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WHEN SAP ALONE IS NOT ENOUGH
Wim Stoop | Senior Technical Marketing Manager, Cloudera
Michal Alexa | Service Line Manager, Datavard
2 © Cloudera, Inc. All rights reserved.
TODAY’S SPEAKERS
Wim Stoop
Senior TMM
wim@cloudera.com
Michal Alexa
Service Line Manager
michal.alexa@datavard.com
# 3
Why you need to bridge SAP and Hadoop to turn your
data into Business Value
# 4
SAP and Hadoop – bridging two worlds
Hadoop
 Java, Python, PigLatin
 Massive clusters for big data processing
 Structured & unstructured data
 Apache & open source
 Distributions (e.g. Cloudera)
 Engines (e.g. Spark, Impala)
 Fast paced evolution since 2006
 Big Data management
SAP
 ABAP
 Client/Server
 classic RDBMS as relational database
 Proprietary software
 Interfaces and open standards
 Business Software
 Steady evolution since 1972
 Data management
# 5
SAP and Hadoop – bridging two worlds
Hadoop
 Java, Python, PigLatin
 Massive clusters for big data processing
 Structured & unstructured data
 Apache & open source
 Distributions (e.g. Cloudera)
 Engines (e.g. Spark, Impala)
 Fast paced evolution since 2006
 Big Data management
SAP
 ABAP
 Client/Server
 classic RDBMS as relational database
 Proprietary software
 Interfaces and open standards
 Business Software
 Steady evolution since 1972
 Data management
75% of global GDP is generated by
companies running on SAP®
# 6
Data Management Issues
Scalability
Data-Pipelines
Granularity and Velocity
Data-Silos
Extensibility
• Not any more possible to do lifetime sizing of platform during procurement
• HW requirements create limitations to possible growth
• Scale UP comes often with great cost, and scale DOWN is usually
valueless
• Data transformations are I/O intensive operations
• Take lot of time, consume lot of resources
• Limitations on format of data
• Limitations on granularity of data, often only aggregated and cleaned
data are stored
• Raw data are necessary for data science activities
• Too many places for storing data
• No interconnection between company units limits data analyzing
possibilities
• Data analyses requires lot of programing languages
• Limited applications compatibility
# 7
From Data management to Big Data management
Data Management Issues
Data Growth
Data Separation
# 8
From Data management to Big Data management
Data Management Issues Business Questions to
answer
Data Growth
Data Separation
Cost Reduction
Revenue Increase
# 9
Cost Reduction
# 10
“Only 12-18% of all data in BW is
actually used.”
Forrester research
# 11
“Only 12-18% of all data in BW is
actually used.”
Forrester research
“In Average 35% of SAP data is
temporary and could be deleted”
Based on 300+ Fitness Tests
# 12
3%
5%
5%
5%
9%
11%
15%
15%
32%
Cube D data
Master data
Cube F data
Cube E data
PSA data
Changelog data
Other data
Temporary data
DSO data
0% 5% 10% 15% 20% 25% 30% 35%
Data distribution in SAP BW* * Based on 300+ DataVard BW FitnessTestTM
“Only 12-18% of
all data in BW
is actually
used.”
Forrester research
35 %
Housekeeping
“In Average
35% of SAP data
is temporary
and could be
deleted”
Based on 300+ Fitness Tests
# 13
DATA GROWTH WITH & WITHOUT DATATIERING
1290
1710
2250
2925
3803
4943
774 716 754
857
1041
1309
0
1000
2000
3000
4000
5000
6000
2017 2018 2019 2020 2021 2022
Data size without datatiering Data size after datatiering
SAP DATA GROWTH (in GB)
3.6 TB
saving
DATA GROWTH
25% p.a.
SIZE TODAY
1,3 TB
SIZE IN 5 YEARS
4,9 TB
DATATIERING ROI
2 YEARS
# 14
Revenue Increase
# 15
# 16
# 17
# 18
-10
-5
0
5
10
15
3/1/2018
3/8/2018
3/15/2018
3/22/2018
3/29/2018
Temperature in Bratislava March 2018
# 19
# 20
35 %
Housekeeping
# 21
35 %
Housekeeping
# 22
35 %
Housekeeping
# 23
How it fits together?
