Chicago Business Objects User Group HANA Vs Sybase

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 Par. Accels 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. BIG DATA 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 : 1. Supporting large volumes of data such as petabytes and Exabyte. Oracle Exalytics SAP HANA HP Vertica Hadoop Cassandra 5. Sybase IQ 4. EMC Greenplum 3. Supporting Massive Parallel Processing (MPP) , i. e. , potentially distributed across thousands of heterogeneous processors Supporting Not Only Structured Query Language (NO SQL) but also for unstructured data Supporting automatic parallelization such as query optimization, queries across segment servers Linear scalability to linearly scale compute performance IBM Netezza Appliance 2. IN-MEMORY IN-DATABASE 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 3 rd 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. 6

HANA – How it works

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SAP HANA 1. Real Time is anything that is too fast for your current ETL (Kimball) 2. Requires integration of data and events from operational processes/systems in real-time 3. 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

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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 14 and Analytics. * 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 BEx BI tools MDX Analytics Engine BO BI Tools BICS MDX Calculation Engine In-memory Engine Datawarehousing Platform DS** BW RTDS* HANA Source Systems (OLTP, DW, 3 rd) Data Flow *Images Source: SAP 18 Read 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 Map. Reduce 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

Compare HANA and Sybase (cont. ) HANA Sybase IQ Technical Use Cases • Real time operations analysis • Rapid creation of analytic models without impacting established BI environment • Mash up of data from multiple sources • Enabled for all BO BI Tools (“Aurora” BO 4. 0) • • • Business Use Cases • • • • 19 Point of Sales Demand Signal Repository Market Measurements Analysis Traffic Analysis Liquidity Risk Management Situational Awareness Operational Intelligence Real-Time Business Intelligence Historical Analysis Predictive Analysis Text Analytics Unstructured Data Processing Enabled for ‘Big Data’ Analytics Operational Continuity Demand Forecasting Customer Churn Analysis Operational Intelligence Point of Sales Market Measurement Analysis

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 • • • 21 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 BW BRM ERP BW 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 2> Communication Outage Detected No Power Outage Detected but network outage detected 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 Decision Event Insight 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 Decision 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 Create Service Notification Email Notification Sent with Context Details Dispatch Outage BPM BRM Service Order Closed ERP Bottom Line: Real-time insight, context data, recommended decision steps and linkage into exception process handling all work 23 hand 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 1> ‘High’ priority VMI order She selects this in danger of missing SLA, root-cause analysis suggests carrier breakdown BW Tex t An ERP Action 2> ‘Medium’ priority order She selects this in danger of missing SLA, trend analysis suggests ‘inclement weather’ 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 Decision Event Insight 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 Decision 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 Create rush Delivery w new carrier Email Notification Sent to Delivery with Context Details Pick Pack BPM Ship BRM Billing & AR POD ERP aly tics Bottom Line: Real-time insight, context data, recommended decision steps and linkage into exception process handling all work 25 hand in hand to resolve perfect order inefficiency and meet customer SLA

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Thank you, Chinni Ranganath 732. 325. 5155 cchinni@deloitte. com

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