PUBLIC
Amit Satoor, SAP
March Hartz, SAP
Capture Business Opportunities from Systems of Record and Systems of Innovation
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Machine
Learning
Blockchain
Big Data
Internet of
Things
Analytics
Business expectations
Deep data insights
Broad data scope
Access to most recent
data
Answers in real time
Not delayed by constant
data preparation
Predictive capabilities
SAP Leonardo
technologies
Internet of Things
Machine learning and
artificial intelligence
Big Data
Analytics
Blockchain
Big Data transformation powers digital innovation systemRelevant nuggets of information are still hard to find
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Why is it difficult to capture new opportunities?Silos, data movement, and complexity in understanding raw data hinder business agility and innovation
Proliferating
data lakes
Multiple EDWs,
data marts
Exploding data volumes
Accelerated data velocity
Increasing data variety
Cloud
storages
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Low latencyImmediate response
FrictionlessSharing and enrichment
for thorough
understanding
IndependenceOf data and compute
for right-place
decisions
Logical modelIntegrated for 360-
degree business view
FreedomCloud, hybrid, or
on premise
7 LAWS OF UNIVERSALDATA
GovernanceSecurity, auditing,
and compliance
PerformanceTo handle all types
of needs
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Critical components for
universal data foundation
Business
transformation
Data sharing
Processing and
computing
Storage
Ingestion
DATA-DRIVEN
AUTOMATION
INTEGRATIONBUSINESS VALUE
FLEXIBILITY
AGILITY
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A radical new approach to uncover new opportunitiesEmbrace new innovations to run and differentiate your business
Big Data
Applications
Big Data
Analytics
Big Data
Computing framework
Big Data
Managed services
Big DataEnterprise
data
SAP HANA
platform
SAP BusinessObjects
solutions
SAP solutions
for EIM
SAP Vora
SAP Cloud
Platform Big Data
Services
SQL
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DATABASE MANAGEMENT
Web server JavaScript
Graphic modeler
Data virtualization ELT and replication
Columnar OLTP+OLAP
Multicore and parallelization
Advanced compression
Multitenancy Multitier storage
Graph Predictive Search
Dataquality
Seriesdata
Business functions
Hadoop and Spark integration
Streaming analytics
Application lifecycle management
High availability anddisaster recovery
OpennessDatamodeling
Admin andsecurity
Remote data sync
Spatial
Text analytics
SAP Fiori UX
ALM
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APPLICATION DEVELOPMENT DATA INTEGRATION AND QUALITYADVANCED ANALYTICAL PROCESSING
SAP HANA platform The in-memory data platform for digital business
SAP, ISV, and custom applications
All devices
OLTP + OLAP ONE open platform ONE copy of the data
SAP HANA platform
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Files
SAP Vora
Spark…
Hadoop
Spark
Distributed transaction log
Disk-to-memory accelerator
Data modeler
Relational Time series Graph Doc store
SAP Vora
Distributed computing cluster
SAP VoraDistributed computing solution to uncover actionable insights from Big Data
Other apps
SAP HANA
platform
Files
SAP Vora
Spark
Files
SAP Vora
Spark
Optional
Data
sciencePredictive Business
intelligence
Visualization
apps
In-memory
store
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Rapidly and efficiently acquire and consolidate all dataIntegrate and ingest massive amounts of diverse and arbitrary data
Leverage data in real time for
innovation, faster deployment,
and lowered total cost of
ownership (TCO)
Quickly acquire, stream, replicate, and
transform any kind of data at any velocity
Connect relational databases, classic EDW,
and Hadoop sources quickly
Integrate any data source – flat file, relational,
Big Data, or event
Employ simple and powerful data acquisition,
streaming, and transformation capabilities
Use efficient tools for data movement,
cleansing, and placement throughout the
architecture
Data
virtualization
ELT/
replications
Hadoop/
open source
integration
Remote data
syncData quality
Data quality MDMData
integration
Data discovery
and
architecture
Information
lifecycle
management
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Manage massive volumes of data with elastic storage and persistence
Leverage ALL data for innovation
for quicker ROI and reduced
latency and TCO
Organize and store petabytes of data for
ready analysis
Use native multitemperature management
and virtualization; reduce data movement by
accessing and processing data from any
source at its location
Optimize and integrate data warehousing with
logical data modeling
Use full virtualization and schema extension
capabilities; federate data from any data
source
Columnar
OLTP+OLAP
Multicore and
parallelization
Advanced
compressionMultitenancy
Multitier
storage
Data modeling Admin and
securityHigh availability
and disaster
recovery
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Achieve real results to get the insights you need, alone or in combinationUnified in-memory computing services and distributed processing
Discover insights to drive
innovation and make better and
faster business decisions
In-memory first architecture for operational
systems and Big Data landscape
Integrated multimodal advanced analytics to
converge multiple complex analytic processing
without data movement or duplication; native
multimodal SQL (predictive, search, text,
spatial, graph, series, streaming)
Accelerated OLAP analysis and scoring with
