This document discusses Smart Data Access (SDA) in SAP HANA. SDA allows virtual access to remote data sources without moving the data into HANA. It discusses use cases like accessing HANA and Sybase IQ data, using Hadoop for flexible data storage and processing, and leveraging HANA capabilities like spatial processing and analytics. Example implementations described are predictive maintenance using various data sources, real-time retail recommendations, and problem identification in telecom networks.

1. Smart Data Access

2.  Introduction to SDA
 Value Proposition
 Use Cases of Smart Data Access
 Accessing HANA to HANA via SDA
 Accessing “Cold Data” from Sybase IQ
 Hadoop as a Flexible Data Store & a Simple Database
 Hadoop As a Processing Engine & for Data Processing and
Analytics
 Leverage HANA Spatial Processing, Text processing, Predictive
Analytics
 SAP HANA Spatial Processing with SDA
 Sample Examples
 Implementation of a Predictive Maintenance Service Offering
 Implementation of Real-Time Retail Recommendations
 Implementation of Problem Identification in Telecom Operator
Network
AgendaAGENDA

3. Introduction to SDA
Smart Data Access is a data virtualization feature in SAP HANA that
allows access to data virtually from remote sources such as Difference
HANA systems, Hadoop, Oracle, Teradata, SQL Server and SAP
databases and combine it with data that resides in an SAP HANA
database.
In other Words ..
SDA enables remote data access to any other source or system without
having to move or replicate the data itself into SAP HANA.

4. SAP
HANA
SAP BW
Machine
Generated
Data
SQL
Or
SAP River
Business
Applications
Tightly integrated Orchestration for
Management, Monitoring and
control
Mobile
Applications
Data Fabric Layer
SAP HANA
Streaming
SAP IQ Op RDBMS
SDA
SDA
Map Reduce/
Hive
SAP Data
Services
Other Sources
Real -Time Events /
Machine - Generated
Data
Petabytes
of
Structured
Data
Load Source
Databases
Load Source Databases
SAP HANA Smart Data Access
SAP
HANA

5. SAP HANA Platform Converges Database, Data Processing
and Application Platform Capabilities & Provides Libraries for
Predictive, Planning, Text Processing, Spatial, or Business
Analytics

6. • Can easily setup virtual tables and start writing apps on SAP HANA.
• Due to virtualization, there is no need to load data from source to start
the project – saves cost, and is non disruptive
Easily utilize
enterprise wide data
• SAP HANA with SDA leverages processing capability of target sources
thus significantly optimizing query processing.
• Move minimal data between HANA and sources
High Performance
• The access to remote data is secured utilizing secondary credentials.
Secure access to
remote data
• Integrate output of Map-Reduce jobs in Hadoop/HIVE and access the
data from Hadoop seamlessly from HANA
Leverage Big Data
processing
• Store hot data in HANA, and cold data in disk based systems like IQ,
yet have seamless access from HANA
• Queries in HANA can integrate data from IQ and HANA
Seamless archived
data access
Value Proposition

7. • Build analytical applications on SAP HANA, with an access to data from other sources
using HANA smart data access, without moving data into HANA
• Initially supported databases – HANA, ASE, IQ, Teradata, HIVE/Hadoop
Developing Apps Using Dispersed Enterprise-Wide Data
• Access Hadoop/HIVE data from HANA virtual tables via ODBC
Federation of data in Hadoop/HIVE
• Using smart data access feature, HANA customers can access data in IQ
• Store archived/cold data in IQ, and real time in HANA
• Access IQ data as “hot-archive”
IQ as store for “cold data” in HANA
• Access BW on HANA data, from an instance of HANA
HANA to HANA
• Develop and deploy spatially-enabled analytics and applications, thus leveraging SAP
HANA Spatial capabilities
SAP HANA Spatial Processing
Use Cases of Smart Data Access

8. Use case for SDA in SAP HANA

9. Its possible to connect the Production schema of HANA to Development or Testing
schema to provide latest and real data for simulating live environment in Test data.
Access BW on HANA data, from an instance of HANA.
Combining Local Data marts to global database for Querying on Local as well as
Global Data.
Accessing HANA to HANA via SDA
Dispersed Enterprise-Wide Data
• Build analytical applications on SAP HANA, with an access to data from
other sources using HANA smart data access, without moving data into
HANA.

