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Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
Autodesk Technical Webinar: SAP HANA in-memory database
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Autodesk Technical Webinar: SAP HANA in-memory database

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  • 1. SAP HANA Overview Jan Teichmann, P&I HANA Product Management November, 2013
  • 2. Disclaimer This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 2
  • 3. SAP HANA In-Memory Platform Platform for next-generation “smart” applications Developers Data Scientists Applications & Tools Business Users Executives Consumers Industry | LoB | Consumer | Analytics | Social | Cloud | Mobile Application Services Application Server | UI Integration Services | Web Server Processing Engine Event Processing | Planning | Calculation | Predictive Analytics Database Services Transactions | Analytics | Partitioning Compression | Availability | Encryption Rules | Text Mining | Search | Application Function Libraries | Geospatial Integration Services Unified Administration | Security Services Development | Connectivity | Lifecycle Management Services SAP HANA PLATFORM Mobile | XaaS | High-volume Replication | Real-time Replication | Hadoop SAP HANA is a completely re-imagined platform that transforms transactions, analytics, predictive, sentiment and spatial processing so that businesses can operate in real time. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 3
  • 4. SAP HANA and Real-Time Data Platform Architecture Overview Data Scientists Applications &T ools Business Users Executives Consumers Industry | LoB | Consumer | Analytics | Social | Cloud | Mobile SAP Replication Technology Application Services Application Server | UI Integration Services | Web Server Unified Administration | Security Services Development | Connectivity | Lifecycle Management Services SAP HANA PLATFORM Processing Engine Planning | Calculation | Predictive Analytics Database Services Transactions | Analytics | Partitioning Compression | Availability | Encryption Rules | Text Mining | Search | Application Function Libraries Integration Services Mobile | Federation | High-volume Replication | Real-time Replication | Hadoop SAP Data Services © 2013 SAP AG or an SAP affiliate company. All rights reserved. SAP ESP SAP SQLA SAP ASE SAP IQ Real-time Data Platform Transact | Analyze | Deliver Developers 4
  • 5. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Sub-second response, no matter how complex Core 1 Core N 1.5ns* L1 Cache 4ns* L2 Cache CPU CPU 15ns* CPU L3 Cache 60ns* Memory Bottleneck Memory Query Compressed Data Copy into memory Log Data Memory Hard Disk: 10,000,000ns* / SSD: 200,000ns* Disk Storage DB Code Storage App Log Any Column as Index Parallelized Query Storage SAP HANA (DB + App) Data  Process data and application logic in parallel (MPP), using all cores in a multi-core architecture, by effectively partitioning data.  Avoid unnecessary compensation (e.g.: buffering, data duplication) during application execution by running application using the SAP HANA application services (built-in web server).  Eliminate disk I/O by keeping all data in memory using column store, and by significantly compressing data.  Access data faster using any column as index, and by accessing only relevant columns via dictionary-encoded column store. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 5
  • 6. Technology trends: Amdahl’s law  Competitive DBs try to avoid HDD access, say with 99.9% success – Caching, indexes, aggregate tables, pre-fetching, hashing, compression, …  Pretty good? What is the impact of 0.1%?  10,000,000ns vs. 60ns: 150,000 times slower access! © 2013 SAP AG or an SAP affiliate company. All rights reserved. 6
  • 7. The Bottleneck has Shifted…  Access to memory is 4 times slower than L3 cache, and 50 times slower than L1 cache… © 2013 SAP AG or an SAP affiliate company. All rights reserved. 7
