Transcript Title — Times New Roman 28pt, line spacing .85 Title 2

Chicago
Business Objects
User Group
HANA Vs Sybase IQ
Chinni Ranganath, Deloitte
June 1, 2012
Perspective
Integration with
# Prod Inst.
Scalability
Cost
BO and others
Query Performance
Our Perspective
•
HANA or Sybase IQ only comes into the picture with ‘Big Data’
•
HANA is being built to allow truly real-time analysis on transaction data by handling OLTP and OLAP
processing workloads by one instance while IQ is being built to compete in specialized analytics
markets competing with Greenplums, Teradatas, Netezzas, Exalytics and ParAccels of the world.
Analogy
Objective: Is not to show the superiority of one tool versus other, but rather bring some reality
and perspective to the HANA discussion
2
Big Data Defined
In information technology, big data consists of data sets that grow so large and complex that they become
awkward to work with using on-hand database management tools. Difficulties include capture,
storage, search, sharing, analytics, and visualizing. This trend continues because of the benefits of working
with larger and larger data sets allowing analysts to "spot business trends, prevent diseases, combat
crime." Though a moving target, current limits are on the order of petabytes, Exabyte and zettabytes of data.
Wikipedia
You have a Big Data Challenge/Opportunity,
1.
If your data volume is growing into unmanageable levels (petabytes, Exabyte or zettabytes)
2.
If your organization is generating variety of unstructured data: email, web, logs, scientific,
machine generated and etc
3.
If database latency is causing business operational challenges, velocity at which data need to be
analyzed for informed real-time decision making.
Volume
3
Velocity
Variety
Big Data Solution(s)
Addressing the challenge of Big Data will require a technology or combination of technologies that are
capable of :
Supporting automatic parallelization such as query optimization, queries across segment servers
5.
Linear scalability to linearly scale compute performance
Oracle Exalytics
4.
SAP HANA
Supporting Not Only Structured Query Language (NO SQL) but also for unstructured data
HP Vertica
3.
Sybase IQ
Supporting Massive Parallel Processing (MPP) , i.e., potentially distributed across thousands of
heterogeneous processors
EMC Greenplum
2.
IBM Netezza Appliance
Supporting large volumes of data such as petabytes and Exabyte.
Hadoop
Cassandra
1.
Big Data
Deloitte’s Point Of View
We do not view In-Memory and In-database technologies as overlapping, but rather as
complementing technologies having distinct roles to play in ‘Big Data Solution
Framework’ depending upon the analytical application and business priorities.
4
What is in-memory technology/computing?
• Brings data close to the CPU for quick reads and/or writes
• Stores the data off disks into the system's main memory which significantly minimizes the
overall time taken by the CPU to access data due to the reduced I/O for retrieving data.
• Utilizes a memory resident database for data management and access. Similar to
traditional database management systems, In-Memory database management also
supports the standard atomicity, consistency, isolation, durability (ACID) properties.
5
* Source: SAP HANA Overview & Update presentation
What is in-database technology/computing?
• In-database processing, sometimes referred to as in-database analytics, refers to the
integration of data analytics into data warehousing functionality. Today, many large
databases, such as those used for credit card fraud detection and investment bank risk
management, use this technology because it provides significant performance
improvements over traditional methods.
– Wikipedia
• Traditional approaches to data analysis require data to be moved out of the database into
a separate analytics environment for processing, and then back to the database. Indatabase processing moves processing to database thus avoiding physical movement of
data into separate analytics environment for processing. Doing the analysis in the
database, where the data resides, eliminates the costs, time and security issues
associated with the old approach by doing the processing in the data warehouse itself
• With Sybase IQ in-database analytics enterprises and application vendors answer complex
questions without having to move mountains of data to 3rd party tools. With hundreds of
statistical and data mining techniques, advanced text analytics capabilities, and APIs to
execute proprietary algorithms safely inside Sybase IQ, data scientists can gain insights in
unparalleled time. And with fast, accurate insights enterprises can quickly make the
decisions
• Sybase IQ supports a DBLytix library from Fuzzy Logix containing hundreds of advanced
analytic, statistical and data mining algorithms that can run inside Sybase IQ.
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HANA – How it works
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SAP HANA
1.
2.
3.
Real Time is anything that is too fast for your current ETL (Kimball)
Requires integration of data and events from operational processes/systems in real-time
Is a Just-In-Time Information Infrastructure providing real-time insights into operational events
An adaptive enterprise with the ability to manage more effectively and optimize daily business
activities by integrating operational processes
9
10
11
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Sybase – How it works
In-Database Analytics – Sybase IQ
•
Sybase IQ is a highly optimized
analytics server, designed specifically to
deliver
•
ultra-high-speed business intelligence
and reporting on standard hardware and
•
operating systems.
•
Unlike traditional databases, Sybase IQ
is architected for analytics—not
transactions—with a column-based
structure.
•
Sybase IQ provides a reduction in disk
and CPU requirements (by reducing I/O
bottlenecks) compared to traditional
row-based RDBMS systems that have
to be retro-fitted to support Data
Warehousing and Analytics.
14
* Source: Sybase IQ Technical Overview
15
* Source: Sybase IQ Technical Overview
16
* Source: Sybase IQ Technical Overview
HANA and Sybase IQ
compared
Compare HANA and Sybase IQ
HANA
• Business Analytics in-memory computing technology
that comprises the SAP Business Analytic Engine (BAE)
— an in-memory columnar data store with compression
technology, and library of statistical and data mining
algorithms via native support to ‘R’ functions in HANA,
combined with optimized hardware from various
partners.
