Transcript Business- Full-Speed-Ahead-Berg_v5
Technology Trends series
Business: Full Speed Ahead
Dr. Bjarne Berg
Session Agenda
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Introduction
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Why Change to In-Memory Processing?
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The Early Solutions
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SAP HANA an Introduction
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Demo
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New Innovation and Interesting Usages
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The Future and Big-Data Trends
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Questions and Answers
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Speaker Introduction
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Session Agenda
•
Introduction
•
Why Change to In-Memory Processing?
•
The Early Solutions
•
SAP HANA an Introduction
•
Demo
•
New Innovation and Interesting Usages
•
The Future and Big-Data Trends
•
Questions and Answers
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The Creation of Big Data 90% of all digital information was created in the last 3 years. By 2020 we will have 5,600 GB of data for every person on earth (incl. pictures, movies and music).
That is 40 Zettabytes!
The Issue: How do we store the big data and how can we access it faster?
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Where is the data Located and What Drives the Growth?
Source: WipPro, 2013 6
Who Benefits the Most of Big-Data Access?
Source: University of Texas 7
Data is Created Everywhere Total number of hours spend on facebook each month: 700 Billion Data sent and received by mobile platforms and phones: 1.3 Exabytes Number of emails sent each day: 2.5 Billion Data processed by Google each day: 2.4 Petabytes Videos uploaded to YouTube each day: 1.7 million hours Data consumed by each world’s household each day: 357 MB (growing fast)!
Number of tweets send each day: 50 Million Number of Products sold on Amazon each hour: 263 Thousand Every day we create 25,000,000,000,000,000,000 bytes of data!
(that is 25 quintillion bytes).
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Why In-Memory Processing? CPU Memory Technology 1990 2013 Improvement 0.05
MIPS/$ 304.17
MIPS/$ 6083 x 0.02
MB/$ 52.27
MB/$ 2614x Addressable Memory 2 16 2 64 2 48 x Network Speed 100 Mbps 100 Gbps 1000 x Focus Disk 5 Data Transfer MBPS 620 MBPS 124x
Source: 1990 numbers SAP AG, 2013 numbers, Dr. Berg Source: BI Survey of 534 BI professionals, InformationWeek,
Disk speed is growing slower than all other hardware components, while the need for speed is increasing.
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Why Change to In-Memory Processing?
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An History Lesson:
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File systems were created to manage hard disks
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Relational Databases were made to manage file stems
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Application Servers were created to speed up applications that ran on a database.
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Therefore:
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Hard drives are DYING!
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Relational databases are DEAD (they just don’t know it!)
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Application Servers will become less important
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Session Agenda
•
Introduction
•
Why Change to In-Memory Processing?
•
The Early Solutions
•
SAP HANA an Introduction
•
Demo
•
New Innovation and Interesting Usages
•
The Future and Big-Data Trends
•
Questions and Answers
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The Death of Storage and Access Technology is Normal
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The Rate of Change – Disruptive Technologies
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Moore’s Law in technology:
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Processing Speed will double every 18 month
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Paradigm shifts:
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SAP HANA queries are executed 400-900 times faster than on relational databases The rate of change in Paradigm Shifts is much faster than the incremental changes and a much lower cost
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Session Agenda
•
Introduction
•
Why Change to In-Memory Processing?
•
The Early Solutions
•
SAP HANA an Introduction
•
Demo
•
New Innovation and Interesting Usages
•
The Future and Big-Data Trends
•
Questions and Answers
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SAP HANA — In Memory Options
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SAP HANA is sold as an in-memory appliance. This means that both Software and Hardware are included from the vendors
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Currently you can buy SAP HANA solutions from Cisco, Dell, Fujitsu, IBM, and Hewlett-Packard
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The future of SAP HANA is to replace relational databases of ERP and data warehouses and run these on the in memory platform
Source SAP AG, SAP HANA has radically changed the way databases operate and make systems dramatically faster.
SAP HANA — In Memory Options There are currently 7 different certified HANA hardware vendors with 13 different products. Some boxes can be used as single nodes with others are intended for scale-out solutions for large multi-node systems Hardware
Cisco C260 Cisco C460 Cisco B440 Dell R910 Hitachi CB 2000 NEC Express 5800 Fujitsu RX 600 S5
128GB
X X X X Fujitsu RX 900 S2 HP DL 580 G7 HP DL 980 G7 HP BL 680 IBM x3690 X5 IBM x3950 X5 X X X
256GB
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Memory 512GB
X X X X X X X X X X X X X X X X X X X X X
1024GB
X+ X X+ X+ X X+ X+
The Hardware – IBM Example
SAP HANA — Available Special Applications
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New Applications has been built that run on SAP HANA in-memory processing and you can also build your own ERP
Database
Applications Databases Files
Virtual Data Marts Virtual Data Marts HANA Virtual Data Marts Virtual Data Marts Applications developed by SAP 1. Planning & consolidation 2. Customer revenue performance mgmt 3. Predictive segmentation & targeting 4. Trade promotion management 5. Merchandise & assortment planning 6. Sales & operations planning (SOP) 7. Demand signal repository 8. Profitability analysis 9. Dynamic cash management 10.Strategic workforce planning 11.Smart meter analytics (power companies) 12. and much more…
This provides much tighter integration with the source system (less data latency) and much faster query response time for high-volume analysis
SAP BusinessObjects Dashboards for Enterprise Management Dashboards can be built using the SAP BusinessObjects Dashboards tool that takes advantage of the sub-second speed of HANA.