# 24
From Data management to Big Data management
Data Management Issues Business Questions to
answer
Data Growth
Data Separation
Cost Reduction
Revenue Increase
# 25
From Data management to Big Data management
Data Management Issues Big Data Management
Solutions
Business Questions to
answer
Data Growth
Data Separation
Cost Reduction
Revenue Increase
Data Tiering
Data Integration
# 26
2. Data Integration use case stream - GLUE
1. Data Tiering use case stream - OUTBOARD
From Data management to Big Data management
Data Growth
Data Separation
Cost Reduction
Revenue Increase
Data Tiering
Data Integration
# 27
From Data management to Big Data management
1. Data Tiering use case stream - OUTBOARD
Data Growth Cost Reduction Data Tiering
2. Data Integration use case stream - GLUE
Data Separation Revenue Increase Data Integration
3. Security Analyses use case stream – Data Science
Data Protection Cost Prevention Security Analyses
# 28
From Data management to Big Data management
1. Data Tiering use case stream - OUTBOARD
Data Growth Cost Reduction Data Tiering
2. Data Integration use case stream - GLUE
Data Separation Revenue Increase Data Integration
3. Security Analyses use case stream – Data Science
Data Protection Cost Prevention Security Analyses
3. Data Aging or decommission of old system – Data Fridge scenario
Data Aging GDPR/Costs Data Fridge
# 29
How?
30 © Cloudera, Inc. All rights reserved.
IDEAL DATA LAKE SETTING
31 © Cloudera, Inc. All rights reserved.
WHICH DO YOU WANT?
•
Data lake Data hub
32 © Cloudera, Inc. All rights reserved.
USE DATA TO MAKE THE IMPOSSIBLE POSSIBLE
CONNECT PRODUCTS &
SERVICES (IoT)
GROW BUSINESS PROTECT BUSINESS
33 © Cloudera, Inc. All rights reserved.
MODERN DATA
ARCHITECTURE ML / AI
(DATA SCIENCE)
ANALYTICS
CLOUD STORAGE ON-PREMISES STORAGE
MANAGEMENT & SECURITY
DATA
ENGINEERING
34 © Cloudera, Inc. All rights reserved.
CLOUDERA
ENTERPRISE DATA
PLATFORM
The modern platform for
machine learning & analytics
optimized for the cloud
WORKLOADS 3RD PARTY
SERVICES
DATA
ENGINEERIN
G
DATA
SCIENCE
ANALYTIC
DATABASE
OPERATIONA
L DATABASE
DATA CATALOG
GOVERNANCESECURITY LIFECYCLE
MANAGEMENT
STORAGE
Microsoft
ADLS
COMMON SERVICES
HDFS
Amazon
S3
CONTROL
PLANE
KUDU
35 © Cloudera, Inc. All rights reserved.
• Data Catalog: a comprehensive catalog of all data sets, spanning on-premises,
cloud object stores, structured, unstructured, and semi-structured. Includes
technical schemas from the Hive metastore, as well as business glossary
definitions, classifications, and usage guidance
• Security: role-based access control applied consistently across the platform
using Apache Sentry. Also includes full stack encryption and key management
• Governance: enterprise-grade auditing, lineage, and other governance
capabilities applied universally across the platform with rich extensibility for
partner integrations
• Lifecycle Management: comprehensive ingest-to-purge management of data
set lifecycle activities
• Control Plane: multi-environment cluster provisioning, deployment,
management, and troubleshooting
SHARED DATA CONTEXT SERVICES
Built for multi-function analytics anywhere
WORKLOADS 3RD PARTY
SERVICES
DATA
ENGINEERING
DATA
SCIENCE
ANALYTIC
DATABASE
OPERATIONAL
DATABASE
DATA CATALOG
GOVERNANCESECURITY LIFECYCLE
MANAGEMENT
STORAGE
Microsoft
ADLS
COMMON SERVICES
HDFS
Amazon
S3
CONTROL
PLANE
KUDU
36 © Cloudera, Inc. All rights reserved.
HYBRID IS THE NEW NORMAL IN ML & ANALYTICS
CLOUD
• Elastic
• Transient
• IoT
• Dev / Test
• New locations
ON-PREMESIS
• Data sovereignty
• Persistent
• Legacy
• Cost
• Performance
+
Choice | Economics | Migration | Governance | Control
37 © Cloudera, Inc. All rights reserved.
EXTENSIVE INTEGRATION WITH PUBLIC CLOUD VENDORS
DATA
ENGINEERING
DATA
SCIENCE
ANALYTIC
DATABASE
OPERATIONAL
DATABASE
CLOUDERA ENTERPRISE
Private Cloud
Infrastructure-as-a-Service
CLOUDERA ALTUS
DATA ENGINEERING DATA SCIENCEANALYTIC DB
Platform-as-a-Service
beta
beta soon
Bare Metal
38 © Cloudera, Inc. All rights reserved.