• For High performance-low latency
Sybase IQ
• Business Analytics in-database computing technology
that comprises of shared everything, MPP , columnar
data store and analytics DBMS engine to analyze
structured, semi-structured, unstructured data with
native MapReduce API, comprehensive and flexible
Hadoop Integration, PMML Support and an expanded
library of statistical and data mining algorithms that
leverage the power of distributed query processing
across Massively Parallel Processing Grid.
• High performance without latency issues
BEx
BI tools
MDX
Analytics
Engine
BO BI Tools
BI Tools
BICS
MDX
Calculation
Engine
In-memory
Engine
Datawarehousing
Platform
DS**
BW
RTDS*
HANA
Source Systems
Source Systems
Source Systems (OLTP, DW, 3rd)
Data Flow
*Images Source: SAP
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Read
Compare HANA and Sybase (cont.)
HANA
Sybase IQ
Technical Use Cases
Technical Use Cases
• Real time operations analysis
• Historical Analysis
• Rapid creation of analytic models without impacting
established BI environment
• Predictive Analysis
• Mash up of data from multiple sources
• Unstructured Data Processing
• Enabled for all BO BI Tools (“Aurora” BO 4.0)
• Enabled for ‘Big Data’ Analytics
Business Use Cases
Business Use Cases
• Point of Sales
• Operational Continuity
• Demand Signal Repository
• Demand Forecasting
• Market Measurements Analysis
• Customer Churn Analysis
• Traffic Analysis
• Operational Intelligence
• Liquidity Risk Management
• Point of Sales
• Situational Awareness
• Market Measurement Analysis
• Operational Intelligence
• Real-Time Business Intelligence
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• Text Analytics
Utilities Case Study
HANA in action
Real-Time Enterprise
Convergence of Robust Technologies in-memory
Business
Events
Business
Processes
Robust business processes
and business rules
management
Powerful event
processing technology
Business
Intelligence
State of the art analytics
HANA In-Memory
SAP Event Insight
SAP Business Process Management
SAP Business Rules Management
SAP BOBJ Xcelsius
SAP BOBJ Crystal Reports
Examples
Examples
Examples
•
•
•
•
•
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Purchase orders
Complaint logs
News feeds
Temperature readings
Meter data
•
•
•
•
Automated BPM&BRM response
Process steps with real-time reports
Instant notifications
Approval Process
• Email and RSS Feeds
notifications
• Real-time Dashboards
• Integration with decision
support and historical data
Real Time Enterprise Architecture Slide
22
Outage Scenario – Solution Workflow
Mike
Control Center Operator
Action
Dist. SCADA Alert
indicating outage
Mike is alerted with an
outage event from
SCADA systems
1> Power Outage
Detected Power to be
restored to minimize
cost. Event correlation
suggests transformer
overheat problem
Decision
Meters are tending to
RED.
Mike is alerted with the
list of “Zero Read
Meters” indicating
outage
1> Dispatch Work Force
with appropriate
equipment and skillset
2> Communication
Outage Detected No
Power Outage
Detected but network
outage detected
Decision
Event
Insight
Insight
Trouble Ticket Volume
Mike is alerted with
increasing volume of
customer trouble tickets
in CRM system
2>Since no power
outage is detected,
notify concerned dept
to repair comms
network problems
1> Mike selects the ‘Create
Service Notification’ option
which initiates the process in
ERP (via Workforce Mgmt
System)
2> Mike sends an Alert
Notification to comms team
reporting problem with
communication network
Create Service
Notification
Dispatch
Outage
Service Order
Closed
Email Notification
Sent with Context
Details
Bottom Line: Real-time insight, context data, recommended decision steps and linkage into exception process handling all
work23hand in hand to resolve outage and minimize the cost and impact of outage and reduce restoration time.
Supply Chain Case Study
HANA in action
Scenario # 2
Perfect Order Visibility, Insight & Action
Action
1> ‘High’ priority VMI
order She selects this
in danger of missing
SLA, root-cause
analysis suggests
carrier breakdown
Decision
The Perfect Order Metric
is tending to Red.
Ann Smith is alerted
with the list of “Problem
Orders” in danger of
SLA non-compliance
1>Rush Order Delivery
with an alternate carrier
recommended
2> ‘Medium’ priority
order She selects this
in danger of missing
SLA, trend analysis
suggests ‘inclement
weather’
Decision
Event
Insight
Insight
Ann Smith
Sales Operations
Manager
2>Since no delivery as
been yet initiated, notify
delivery to change
mode of transport from
‘Sea’ to ‘Air’ Freight
1>Ann Smith selects the ‘Rush
Order Delivery’ option which
initiates the process in ERP (via
BPM)
2>Ann Smith sends an Alert
Notification to delivery
recommending the change in
mode of transport
Create rush
Delivery w new
carrier
Pick
Pack
Ship
Billing
& AR
POD
Email Notification
Sent to Delivery
with Context
Details
Bottom Line: Real-time insight, context data, recommended decision steps and linkage into exception process handling all
work25hand in hand to resolve perfect order inefficiency and meet customer SLA
Appendix
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31
Thank you,
Chinni Ranganath
732.325.5155
[email protected]
© 2011 Deloitte Touche Tohmatsu Limited
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