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SAP Dashboards Example — Flexibility
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Graphs can be displayed many ways Navigation can be done and saved as “scenarios
SAP Dashboards — Mobile Example
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Dashboards are most useful when compared to something
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This dashboard is relative to a business plan
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Notice that all graphs can be displayed many ways and that color coding is consistent across dashboards The layout, buttons, and colors are consistently used and that the location of the objects aligns perfectly with each other.
Formatted Number based Dashboard Example Dashboards can also be highly formatted and static with little user interaction The In-Memory capabilities of HANA allows managers to see all financials in one-place in hyper-fast speed..
Operational Dashboards for Line Managers
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Dashboards can be operational
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This dashboard focus on billing disputes and is used to monitor closing of cases
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The users of this dashboard are clerks in the billing office, not executives With HANA – Real-time operational dashboards can by pushed to managers everywhere
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Link of HANA data to Maps and News Feeds
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Dashboards are most useful when shared with others
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Power users can create great departmental dashboards that can be shared inside smaller organizational units In this dashboard, the data is merged with Google maps and external news feeds. This makes the dashboard much more interactive and interesting.
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HANA Demo
Session Agenda
•
Introduction
•
Why Change to In-Memory Processing?
•
The Early Solutions
•
SAP HANA an Introduction
•
Demo
•
New Innovation and Interesting Usages
•
The Future and Big-Data Trends
•
Questions and Answers
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BI Workspaces and Modules BI Workspaces allows you to link many SAP BI tools in the same area, without the need to jump between them. In this workspace, we have 6 Objects with 4 different technologies.
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New Big-Data Innovation in Medical Field for Big Data
CAT Scan of Tumor Patient
CAT scans and X-Rays create an large amount of data that doctors have to review and access
X-Ray of Cancer Patient
HANA can store this and provide high volume and provide almost instant access to hundreds of Terabytes of data
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New Big-Data Innovation in Safety and Security
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Thousands of hours of video is taken at airports, banks, casinos, borders and other sensitive areas
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Facial recognition software can identify wanted criminals
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SAP HANA can store that data and process the information
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New Big-Data Innovation – Weather and Fishery tracking Tracking whether and execute predictive models require significant number of data points with high data volume Modeling resources such as fisheries and specie movements also require significant data volumes and data points on catch information across the globe
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New Big-Data Innovation – Company War Rooms
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In a multi-national company, data is created and consumed everywhere.
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With SAP HANA you can create a corporate war-room to track customer demand, shipments, marketing success and business intelligence This picture is from Sprint phone company’s war-room to track usage and transition issues during system mergers and product launches.
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New Big-Data Innovation – Pollution Tracking
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Geo data from pollution, data modeling and tracking creates hundreds of Terabytes.
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SAP HANA can assist in storing and retrieving this data With Predictive modeling and data visualization you can build sophisticated models on HANA. You can even use the R-statistical library
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New I Big-Data innovation – Scientific Discovery
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The super collider center CERN, creates over one PetaByte every second it operates.
The new Spectre R telescope of Russia has 1000 times higher resolution than Hubble, generating billions of bytes of data.
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Session Agenda
•
Introduction
•
Why Change to In-Memory Processing?
•
The Early Solutions
•
SAP HANA an Introduction
•
Demo
•
New Innovation and Interesting Usages
•
The Future and Big-Data Trends
•
Questions and Answers
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The Future and Big-Data Trends Big data is being generated from micro and macro levels.
From human DNA for each person to the content of billions of stars in galaxies.
A Map on the Whole Internet Internet usage Map by protocol Computer based human interaction is getting more common and generating tons in data each second Big-Data is only get more prominent. – can computers keep up?
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The Future and Big-Data Trends – More Imaging A data explosion visualized – Micro loans made Companies will start accessing data visually instead of by numbers and text.
Users will have on demand access to all movies, songs, information in sub second speed
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Summary
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SAP is a highly innovative company
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We are removing hard drives and relational databases
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Processing is going to in-memory
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SAP HANA can do all this today
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First we will move all data warehouses to HANA, then all ERP systems
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HAHA is much more than ECC and SAP BW (current tools)
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HANA is a paradigm shift with lower operating costs
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HANA is available today and is being implemented at hundreds of companies in regular industries right now.
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Questions and Answers
Dr. Berg [email protected]