ENTERPRISE-PROVEN MACHINE LEARNING AND ANALYTICS
MACHINE LEARNING
Pattern recognition
Anomaly detection
Prediction
Customers
Run on Cloudera
ANALYTICS
Self-service intelligence
Real-time analytics
Secure reporting
Customers
Run IMPALA on Cloudera
39 © Cloudera, Inc. All rights reserved.
DATA-DRIVEN
JOURNEY
USE CASES
VISIBILITY
Preventive
& Proactive
Maintenance
IoT Hub for
Industry 4.0
Advanced
Threat
Detection
Risk
Modelling &
Analysis
Marketing
Systems
Integration
Customer
360
Insights
Exploratory
Data
Science
Data
Warehouse
Applied
Machine
Learning
GROW
Sales & Marketing
CONNECT
Operations & Product
PROTECT
Security & Compliance
MODERNIZE
IT, Tech, Data Science & Analytics
40 © Cloudera, Inc. All rights reserved.
DELIVERING BETTER
BROADBAND SERVICE
• Deeper network analysis to better predict
customer internet speeds and identify the
cause of performance issues
• Reduces truck rolls to save millions of
pounds
• Positions BT to take advantage of IoT for
predictive maintenance on fleet service
vehicles
• Increased data velocity by 15X (5X the
data in 1/3 of the time)
DRIVE CUSTOMER
INSIGHTS
VISIBILITY
PRODUCTIVITY
TRANSFORMATION
41 © Cloudera, Inc. All rights reserved.
CAPTURING AND GROWING
MARKET SHARE WITH 10X
MORE ACCURATE FORECASTS
• Saves consumers and businesses up
to 30% on electric bills
• Improves accuracy of predictions,
with error rate below 1%
• Enables creation of micro-targeted
campaigns in hours
CONNECT
PRODUCTS &
SERVICES
VISIBILITY
PRODUCTIVITY
TRANSFORMATION
42 © Cloudera, Inc. All rights reserved.
DELIVERING DEEP INSIGHTS
AND BEST PRACTICES IN BIG
DATA SECURITY & COMPLIANCE
• First PCI Certified Hadoop platform
• Optimizes EDW and improves fraud
detection and prevention
• Secures 10 PB in a PCI-compliant
manner every day
• Security Information Event
Management (SIEM) — monitor
access to sensitive datasets, full audit
trail of user behavior
PROTECT YOUR
BUSINESS
VISIBILITY
PRODUCTIVITY
TRANSFORMATION
43 © Cloudera, Inc. All rights reserved.
PARTNER
ECOSYSTEM
Focus on strategic
partnerships to expand
reach and accelerate
consumption
ISVs & SOLUTIONS
CLOUD & PLATFORM
SYSTEM
INTEGRATORSRESELLERS
# 44
Who is Datavard
 Focus on SAP and Data Management: Business Transformation, SAP ABAP, and Big Data
 Software products and consulting services
 More than 200 projects p.a.