  • 8. Intel Xeon – Hyper-threaded Cores, Huge Caches L3 L2 Westmere-EX ALU 10 X State of the art: 10 pipelined cores (20 threads per CPU), 30MB L3 cache Hyper-threading: Sharing of one ALU between two threads; the chip handles the cycle-level taskswitching (when a thread is stalled, typically when it waits for memory) Draw ing from: http://www.phys.uu.nl/~steen/web09/xeon.php © 2013 SAP AG or an SAP affiliate company. All rights reserved. 8
  • 9. Chip Design – L1, L2 and L3 Level Cache – Columnar Processing Cache aware memory organization, optimization and execution Performance bottleneck in the past: Disk I/O Performance bottleneck today: CPU waiting for data to be loaded from memory into cache  Minimize number of CPU cache misses and avoid CPU stalls because of memory access. Approach: column-based storage in memory  Search operations or operations on one column can be implemented as loops on data stored in contiguous memory arrays.  High spatial locality of data and instructions, operations can be executed completely in CPU cache without costly random memory accesses  Memory controllers to use data prefetching to further minimize the number of cache misses Draw ing from: http://www.phys.uu.nl/~steen/web09/xeon.php © 2013 SAP AG or an SAP affiliate company. All rights reserved. 9
  • 10. Advantages Of Columnar Storage Advantage: Higher Data Compression Rates • Columnar data storage allows for highly efficient compression. Especially if the column is sorted, there are ranges of the same values in contiguous memory, so compression methods such as run length encoding or cluster encoding can be used more effectively. Advantage: Higher Performance for Column Operations • • • • Search operations or operations on one column can be implemented as loops on data stored in contiguous memory arrays. Compressed data can be loaded faster into CPU cache - performance gain (less data transport between memory and CPU cache) exceeds the additional computing time needed for decompression dictionary encoding, the columns are stored as sequences of bit encoded integers. That means that check for equality can be executed on the integers Computing the sum of the values in a column is much faster if the column is run length encoded and many additions of the same value can be replaced by a single multiplication. Advantage: Elimination of Additional Indexes • Storing data in columns already works like having a built-in index for each column: The column scanning speed of the in-memory column store and the compression mechanisms – especially dictionary compression – already allow read operations with very high performance. Advantage: Elimination of Materialized Aggregates Advantage: Parallelization • • In a column store data is already vertically partitioned. Operations on different columns can easily be processed in parallel. In multi-node clusters, partitioning of data (“shared nothing approach”) in sections for which the calculations can be executed in parallel leads to additional performance gains. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 10
  • 11. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Real-time applications, zero latency OLTP + OLAP in SAP HANA Traditional: OLTP and OLAP Separate 24hr Old Data ETL SAP HANA Current Data Aggregate 12:00:00 AM 6 Hours 6:00:00 AM 10:00:00 AM Immediate 10:00:01 AM  Run both transactional and analytical applications on one single data model. – Database tables designed to support simultaneous high volume/speed transactional and analytical processing without compromising data consistency (ACID compliance)  Aggregate on-the-fly with no pre-materialization on key figures, including current transactions, using column store and parallel aggregation. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 11
