than expected
heterogeneous infrastructure
different architectures and operating systems
specialsoftware stacks like HANA and Terracotta
not everything is nicely packaged
data privacy protection
SDIL Platform Challenges

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3 networks:BMC, deployment/configuration,services
Two firewalls, strict access control, encryption
IBM Spectrum Scale as storage solution
Foreman and Puppet for deploymentand configuration of SDIL machines
(continuous integration)
GitLab as code repository
Icinga for monitoring (integrated with puppet)
RedhatEnterprise Virtualization (RHEV) on glusterFS
Ticket system for all operation tasks
Weekly shifts for incidentresponse
SDIL Operations – nerd perspective

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IBM Watson Foundations on Power 8

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SAP HANA & RHEV

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Continuously upgrading …