built-in algorithms; ability to adapt the models
frequently
Multiplatform architecture to deploy in the
cloud, on premise, or hybrid
Spatial GraphMachine
learningSearch
Text
analytics
Streaming
analytics
Series
data
Business
functions
OLAP Time series Graph Doc store
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Application services
Database services
Integration services
SAP HANA platform
Smart data
access
Smart data
integration
Streaming
analytics
Remote data
sync
SAP Vora
Spark
SAP Vora
Spark
IBM DB2,
SAP IQ,
Oracle, MS
SQL Server,
Hive
IBM DB2,
Oracle, MS
SQL Server,
Twitter, Hive,
OData
LoadingFederation Streaming Synchronize
SAP HANA and SAP Vora: Connecting data from many sources
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Query results
Query processing
Real-time views Batch viewsServing layer
Real-time
processingSpeed layer
Batch
processing
All data“master dataset”
New data
1 2
3
Overwrite
Update
Enabling Big Data processing patterns: Lambda architecture
Ingest/appendIngest
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Query results
Real-time views
Real-time processingSpeed layer
Unified log
New data
Raw dataLong-term storage
Update
Recompute if
needed
Enabling Big Data processing patterns: Kappa architecture
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External
data source
External
data source
Data
collection
technologies
(Kafka,
Flume)
MapReduce
Offline processing
Enterprise applications Analytics toolsOther SAP HANA
clients
SAP HANA data
warehouse
Universal Big Data foundation for SAP Leonardo
SAP Vora
SAP HANA platform
Streaming analytics
Real-time
processingDynamic
tieringSAP HANA disk store
In-memory store
SDA
SAP HANA processing engines
Other data
stores(for example, SAP IQ)
SDA
In-memory processing
Spark
Raw dataPre-
processed
data
Data
discovery
tools
HDFS
vUDF
SAP Vora processing engines
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Powerful end-to-end IoT scenario
Go from ingestion to consumption with a
modern architecture and ingestion pipeline
Harness high-volume data results (for example, sensor data)
Manage single or multiple data lakes and environments with
the needed data quality
On-premise Hadoop, SAP Cloud Platform Big Data Services,
and/or Amazon S3
Ingest and process data for enterprise applications
Automate, design, and run all data processes on the data hub
Define and execute the IoT ingestion process
Data hub for the Internet of Things (IoT)
IoT apps (SAP Cloud Platform)
Data processing and orchestration
Big Data lakes
Ingestion and stream
IoT gateway and services
Sensors
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Current situation
8 TB a month and over 1 PB
in a 10-year period
TBs of data each month
Must retain data for 10 years
Must accurately forecast
energy usage
Current DW technology is
approaching end of life
Massive amounts of data
stored in proprietary solution
is hard to manage and with
a high total cost of
ownership
Utilities rise to the smart meter challengeSmart meters lead utilities to reconsider how they use data
Benefits of
integrating Big Data
Meet regulatory
requirements
Forecast energy usage
Detect fraud and predict
maintenance requirements
Respond intelligently to
variations in supply and
demand with smart grids
SAP Vora SAP HANA
Historical sensor data
Parallelized
queries
Dynamic tiering
Query data within and across tiers
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Solution
HOT+WARM+COLD data management
strategy leverages SAP HANA data
compression and data tiering
Combined SAP HANA, dynamic tiering,
and Hadoop into a single landscape
Automatic access to stored data in SAP
HANA, dynamic tiering, and Hadoop and
SAP Vora with query execution
Automated data movement between
storage tiers using the data lifecycle
manager
Foundation for advanced predictive
analytics and future business capabilities
The results of simplified Big Data ownership with
SAP HANA, dynamic tiering, SAP Vora, and Hadoop
Results
Instant real-time analytics through SAP
HANA
75% savings in storage costs compared to
current solution
Seamless integration with Hadoop
technology, enabling data scientists to
access and manage Hadoop data
Ability to charge business based on data
storage and performance requirements
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Big Data strategy from SAPTop reasons and capabilities
Comprehensive approachCombines integration, workflow, pipelining,
and data governance in one place
Enterprise-wide discoveryProvides the ability to understand the entire landscape, the
individual systems, and the data within
Embracing the entire data landscapeDiversity of data sources, data types, and endpoints;
cloud or on-premise; SAP and non-SAP data
Unified management Enables more users with a visual and unified solution to
manage, monitor, and develop powerful data pipelines
Data-driven architectureMassively scalable, containerized, and distributed
architecture build for serverless computing
Big
Data
Enterprise
data
SAP HANA
platform
SAP BusinessObjects
solutions
SAP solutions
for EIM
Hadoop
integration
SAP Vora
SAP Cloud
Platform Big Data
ServicesSQL
Data sharingGovernance | Streams and pipelines | Orchestration
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Join the Big Data product leadership programUnearth business signals from Big Data landscapes to grow value
Be in the know
Get the latest news and access to special
activities
Early adopter care
Gain competitive advantage Get
implementation supportProvide product feedback
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