Poslovni slučajevi upotrebe IBM Netezze

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1 Poslovni slučajevi upotrebe IBM Netezze data at the Speed and with Simplicity businesses need 25. ožujak

2 Agenda A. IBM PureData for Analytics Netezza B. Scenarij 1.: Novi Data Mart / DWH C. Scenarij 2.: Podrška naprednoj / unaprijeđenoj analitici D. Scenarij 3.: Tradicionalna DWH platforma više nije optimalna E. Zašto Netezza?

3 Agenda A. IBM PureData for Analytics Netezza B. Scenarij 1.: Novi Data Mart / DWH C. Scenarij 2.: Podrška naprednoj / unaprijeđenoj analitici D. Scenarij 3.: Tradicionalna DWH platforma više nije optimalna E. Zašto Netezza?

4 Traditional Data Warehouses are just too complex They do NOT to meet the demands of analytics, such as advanced analytics on big data. Too complex an infrastructure Too complicated to deploy Too much tuning required Too long to get answers Too inefficient at analytics Too many people needed to maintain Too costly to operate 4

5 DWH Appliances make it simple transforming the user experience. Dedicated device Optimized for purpose Complete solution Fast installation Very easy operation Standard interfaces Low cost

6 Netezza IBM PureData System for Analytics The simple data warehouse appliance for serious analytics System for Analytics Purpose-built analytics engine Integrated database, server and storage Standard interfaces Low total cost of ownership What makes it different? Speed x faster than traditional custom systems 1 Simplicity minimal administration and tuning Scalability Petabyte+ scale user data capacity Smart high performance, advanced analytics 1 Based on IBM customers' reported results. "Traditional custom systems" refers to systems that are not professionally pre-built, pre-tested and optimized. Individual results may vary.

7 Evolution of Netezza & PureData System for Analytics World s First appliance with no cost encryption World s Fastest and Greenest Analytical Appliance PureData System for Analytics N200x PureData System for Analytics N300x World s First Analytic Data Warehouse Appliance World s First Petabyte Data Warehouse Appliance TwinFin TwinFin with i- Class Advanced Analytics World s First 100 TB Data Warehouse Appliance World s First Data Warehouse Appliance NPS 8000 Series NPS Series

8 IBM PureData System for Analytics N3001 Changing the game for data warehouse appliances Big Data and Business Intelligence ready with capabilities to unlock data s true potential Advanced security in an insecure world at no extra cost An even broader family of appliance models to fit a broad range of data capacity needs

9 Agenda A. IBM PureData for Analytics Netezza B. Scenarij 1.: Novi Data Mart / DWH C. Scenarij 2.: Podrška naprednoj / unaprijeđenoj analitici D. Scenarij 3.: Tradicionalna DWH platforma više nije optimalna E. Zašto Netezza?

10 New Data Mart or DWH: traditional way vs PDA way Traditional way System install (unpack HW, install SW, test SW, design and create partitions, create tables) - time depends Data modeling: create tables, choose indexes, choose compression, choose distribution, load data, run queries, get statistics, tune indexes, enjoy tuned system before next ad hoc query comes get statistics, tune indexes, create views, enjoy tuned system PDA way System install (unpack appliance, run self diagnostics, create tables) ready for data on day 2 Data modeling: create tables, run queries, enjoy performance run more queries, enjoy performance

11 Introducing PureData System for Analytics N Simple Same user experience as all PureData System for Analytics appliances Full function Netezza Platform Software with IBM Netezza Analytics Support tools and Netezza Performance Portal ODBC/JDBC/OLE-DB/SQL Driver integration Load and go with no tuning or administration Speed x faster than traditional custom systems 1 Smart Rich set of in database analytic functions Protection of all data from unauthorized access Advanced security with self-encrypting drives, Kerberos support Includes starter kits for Big Data and Business Intelligence Agile Easily incorporated into the data center with simplified installation into an existing rack Affordable Purchase or lease 1 Based on IBM customers reported results. Traditional custom systems refers to systems that are not professionally pre-built, pre-tested and optimized. Individual results may vary.

12 PureData System for Analytics N The mini appliance Solution Highlights Rack mountable appliance Same ease of use and feature functions as larger appliances Full function, production ready Up to 16TB 1 of user data Load and go with no tuning or administration Highly available Full redundancy Rich set of in database analytic functions Remote access for support 1 Assuming 4X compression

13 Big Data and Business Intelligence Ready Unlocking Data s True Potential Included with the PureData System for Analytics N3001 Data Warehouse Appliance Cognos: Business Intelligence Up to 16TB capacity for your Data Warehouse or Data Mart with built-in in-database analytic capability Built-in, In-Database analytic capability and integration with a variety of 3 rd party tools Exceptional value provided DataStage: Data Integration & Transformation BigInsights: Hadoop Data Services InfoSphere Streams: Real-time Analytics For additional value Industry Process & Data Models Models for Banking, Financial Markets, Healthcare, Insurance, Retail, Telco IBM InfoSphere Data Privacy and Security for Data Warehousing 2014 IBM Corporation

14 Primjer reference Optimizing customer s experience using IBM PureData System for Analytics

15 Agenda A. IBM PureData for Analytics Netezza B. Scenarij 1.: Novi Data Mart / DWH C. Scenarij 2.: Podrška naprednoj / unaprijeđenoj analitici D. Scenarij 3.: Tradicionalna DWH platforma više nije optimalna E. Zašto Netezza?