10. •Data is ready and/or written frequently
•In Memory
•No restriction. All features available
HOT
•Infrequent Access
•On Disk. No need to keep in Mem all the time
•No restriction. All features available
WARM
•Sporadic Access
•Not stored in HANA DB. Stored in Near Line Storage like Sybase IQ and
accessed via Smart Data AccessCOLD
Accessing “Cold Data” from Sybase IQ

11. Key Scenarios for Hadoop with HANA using
SDA

12. Hadoop as a Flexible Data Store & a Simple
Database
• Focus
• Cost effectively capture any type of low-level data.
• Capture Streaming data, Social media data, archive data or Enterprise data like
documents and images in a cost effective way to be retrieved later for analytics.
• Focus on data storage and retrieval by other systems
• Use as near line store for offloading data that is considered “cold”.
• Scenarios
ETL from Hadoop
• Combine analytic data in SAP HANA with data from Hadoop; aggregate data in Hadoop to create
online analytical processing (OLAP) fact tables for upload to SAP HANA.
Real Time Access from Hadoop
• Carry out direct queries on smart-meter data or other low-level data stored in Hadoop.
Real-time database for very large documents
• Use as a key to store and retrieve any large document, for example, a PDF, image, or Video.

13. Hadoop As a Processing Engine & for Data
Processing and Analytics
• Focus
• MapReduce programs can be written and deployed that execute
process logic on Hadoop data for many purposes, such as Pig for
data analysis and Mahout for data mining or risk analysis
• The inclusion of Hadoop impacts the way analytics solutions work
• Using Hadoop results in two fundamentally different approaches:
• Two-phase analytics
• Federated queries.
Normal Data Analytics Process

14. • SAP HANA provides Spatial Processing , Text processing capabilities
which can be leveraged on data stored in non HANA DB using SDA on
the fly without the need to move data.
• 80% of enterprise-relevant information originates in “unstructured” data
in the form of “ Text”
• SAP HANA provides
File Filtering and
Native Text Analysis
to extract data from
Text.
Leverage HANA Spatial Processing, Text
processing, Predictive Analytics

15. Real-time high-
performance
spatial processing
Store, process,
manipulate,
retrieve and share
spatial data
Unified modeling
platform
Combine spatial
with business data
Geo-content and
services

16. Spatial data provides the ability to answer an entirely new set of business questions with an
additional location dimension. It goes beyond just postal/zip codes for precise location
intelligence. It allows users to view, understand, interpret, question, and visualize data in a
way that reveals relationships, patterns, and trends in the form of maps.
Leverage native geo-spatial capabilities to store, pre-process, compute, and analyze huge
volumes of spatial data in real-time
Use data streaming with spatial visualizations for real-time comparative time-travel analytics
Enable ad-hoc queries to identify location-driven opportunities & risks
SAP HANA Spatial Processing with SDA

17. Sample Examples

18. • This is a use case for a computer server hardware manufacturer wants to be more familiar with
customer problems.
• They capture customer call center data and store that in Hadoop and determine potential problems in
servers.
• They took those call results and merged them with hardware monitoring logs and tried to correlate and
pull together
• They pulled this together with CRM and BOM together to get a complete picture of problems they were
experiencing
Implementation of a Predictive Maintenance
Service Offering

19. Implementation of Real-Time Retail
Recommendations
• Combine information from multiple sources like Social media data, Point-of-sale data, Historical Web
log information, Inventory and stock Information, CRM data, Real-time Web activity.
• Social media data, Point-of-sale data, Historical Web log for analyzing the customer’s likes, dislikes,
and previous buying behavior. Merge this data with inventory and stock information, the CRM data,
and information on what the customer is doing in real time on the e-commerce Web site.
• Immediate recommendations will be made for products the customer may be interested in purchasing
as well as local stores where those products are in stock.

20. Implementation of Problem Identification in
Telecom Operator Network

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