 Customers of all industries, regions and sizes
 No “me too” topics
 Strong partnership with SAP since 1998
 Privately held since 1998, 2018: 245 employees
 Germany: Heidelberg (HQ), Hamburg | USA: Philadelphia, Washington DC
Switzerland: Regensdorf | Italy: Milan | Central Europe: Bratislava | Singapore
Explore Optimize Transform Innovate
THANK YOU

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When SAP alone is not enough

  • 1. WHEN SAP ALONE IS NOT ENOUGH Wim Stoop | Senior Technical Marketing Manager, Cloudera Michal Alexa | Service Line Manager, Datavard
  • 2. 2 © Cloudera, Inc. All rights reserved. TODAY’S SPEAKERS Wim Stoop Senior TMM wim@cloudera.com Michal Alexa Service Line Manager michal.alexa@datavard.com
  • 3. # 3 Why you need to bridge SAP and Hadoop to turn your data into Business Value
  • 4. # 4 SAP and Hadoop – bridging two worlds Hadoop  Java, Python, PigLatin  Massive clusters for big data processing  Structured & unstructured data  Apache & open source  Distributions (e.g. Cloudera)  Engines (e.g. Spark, Impala)  Fast paced evolution since 2006  Big Data management SAP  ABAP  Client/Server  classic RDBMS as relational database  Proprietary software  Interfaces and open standards  Business Software  Steady evolution since 1972  Data management
  • 5. # 5 SAP and Hadoop – bridging two worlds Hadoop  Java, Python, PigLatin  Massive clusters for big data processing  Structured & unstructured data  Apache & open source  Distributions (e.g. Cloudera)  Engines (e.g. Spark, Impala)  Fast paced evolution since 2006  Big Data management SAP  ABAP  Client/Server  classic RDBMS as relational database  Proprietary software  Interfaces and open standards  Business Software  Steady evolution since 1972  Data management 75% of global GDP is generated by companies running on SAP®
  • 6. # 6 Data Management Issues Scalability Data-Pipelines Granularity and Velocity Data-Silos Extensibility • Not any more possible to do lifetime sizing of platform during procurement • HW requirements create limitations to possible growth • Scale UP comes often with great cost, and scale DOWN is usually valueless • Data transformations are I/O intensive operations • Take lot of time, consume lot of resources • Limitations on format of data • Limitations on granularity of data, often only aggregated and cleaned data are stored • Raw data are necessary for data science activities • Too many places for storing data • No interconnection between company units limits data analyzing possibilities • Data analyses requires lot of programing languages • Limited applications compatibility
  • 7. # 7 From Data management to Big Data management Data Management Issues Data Growth Data Separation
  • 8. # 8 From Data management to Big Data management Data Management Issues Business Questions to answer Data Growth Data Separation Cost Reduction Revenue Increase
  • 10. # 10 “Only 12-18% of all data in BW is actually used.” Forrester research
  • 11. # 11 “Only 12-18% of all data in BW is actually used.” Forrester research “In Average 35% of SAP data is temporary and could be deleted” Based on 300+ Fitness Tests
  • 12. # 12 3% 5% 5% 5% 9% 11% 15% 15% 32% Cube D data Master data Cube F data Cube E data PSA data Changelog data Other data Temporary data DSO data 0% 5% 10% 15% 20% 25% 30% 35% Data distribution in SAP BW* * Based on 300+ DataVard BW FitnessTestTM “Only 12-18% of all data in BW is actually used.” Forrester research 35 % Housekeeping “In Average 35% of SAP data is temporary and could be deleted” Based on 300+ Fitness Tests
  • 13. # 13 DATA GROWTH WITH & WITHOUT DATATIERING 1290 1710 2250 2925 3803 4943 774 716 754 857 1041 1309 0 1000 2000 3000 4000 5000 6000 2017 2018 2019 2020 2021 2022 Data size without datatiering Data size after datatiering SAP DATA GROWTH (in GB) 3.6 TB saving DATA GROWTH 25% p.a. SIZE TODAY 1,3 TB SIZE IN 5 YEARS 4,9 TB DATATIERING ROI 2 YEARS
  • 15. # 15
  • 16. # 16
  • 17. # 17
  • 19. # 19
  • 23. # 23 How it fits together?
  • 24. # 24 From Data management to Big Data management Data Management Issues Business Questions to answer Data Growth Data Separation Cost Reduction Revenue Increase
  • 25. # 25 From Data management to Big Data management Data Management Issues Big Data Management Solutions Business Questions to answer Data Growth Data Separation Cost Reduction Revenue Increase Data Tiering Data Integration