  • 12. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Process any data, in any combination, instantaneously with SQL Embed sentiment fact extraction in same SQL Support advanced text analytics Analyze text in all columns of table and text inside binary files with advanced text analytic capabilities such as: automatically detecting 31 languages; fuzzy, linguistic, synonymous search, using SQL. CREATE FULLTEXT INDEX TWEET_INDEX ON TWEET (CONTENT) CONFIGURATION 'EXTRACTION_CORE_VOICEOFCUSTOMER' ASYNC FLUSH EVERY 1 MINUTES LANGUAGE DETECTION ('EN') TEXT ANALYSIS ON; Embed geospatial in same SQL Structure unstructured data Use advanced text analytics, such as sentiment fact extraction, to structure unstructured data. CREATE COLUMN TABLE MYTABLE1 ( ID INTEGER, KEYFIGURE DECIMAL(10,2), SHAPE ST_GEOMETRY ); Embed fuzzy text search in same SQL Analyze streaming data from integrated ESP in combination with data in SAP HANA. SELECT SHAPE.ST_AsGeoJSON() FROM MYTABLE1; SQL CREATE FULLTEXT INDEX i1 ON PSA_TRANSACTION( AMOUNT, TRAN_DATE, POST_DATE, DESCRIPTION, CATEGORY_TEXT ) FUZZY SEARCH INDEX ON SYNC; Process geospatial data SAP HANA SELECT SCORE() AS SCR, * FROM "SYSTEM"."PSA_TRANSACTION" WHERE CONTAINS (*, 'Sarvice', fuzzy) ORDER BY SCR DESC; Any Data Customer Data RFID Smart Meter Mobile Point of Sale Geospatial Data Machine Data Connected Vehicles Structured Data Clickstream Social Network Text Data “ ” At BigPoint in the Battlestar Galactica online game, we have more than 5,000 events in the game per second which we have to load in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In this co-innovation project with SAP HANA, using Real Time Offer Management at BigPoint, we hope to increase revenue by 10-30%. Claus Wagner, Senior Vice President SAP Technology, BigPoint (video) © 2013 SAP AG or an SAP affiliate company. All rights reserved. 12
  • 13. SAP Sybase Event Stream Processor INPUT STREAMS Studio (Authoring) Sensor data Transactions SAP Event Stream Processor ? Dashboard Application Message Bus Market Events Database Reference Data • Unlimited number of input streams • Incoming data passes through “continuous queries” in real-time • Output is event driven • Scalable for extreme throughput, millisecond latency © 2013 SAP AG or an SAP affiliate company. All rights reserved. 13
  • 14. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Rapid data provisioning with data virtualization Modeling and Development Environment Application Data-Type Mapping & Compensate Missing Functions in DB SAP HANA Merge Results Application SELECT from DB(x) SELECT from DB(y) One SQL Script SELECT from HIVE Virtual Tables Modeling Environment Modeling Environment Modeling Environment Supported DBs as of SP6: HANA ,Sybase ASE, IQ Hadoop/HIVE, Teradata  Leverage remote database’s unique processing capabilities by pushing processing to remote database; Monitors and collects query execution data to further optimize remote query processing.  Compensate missing functionality in remote database with SAP HANA capabilities.  Accelerate application development across various processing models and data forms with common modeling and development environment. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 14
  • 15. SAP HANA Smart Data Access Data virtualization for on-premise and hybrid cloud environments Benefits Transactions + Analytics  Remote real-time query processing  Smart continuously self-tuning system  Secure access to heterogeneous data sources SAP HANA Heterogeneous data sources IQ Teradata     SAP HANA to Hadoop (Hive) Teradata SAP Sybase ASE SAP Sybase IQ ASE Hadoop SAP HANA © 2013 SAP AG or an SAP affiliate company. All rights reserved. 15
  • 16. SAP HANA Smart data access Differentiation The intelligence of knowing when to delegate query processing or pull the data into SAP HANA for query processing, based on the performance windows Data Federation Data Virtualization Smart Data Access  Dynamic query recommendation To return query results extremely fast. Capabilities supporting fast processing leveraging in-memory acceleration  Cost-based query optimization  Data pre-caching  In-flight transformation Converged data processing © 2013 SAP AG or an SAP affiliate company. All rights reserved. 16