  • 26. # 26 2. Data Integration use case stream - GLUE 1. Data Tiering use case stream - OUTBOARD From Data management to Big Data management Data Growth Data Separation Cost Reduction Revenue Increase Data Tiering Data Integration
  • 27. # 27 From Data management to Big Data management 1. Data Tiering use case stream - OUTBOARD Data Growth Cost Reduction Data Tiering 2. Data Integration use case stream - GLUE Data Separation Revenue Increase Data Integration 3. Security Analyses use case stream – Data Science Data Protection Cost Prevention Security Analyses
  • 28. # 28 From Data management to Big Data management 1. Data Tiering use case stream - OUTBOARD Data Growth Cost Reduction Data Tiering 2. Data Integration use case stream - GLUE Data Separation Revenue Increase Data Integration 3. Security Analyses use case stream – Data Science Data Protection Cost Prevention Security Analyses 3. Data Aging or decommission of old system – Data Fridge scenario Data Aging GDPR/Costs Data Fridge
  • 30. 30 © Cloudera, Inc. All rights reserved. IDEAL DATA LAKE SETTING
  • 31. 31 © Cloudera, Inc. All rights reserved. WHICH DO YOU WANT? • Data lake Data hub
  • 32. 32 © Cloudera, Inc. All rights reserved. USE DATA TO MAKE THE IMPOSSIBLE POSSIBLE CONNECT PRODUCTS & SERVICES (IoT) GROW BUSINESS PROTECT BUSINESS
  • 33. 33 © Cloudera, Inc. All rights reserved. MODERN DATA ARCHITECTURE ML / AI (DATA SCIENCE) ANALYTICS CLOUD STORAGE ON-PREMISES STORAGE MANAGEMENT & SECURITY DATA ENGINEERING
  • 34. 34 © Cloudera, Inc. All rights reserved. CLOUDERA ENTERPRISE DATA PLATFORM The modern platform for machine learning & analytics optimized for the cloud WORKLOADS 3RD PARTY SERVICES DATA ENGINEERIN G DATA SCIENCE ANALYTIC DATABASE OPERATIONA L DATABASE DATA CATALOG GOVERNANCESECURITY LIFECYCLE MANAGEMENT STORAGE Microsoft ADLS COMMON SERVICES HDFS Amazon S3 CONTROL PLANE KUDU
  • 35. 35 © Cloudera, Inc. All rights reserved. • Data Catalog: a comprehensive catalog of all data sets, spanning on-premises, cloud object stores, structured, unstructured, and semi-structured. Includes technical schemas from the Hive metastore, as well as business glossary definitions, classifications, and usage guidance • Security: role-based access control applied consistently across the platform using Apache Sentry. Also includes full stack encryption and key management • Governance: enterprise-grade auditing, lineage, and other governance capabilities applied universally across the platform with rich extensibility for partner integrations • Lifecycle Management: comprehensive ingest-to-purge management of data set lifecycle activities • Control Plane: multi-environment cluster provisioning, deployment, management, and troubleshooting SHARED DATA CONTEXT SERVICES Built for multi-function analytics anywhere WORKLOADS 3RD PARTY SERVICES DATA ENGINEERING DATA SCIENCE ANALYTIC DATABASE OPERATIONAL DATABASE DATA CATALOG GOVERNANCESECURITY LIFECYCLE MANAGEMENT STORAGE Microsoft ADLS COMMON SERVICES HDFS Amazon S3 CONTROL PLANE KUDU
  • 36. 36 © Cloudera, Inc. All rights reserved. HYBRID IS THE NEW NORMAL IN ML & ANALYTICS CLOUD • Elastic • Transient • IoT • Dev / Test • New locations ON-PREMESIS • Data sovereignty • Persistent • Legacy • Cost • Performance + Choice | Economics | Migration | Governance | Control
  • 37. 37 © Cloudera, Inc. All rights reserved. EXTENSIVE INTEGRATION WITH PUBLIC CLOUD VENDORS DATA ENGINEERING DATA SCIENCE ANALYTIC DATABASE OPERATIONAL DATABASE CLOUDERA ENTERPRISE Private Cloud Infrastructure-as-a-Service CLOUDERA ALTUS DATA ENGINEERING DATA SCIENCEANALYTIC DB Platform-as-a-Service beta beta soon Bare Metal
  • 38. 38 © Cloudera, Inc. All rights reserved. ENTERPRISE-PROVEN MACHINE LEARNING AND ANALYTICS MACHINE LEARNING Pattern recognition Anomaly detection Prediction Customers Run on Cloudera ANALYTICS Self-service intelligence Real-time analytics Secure reporting Customers Run IMPALA on Cloudera
  • 39. 39 © Cloudera, Inc. All rights reserved. DATA-DRIVEN JOURNEY USE CASES VISIBILITY Preventive & Proactive Maintenance IoT Hub for Industry 4.0 Advanced Threat Detection Risk Modelling & Analysis Marketing Systems Integration Customer 360 Insights Exploratory Data Science Data Warehouse Applied Machine Learning GROW Sales & Marketing CONNECT Operations & Product PROTECT Security & Compliance MODERNIZE IT, Tech, Data Science & Analytics
  • 40. 40 © Cloudera, Inc. All rights reserved. DELIVERING BETTER BROADBAND SERVICE • Deeper network analysis to better predict customer internet speeds and identify the cause of performance issues • Reduces truck rolls to save millions of pounds • Positions BT to take advantage of IoT for predictive maintenance on fleet service vehicles • Increased data velocity by 15X (5X the data in 1/3 of the time) DRIVE CUSTOMER INSIGHTS VISIBILITY PRODUCTIVITY TRANSFORMATION
  • 41. 41 © Cloudera, Inc. All rights reserved. CAPTURING AND GROWING MARKET SHARE WITH 10X MORE ACCURATE FORECASTS • Saves consumers and businesses up to 30% on electric bills • Improves accuracy of predictions, with error rate below 1% • Enables creation of micro-targeted campaigns in hours CONNECT PRODUCTS & SERVICES VISIBILITY PRODUCTIVITY TRANSFORMATION
  • 42. 42 © Cloudera, Inc. All rights reserved. DELIVERING DEEP INSIGHTS AND BEST PRACTICES IN BIG DATA SECURITY & COMPLIANCE • First PCI Certified Hadoop platform • Optimizes EDW and improves fraud detection and prevention • Secures 10 PB in a PCI-compliant manner every day • Security Information Event Management (SIEM) — monitor access to sensitive datasets, full audit trail of user behavior PROTECT YOUR BUSINESS VISIBILITY PRODUCTIVITY TRANSFORMATION
  • 43. 43 © Cloudera, Inc. All rights reserved. PARTNER ECOSYSTEM Focus on strategic partnerships to expand reach and accelerate consumption ISVs & SOLUTIONS CLOUD & PLATFORM SYSTEM INTEGRATORSRESELLERS
  • 44. # 44 Who is Datavard  Focus on SAP and Data Management: Business Transformation, SAP ABAP, and Big Data  Software products and consulting services  More than 200 projects p.a.  Customers of all industries, regions and sizes  No “me too” topics  Strong partnership with SAP since 1998  Privately held since 1998, 2018: 245 employees  Germany: Heidelberg (HQ), Hamburg | USA: Philadelphia, Washington DC Switzerland: Regensdorf | Italy: Milan | Central Europe: Bratislava | Singapore Explore Optimize Transform Innovate

Editor's Notes

  1. Hello Everyone I’m…Why you need… Thx Cloudera Now...How many of you know about SAP?
  2. Left corner SAP Right corner – I don’t need probably to talk about that Now for the purposes of my presentation I would consider SAP as the essential… expensive And Hadoop a cheap yet powerful…all kind of data To justify why I’m here today
  3. There are lot of cool companies… BUT 75% Now why would we want to connect… Because we are in trouble!!!
  4. Lots of trouble Please I ask you not to read… 3 years old slide, 1TB – 5TB Jim Rohn said – “You don’t need 5 reasons to fail, one is enough!” So - let me give you two biggest issues!
  5. Data Growth 1. 2016 2. Expensive systems Data Separation More vicious one, mostly for 21st century Units, Systems, security 1. What we are missing are the business questions? Why? 2. Well, if you have issues not related to your business 3. So what are the simplest but yet most related business questions?...
  6. Two valid business questions – Cost reduction and Revenue Increase Does that make sense to you? Do you want to solve those two first?
  7. Good so! How to do cost reduction? Or How much trash is in your system
  8. Forrester research: 12% of data is used in reporting Fitness test – answers how is your system being used?
  9. Datavard find out that in Average 35% of SAP data is temporary and can be deleted Let me talk money! IF you have spend 10MIL on your 20TB SAP Hana system You are using 1,2 – 1,8M out of 10 AND! 3,5M you spent on trash – what an investment 
  10. This is actually average of the biggest BW systems in the world Data allocation
  11. Now calculation based on real case scenario from last year Left side Comparison of system growth with our Data Tiering solution and Without Without Data Tiering solution exponential, with hadoop and data tiering solution we are more in the linear world Saving of ~3M on the SAP Hana in the horizon of several years Guys from Cloudera – can I spin a hadoop cluster in the Altus for 3Mil? So ROI is 2YEARS SO does this make sense to you to bridge sap with hadoop in order to do this??
  12. 1. Revenue increase - Or to increase value of your data – so you increase value of your business. 2. Fact of life is I cannot quantify in general… 3. And there are a lot of use cases around – but there is a rule of the thumb and it’s not coming from the computer world! 4. It is actually leadership 1.0
  13. Do you know what is the relation between output of group of people and the total of output generated by individuals? Anyone a People manager?