  • 17. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Linear scalability to meet any time window With the power of mathematics and distributed computing, SAP HANA can predictably complete any information processing tasks, however complex, within a given time-window. Scale Up Extreme Linear Scalability Scale Out Query processing time (in seconds) 3.816 3.249 0.425 No disk Distributed computing Multi-core / parallelization Partitioning 0.7 0.266 16 nodes (100 billion rows) 0.491 51 nodes (650 billion rows) Query 1 Query 2 3.102 0.142 0.502 95 nodes (1,200 billion rows) Query 3 Sales and Distribution reports Query 1: Single customer and material for one month Query 2: Range of Customers and Materials for six months “ ” SAP HANA Performance, July 2012 Query 3: Year-over-Year trending report for Top 100 customers for five years It is only a matter of scaling the hardware – there are no other variables or unknowns. SAP HANA: Re-Thinking Information Processing for Genomic and Medical Data, Prof. Dr. Hasso Plattner, 2013 © 2013 SAP AG or an SAP affiliate company. All rights reserved. SAP HANA scales better than linearly for workloads with increasing capacity (up to 100 TB of raw data), complexity (queries with complex join constructs and significant intermediate results run in less than two seconds), and concurrency (25-stream throughput representing about 2,600 active users). 17
  • 18. Certified HANA Hardware – June 2013* (only China) XS: 128GB X X S: 256GB X X X S+: 256GB X X X M: 512GB X X X M+: 512GB X X X X L: 1.0TB X X X X X Scale Out (BW) X X X X X X X 1/2/4 1/2/4 2/4 1 1 1/2/4 2 X X X X X X X X X X X SoH: 1/2/4TB High Availability X X X X X X X X X X X X X X X X X X X planned X planned DR – Storage Repl.: Async DR – Storage Repl.: Sync X * For most up to date list please go to the SAP Product Availability Matrix © 2013 SAP AG or an SAP affiliate company. All rights reserved. 18
  • 19. Multi-SID on one SAP HANA hardware Productive Systems White-Listed Scenarios Non-Productive Systems „Classical“ scenario “MCOD” “MCOS” Virtualization (on premise) Appliance approach for optimal performance Multiple Components on one Database Multiple Components on one System, multi-SID  1 x Appliance  1 x Appliance  1 x Appliance Virtualization technology separates multiple OS images each containing one HANA DB  1 x HANA DB  1 x HANA DB  n x HANA DB  n x Virtualized Appliances  1 x DB schema  n x DB schema  n x DB schema  n x HANA DB  1 x Application (e.g. ERP, CRM or BW)  n x Applications  n x Applications  n x DB schema Prod. usage for white listed scenarios allowed, e.g. SAP ERP together with SAP Fraud Management. See SAP notes AS ABAP SID: Application 1661202 and 1826100. ABC SID: XYZ E.g. DEV and QA system on one hardware. See SAP note 1681092. AS ABAP SID: ABC SAP HANA <HDB> Schema ABC SAP HANA <HDB> Schema ABC Schema XYZ © 2013 SAP AG or an SAP affiliate company. All rights reserved. AS ABAP SID: ABC SAP HANA <HDB1> Schema ABC AS ABAP SID: XYZ SAP HANA <HDB2> Schema XYZ  n x Applications AS ABAP SID: ABC AS ABAP SID: XYZ SAP HANA <HDB> Schema ABC SAP HANA <HDB> Schema XYZ 19
  • 20. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Bring your own code to an open platform Browser / Mobile Web JS Lib Third Party & Custom Application Data Viz Lib http(s),OData/JSON Web App Server ODBO ODBC, JDBC HTTP(S), OData, XML/A ODBC, JDBC, ADBC, ODBO MDX, SQL  Easy to bring data into HANA. – Reuse current data sources with Data Virtualization. Any HTML5/JS Library – Replicate real-time data from multiple sources into SAP HANA for comprehensive data analysis. DB Services Stored Procedure  Build new web applications with any open source HTML5 / JS libraries, Server Side Java Script. – Import data in CSV, Excel or Binary formats. Load Geospatial files in shapefile, CSV, Binary, WKT and WKB file formats. SAP HANA App Services (Web Server)  Easily migrate your applications (e.g.: Java, PHP, .NET) using JDBC, ODBC and OData/JSON.  Open Cloud Partner Program allows you to select the best SAP HANA cloud deployment option from several partners. Virtual Tables SQL Script Real-time Replication Import CSV, Binary, shapefile, WKT and WKB files © 2013 SAP AG or an SAP affiliate company. All rights reserved. 20