  14. Of course the output value of the group is higher. Only the diversity by itself is a value! Same it’s with your data. Combined data have much bigger value then individually presented. Let me give you an example 
  15. Do you know what is this mountain? It’s not any particular stock-price It’s average daily temperature in the month of March in Bratislava – central Europe – Stable continental weather – at least used to be
  16. We have one customer, premium retail shop. I love retails. Their SAP system is filled with details of inventory… But there used to be nothing about the weather data. Now let’s imagine that you… Come at the beginning of March for winter cloth – yeas business done check Spring cloth is history – winter jacket to tshirt You come 2nd week when there is 20 degrees more and they have winter jacket in sale? You want to buy Tshirt – either no buy or next shop in the market OR they tell you come in a week. When there is -6? So how can you create strategy when you have no clue on what is going to happen? Without diversity of the data you will be able to count only your loss in comparison to normal months 
  17. You start with a proper platform!
  18. You add core data and another source of data
  19. You want to know how your customers feel about the change
  20. And I recommend smart BI solution on top Small sanity check Does that make sense?
  21. 1. So how it fits together?
  22. We have our Issue and Business question connection? So lets add a solution
  23. Data Tiering from Datavard and Cloudera And Data Integration
  24. What brings us to direct streams and complete answer to why you should bridge SAP with a Hadoop! Now, I’ve said you don’t need 5 reasons to fail, one is enough Actually you don’t need to be successful in both areas to justify your big-data platform, one is enough! But do try both! Or do you need more?
  25. You want another? Data Protection How that relates to Hadoop? You have system with 20.000 users writing or reporting on data -> you don’t doo security analyses… You want more?
  26. Who would I be if I would not mention GDPR!  You want another? Data Aging! Now does that answer the question why you need to bridge SAP and Hadoop? What do you say? Exactly it makes complete sense to do it so HOW?
  27. I’ll answer that in case you are interested in a f2f conversation  If you allow me to spend time with you and get answer to few core questions I believe you can greatly benefit
  28. Ovum’s definition of a data lake is a governed repository that becomes the default ingest point for raw data. So here we are: an idyllic data lake side setting.
  29. Data Lakes got a ‘bad rap’ early on because they were just repositories. The tools and technologies for governance and data stewardship were missing or immature.
  30. ADD DATA PRIVACY HIGHLIGHTS So we introduced Cloudera SDX - or shared data experience – the foundations of Cloudera Enterprise. SDX makes it possible for companies to run dozens - hundreds - of analytic applications against a common pool of data. SDX applies a centralized, consistent framework for catalog, security, governance, management, data ingest and more. It makes it faster, easier, and safer for organizations, teams, people to develop and deploy high-value, multi-function use cases like customer next best offer, clinical prediction, and risk modeling. SDX cuts through silos to unify data, analytics, management, security, and governance, and empowers self-service
  31. BUSINESS CATALOG SERVICES (NOT JUST HMS) ALL DATA SETS, SCHEMAS, COLLABORATIVE TAGS, BUSINESS CLASSIFICATIONS, TARGETTED FOR EACH USER SDX is a set of open platform services built for multi-functional or multi-disciplinary analytics that have been optimized for the cloud. This means that we offer a unified security model that helps protect sensitive data with a consistent set of controls, that we offer a consistent governance model that enables self-service secure access to all of your relevant data. Not just one type of data, really to all of it, increasing your ability to be compliant, particularly in a regulatory environment. Next, easy workload management that increases user productivity and boosts job predictability. Next, flexible data ingest and replication. We have a number of core partners that we work with in this arena that help you aggregate a single copy of all of your data, providing you easier debt disaster recovery and that eases migration of data from one place to another. Last but not least, as I mentioned a moment ago, we offer a shared catalog that helps to define and preserve the structure and the business context of all your data, regardless of where it happens to reside. So, SDX is really a core piece of how we at Cloudera separate ourselves from the competition.