  • 21. SAP HANA - Openness SAP is committed to a Truly Open Ecosystem for SAP HANA • Intel partnerships for CPU optimization and Hadoop distribution • 11 Hardware partners with > 70 available hardware landscapes, incl. Virtualization • Open APIs for BI (MDX, SQL), WebDevelopment (HTTP/S), Dev Platforms (ODBC/JDBC) • 3rd party Software certification for backup infrastructures, integrate SAP HANA within bigger management environments, or provide Single-SignOn (SSO) capabilities • Several (growing number of) Cloud Service Providers • http://www.saphana.com/community/blogs/blog/2 013/09/24/engineering-open-appliances-for-highperformance-without-lock-in © 2013 SAP AG or an SAP affiliate company. All rights reserved. 21
  • 22. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Transformative power, simplified programming Browser / Mobile Browser / Mobile + + Web JS Lib HTML5 /JS Libraries Data Viz Lib http(s) Web App Server http(s), OData / JSON App Logic App Logic App Logic App Logic App Logic App Logic SAP HANA BRM App Services (Web Server) SQL App Logic App Logic App Logic Text Mining http(s) App Logic App Logic App Logic DB Server Predictive Java Script DB Server OLAP Text Mining Stored Procedures Predictive Aggregate Standard Table: + OData Procedural App Logic DB-oriented Logic R Integration Decision Tables SQL Scripts Flexible Table: + + +  Push-down code : Replace application logic at multiple places with reusable DB logic, written in SQL Script, consumed through OData.  Efficient execution with built-in application services : Significantly improve application performance by running applications using SAP HANA application services (built-in web server) to avoid multiple layers of buffering, to reduce data transfers, and processing logic.  Optimized and open: Built-in SAPUI5 libraries with open integration to 3rd-party libraries for both desktop and mobile user experience.  Dynamic Schema: Dynamically add up to 64,000 columns with SQL Insert or Update statements without ALTERing schema. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 22
  • 23. Compare HANA Web App Development To Classic Web Dev Java + MySQL lib Java + HANA R HANA XS R © 2013 SAP AG or an SAP affiliate company. All rights reserved. 23
  • 24. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice “See” the future accurately in real-time Apps Apps KNN classification Logic Predictive SQL Script Logic R (Optimized Query Plan) R Engine Pre Process Pre Process Pre Process R-scripts PAL Unstructured Geospatial K-means Associate analysis: market basket ABC classification Weighted score tables Regression Virtual Tables C4.5 decision tree OLAP Unstructured  Accelerate predictive analysis and scoring with in-database algorithms delivered out-of-the-box. Adapt the models frequently.  Execute R commands as part of overall query plan by transferring intermediate DB tables directly to R as vector-oriented data structures.  Predictive analytics across multiple data types and sources. (e.g.: Unstructured Text, Geospatial, Hadoop) “ ” The HANA platform at Cisco has been used to deliver near real-time insights to our execs, and the integration with R will allow us to combine the predictive algorithms in R with this near-real-time data from HANA. The net impact is that we will be able to take the capability which takes weeks and months to put together, and deliver just-intime as the business is changing. Piyush Bhargava, Distinguished Engineer IT, Cisco Systems (video) © 2013 SAP AG or an SAP affiliate company. All rights reserved. 24
  • 25. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice De-layer, de-clutter. Consolidate! Lifecycle Mgmt./Admin/Monitoring Tools Development / Modeling Tools Unified Development/Modeling/ Admin/Monitoring with Eclipse-based tool Event Processing Enterprise Search $ Business Rule Management $ $ Data Warehouse Appliance Planning SAP HANA $ Data Warehouses Text Analytics / Mining / Unstructured Data $ Predictive Analytics Geospatial $ ETL Web Application Server Multiple Databases Database Cache  Simplify development, modeling and administration environments with Eclipse-based tool.  Reduce TCO by consolidating heterogeneous servers into SAP HANA servers to reduce hardware, lifecycle management, and maintenance.  Avoid hidden costs due to data quality, synchronization and latency. “ ” Pointing to Glass' Law (sourced to Roger Sessions of ObjectWatch), which states that "for every 25 percent increase in functionality of a system, there is a 100 percent increase in the complexity of that system," Gartner emphasizes the ability of an enterprise to get the most out of IT money spent. Gartner © 2013 SAP AG or an SAP affiliate company. All rights reserved. 25
  • 26. Top 10 1 2 3 4 5 6 7 8 9 10 Speed Real-Time Any Data Any Source Predictable Completion Open Simplicity Prediction Consolidation Choice Choose and change deployment options any time Limited Scale Any Scale SAP HANA SAP HANA SAP HANA One (Premium) Public Cloud  Managed by Amazon Web Services (AWS), Korea Telecom, Portugal Telekom and VM Ware.  60.5 GB instance size allowing for 30 GB of data.  HANA One : – 99¢ per hour. Pay as you use. Community Support. SAP HANA Appliance On Premise  Choose hardware (Intel x86 based architecture) from hardware vendors HP, IBM, Fujitsu, Cisco, Dell, NEC, Hitachi, Huawei, and VCE as of July 2013.  Scale as required. Elastic Scale SAP HANA SAP HANA Enterprise Cloud Managed Private Cloud  Real-time platform, infrastructure, and fully managed services from SAP or from our trusted partners.  Bring your existing licenses to run all SAP HANA applications.  Mission critical, global 24x7 operations.  Start using SAP HANA right away.  HANA One Premium : – USD 75,000 per year including SAP Enterprise Support. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 26
  • 27. Definition: Public and Private Cloud and Managed Service Market View IDC‘s Cloud Services Deployment Models IDC, 2013 http://www.idc.com/prodserv/FourPillars/Cloud/downloads/239772.pdf © 2013 SAP AG or an SAP affiliate company. All rights reserved. 27
  • 28. Definition: Public and Private Cloud and Managed Service Market View IDC‘s Cloud Services Deployment Models SaaS PaaS HANA Apps* HANA Enterprise Cloud** HANA Appliance* IaaS Successfactors, Ariba, SoD, ByD … HANA Cloud Platform HANA One / Dev Edition HANA Cloud Infrastructure IDC, 2013 * For on-premise: Software / Platform / Infrastructure http://www.idc.com/prodserv/FourPillars/Cloud/downloads/239772.pdf © 2013 SAP AG or an SAP affiliate company. All rights reserved. 28
  • 29. Summary: SAP HANA In-Memory Platform Ideal platform for next-generation “Smart” applications Key capabilities required for next-generation “Smart” applications: Personalized recommendation with machine learning, predictive and rules Natural language processing Process any variety/volume (e.g. unstructured) Respond within predictable time windows SAP HANA is a high speed processing platform to enable: Easier Processing: Easier Ingestion: Easier Consumption: Easier Development:  NLP, Predictive, R-Integration.  Spatial processing, ad-hoc OLAP views.  Data virtualization.  Replication, streaming, ETL/ELT.  Integration, data cleansing.  HTTP(S), OData, XML/A.  ODBC, JDBC, ODBO.  SQL, MDX.  JavaScript, HTML 5.  Connect any programming language.  App/web services.  Decision table. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 29
  • 30. Demo
  • 31. What is a spatially enabled database? The ability to store, process, manipulate, share, and retrieve spatial data directly in the database Allows for the ability to process spatial vector data with spatial analytic functions: Multi-polygon point line polygon  Measurements – distance, surface, area, perimeter, volume  Relationships – intersects, contains, within, adjacent, touches  Operators – buffer, transform  Attributes – types, number of points Can store and transform between various 2D/3D coordinate systems Vector and raster support Complies with the ISO/IEC 13249-3 standard and Open Geospatial Consortium (1999 SQL/MM standard) © 2013 SAP AG or an SAP affiliate company. All rights reserved. 31