  32. Note: The content of this slide is based on the Success Story and video in 2015. The slide was created in NOV, 2016. Company Background: With £18 billion (about US$30 billion) in revenue in 2014, BT is one of the largest telecommunications providers in the world. The company serves more than 18 million consumers and nearly three million businesses. Use Case: For BT, the key to achieving sustainable, profitable growth in today's competitive landscape is its ability to broaden and deepen customer relationships. To support this goal, BT is using a Cloudera enterprise data hub (EDH) to accelerate data velocity and fast-track the delivery of new offerings to its customers. This EDH provides the backbone for an operational data store (ODS) that enables BT to break through data silos to ingest, store, and prepare data for myriad operational and analytical uses. Within one year, BT increased data processing velocity by a factor of 15, achieved an ROI between 200 and 250 percent, and is now positioned to take on new projects faster at a lower cost. Moving its ETL platform to Cloudera enabled BT to accelerate data velocity, processing five times the data in a third of the time. Following the success of its ETL initiative, BT is now utilizing the Cloudera to help deliver its broadband services. The speed of an individual line is dominated by its length (the distance from network equipment to a customer’s premises), but many other factors can have a significant impact on customer experience. BT uses Cloudera to join network topology (GIS) data with terabytes of DSL performance (time series) and electrical line test data to grade the quality of every line in the network. Using this network analysis, the probability of a successful outcome of an engineer dispatch can be predicted. This reduces wasted engineer visits and truck rolls BT’s work with Cloudera is also helping position the company to take advantage of the Internet of Things (IoT). Take its work with BT is part of the MK:Smart initiative for Milton Keynes (MK), a fast-growing town in Buckinghamshire, England. This initiative includes early IoT solutions such as sensors in car parking spaces that broadcast if the spots are vacant or occupied. Citizens and visitors can then use a smartphone app that guides them to the nearest free parking space based on the sensor data. According to BT, the same data ultimately will be used to better inform multi-million pound infrastructure decisions, such as the location and size of future car parks. IoT and fleet vehicle analytics are also a growing area for BT. The company offers fleet services as a managed service to other utility companies. One of the competitive features that BT can offer is the ability to instrument those vehicles and collect data from them. Ultimately, the company seeks to predict analytics around faults, so it can identify a vehicle failing early, improve the lifetime of that vehicle, and help reduce its overall carbon footprint.  SOLUTION HIGHLIGHTS Modern Data Platform: Cloudera Enterprise, Data Hub Edition Key Components: Apache Hive, Apache Impala, Apache Pig, Apache Sentry, Apache Spark, Cloudera Manager, Cloudera Navigator Industry Use Case: Telecommunications IMPROVED SERVICE PROCESS IMPROVEMENT IT COST REDUCTION Read more with the published story: http://www.cloudera.com/customers/bt.html
  33. Note: The content of this slide is based on the Success Story in JUNE, 2017. The slide was created in JUNE, 2017. Company Background: Podo is a Spanish utilities company, providing electricity to consumers and businesses across Spain. Use Case:   Podo is revolutionizing the utilities industry, using a cloud-based machine learning and advanced analytics platform from Cloudera and Google to help accurately predict future consumption patterns and provide consumers with fully customized rates. Data sources: Historical customer records IoT data from lights and connected devices Third party databases for government statistics and property records Solution Modern Data Platform: Cloudera Enterprise Workloads: Analytic Database, Data Engineering and Data Science Components: Apache Impala (incubating), Apache Spark, Cloudera Manager Analytic tools: R, Python, Matlab Cloud: Google Cloud Platform Industry Use Case: Customer 360° Network optimization Operational analytics Data monetization Read more with the published story: https://www.cloudera.com/more/customers/Podo.html?cq_ck=1497466958591
  34. Note: The content of this slide is based on the PCI Solution brief (http://www.cloudera.com/content/dam/cloudera/Resources/PDF/solution-briefs/MasterCard_PCI-Data-Security_SolutionBrief.pdf) in 2015. The slide was created in DEC, 2016. Company Background: MasterCard’s principal business is to process payments between the banks of merchants and the card issuing banks or credit unions of the purchasers who use the "MasterCard" brand debit and credit cards to make purchases. MasterCard Worldwide has been a publicly traded company since 2006 and had $9.5B in 2014 annual revenue and has 6,700 employees. Prior to its initial public offering, MasterCard Worldwide was a cooperative owned by the 25,000+ financial institutions that issue its branded cards. Use Case: MasterCard chose Cloudera Enterprise for fraud detection and to optimize their DW infrastructure and later expanded to form a partnership with MC Advisors, the consulting arm of MasterCard.  MasterCard requires that any technology handling its applications or payment card data files must have full PCI certification. Receiving this important certification allows MasterCard the opportunity to integrate Hadoop datasets with other environments that are already PCI-certified. Solution Modern Data Platform: Cloudera Enterprise Industry Use Case: Financial Services Fraud Prevention Read more with the published solution brief: http://www.cloudera.com/content/dam/cloudera/Resources/PDF/solution-briefs/MasterCard_PCI-Data-Security_SolutionBrief.pdf
  35. We are an open platform /open ecosystem – runs anywhere (shrunk version of platform) at center Show ISVs on top SIs on right Show /platform cloud on bottom
  36. Who we are