  • 32. Spatial Processing Architecture Introducing in SAP HANA SP6:  New spatial data types (ST_POINT & ST_GEOMETRY)  Optimized data types for spatial  Extended SAP HANA SQL with spatial functions  Columnar storage of spatial data  Native spatial engine as part of Index Server  Access via SQL or Calculation Models/Views Supports:      2D – Vector Types Points, line-strings, polygons, compound polygons Spatial functions SRID (Spatial Reference ID’s) Application development on XS with geo-content and mapping services © 2013 SAP AG or an SAP affiliate company. All rights reserved. 32
  • 33. SAP HANA Spatial Ecosystem Analytics Visualization Applications Interfaces / Services SAP Info Access (HTML5) Mobility odbc, jdbc, XS (InA, geoJSON, API, ODATA) SQL / Calculation Models Data Access Types & Functions: • Point • Linestring • Polygon • SRID metadata • Spatial function library SAP HANA (OGC Compliant) Data Integration Tools GIS Load tools: • SAP Data Services • SAP Event Stream Processor • Clustering • Spatial Joins Engines: • Indexserver • Calc • Spatial • Attribute • XS Geo-Services: • Geoservices • Geocontent Views: • Analytical • Attribute • Calculation Geospatial Import/Export: • Shapefile, csv, binary • WKT / WKB Support Data Sources SAP Data © 2013 SAP AG or an SAP affiliate company. All rights reserved. Non-SAP Data Spatial Data Real-Time Data GIS 33
  • 34. SAP HANA and Esri ArcGIS – Interoperability Vision Esri ArcGIS  Map creation, editing, and publishing Esri ArcGIS Server  Geospatial location analytics Mapping Services Analytic Services Content Services  Geocontent and services Esri QueryLayers Spatial Data Server REST Services SAP HANA  Real-time in-memory columnar database  OGC Compliant CVOM Shapefile  Spatial types and processing Esri ArcGIS + SAP HANA Import / Export  Scalable platform for real-time highperforming spatial and analytic processing Esri  Integration of spatial and non-spatial data and analytics to answer more questions  Lower TCO and TCD © 2013 SAP AG or an SAP affiliate company. All rights reserved. ArcSDE Geodatabase Technology SAP HANA Internal 34
  • 35. SAP HANA Spatial Application Development Quickly develop and deploy SAP HANA based spatial applications with provided geo-content and map services via the native XS engine Capabilities: HTML5 Application iPad/ Browser SAP HANA XS Spatial Engine Maps Geocoding Geocontent SAP HANA Location Services Services © 2013 SAP AG or an SAP affiliate company. All rights reserved.  SAP HANA spatial application development components include: Location Services (on-premises or cloud), Geo-Content, Application Interfaces, Services  Allows for visualization, interaction, and exploration of spatial data in SAP HANA via maps  Supports HTML5 deployments for browser or iPad  Consumes SAP HANA models  NOT a general purpose BI or GIS tool! Benefits:  Quick development and deployment time  Low TCO & TCD and fast response times with 2-tier architecture  Components, content, and services included with SAP HANA; can also use other map svcs 35
  • 36. SAP HANA Spatial Roadmap Advanced Spatial Capabilities Geodatabase and 3D Support Spatial Compliance  Full OGC compliance  Full integration of spatial data-types  Vector spatial data types and functions  Additional OGC features  Import/export capability  Additional product libraries  BI/GIS interoperability  Advanced spatial functions  Geo-content and services  Additional third-party interoperability  Geo-application development platform  3D type and function support  Application enhancements to support and leverage spatial Short-Term Mid-Term  Raster support and processing  Support as a Geodatabase  Non-Geo visualization tool support (Visual Enterprise) Long-Term This is the current state of planning and may be changed by SAP at any time. © 2013 SAP AG or an SAP affiliate company. All rights reserved. Internal 36
  • 37. Feedback, Q & A Thanks for attending this Webinar.

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