16 Analytics Beyond Reporting Optimization Predictive Analytics BI Reporting and Ad-Hoc Analysis What is the best choice? What happened? When and where? How much? What will happen? What will the impact be? IBM Corporation

17 Advanced Analytics the Traditional Way SPSS Data Warehouse Analytics Grid Data SQL ETL Demand Forecasting ETL R, S+ ETL SQL C/C++, Java, Python, Fortran, Fraud Detection SQL

18 Advanced Analytics with the PureData System for Analytics SPSS Data Warehouse Analytics Grid Data SQL ETL Demand Forecasting ETL R, S+ ETL SQL C/C++, Java, Python, Fortran, Fraud Detection SQL

19 Advanced Analytics with the PureData System for Analytics SPSS Demand Forecasting SQL R, S+ Fraud Detection

20 IBM Netezza Analytics In-database analytics for every role in your enterprise Included Use cases Reduce hospital admissions or personalize disease treatments Achieve an order of magnitude improvement in manufacturing quality Better understand the risk of catastrophic events and many more Bring the analytics to the data not the data to the analytics Data Preparation Features Built-in, in-database analytic functions Data mining, prediction, transformations, statistics, geospatial, data preparation Full integration with tools for BI & visualization IBM Cognos, Microstrategy, Business Objects, SAS, MS Excel, SSRS, Kognitio, Qlikview Full integration with tools for model building & scoring IBM SPSS, SAS, Open Source R, Fuzzy Logix Full integration with tools for BI & visualization R, Java, C, C++, Python, LUA Predictive Analytics Geospatial Analytics Advanced Statistics

21 Business Intelligence The power of IBM Cognos with PureData for Analytics Use cases Reporting, analysis, scorecards, dashboards Data visualization Mobile business intelligence and many others Included Rapid deployment of answers to key business questions Features Optimized for PureData for Analytics Offers high performing OLAP over relational experience Cognos Dynamic Query Mode extends benefits of PureData by adding in-memory & caching on top of already fast appliance performance Exploits Netezza analytic in-database functions Included with PureData for Analytics: IBM Cognos Business Intelligence Analytics User licenses, 1 Analytics Administrator license 1 1 PureData System for Analytics N3001 must be the data source for Cognos.

22 Real-Time Analytics Included capability from IBM InfoSphere Streams Included Fraud detection Use cases Predict customer churn Telco real-time mediation and analysis Real-time monitoring of medical sensors to improve healthcare outcomes Defect detection in manufacturing Traffic pattern analysis and management Deploy analytic models on data-in-motion to enable real-time decisions and land data in the warehouse to build the analytic models Features Analyze data in motion Provides sub-millisecond response times, allowing you to view information and events as they unfold Analyze all kinds of data: simple & advanced text, geospatial, acoustics, images, video, sensors Eclipse-based development environment Included with PureData for Analytics: InfoSphere Streams Developer Edition developer users, non-production licenses

23 Data Integration & Transformation InfoSphere DataStage, Designer Client and Data Click Use cases Integration, transform and deliver trustworthy information to your data warehouse Analysts, data scientists or even line-of-business users can easily retrieve data and populate the PureData System for Analytics Move data from the data warehouse into a subject area data mart Ease of Use Features - Provides an easy-to-use, top-down, work-as-youthink design interface that enables users to design once and deploy anywhere batch or real time; extract, transform, load (ETL); or extract, load, transform (ELT) - Self-service data integration to enhance business agility Accelerate time to value - Includes a comprehensive library of transformation components for easily defining common integration processes 1 PureData System for Analytics N3001 must be the source or target database. Included Rich capabilities for data integration Included with PureData for Analytics: IBM InfoSphere DataStage (280 PVU), Designer Client (2 concurrent users), InfoSphere Data Click 1

24 Hadoop Data Services Included capability with IBM InfoSphere BigInsights Use cases Federated SQL access across Hadoop and your PureData System for Analytics Pre-processing and landing zone for all data types prior to loading to data warehouse Queryable backup for cold data Features Included Bringing the power of Hadoop to your enterprise Big data analytical platform - Best of open source + IBM technologies - Big SQL - High performance SQL access of Hadoop - Federation across many data sources - combine information from Hadoop and PureData for Analytics - BigSheets visualization tool Built-in analytics - Text analytics, Big R Included with PureData for Analytics: InfoSphere BigInsights 3.0 software licenses for 5 enterprise nodes to manage up to ~100 TB of Hadoop data 1 1 Based on 4 data nodes + 1 master node. 12 TB uncompressed per data node with 4 TB drives. 12 TB x 4 nodes = 48 TB uncompressed. Using 2-2.5x compression yields TB compressed data. Capacity will depend on hardware configuration selected.

25 Primjer reference - eharmony attracts new members by understanding behavior and fine-tuning matching algorithm

26 Agenda A. IBM PureData for Analytics Netezza B. Scenarij 1.: Novi Data Mart / DWH C. Scenarij 2.: Podrška naprednoj / unaprijeđenoj analitici D. Scenarij 3.: Tradicionalna DWH platforma više nije optimalna E. Zašto Netezza?

27 Data Warehouse Modernization: typical architecture Structured data for analysis BI & Reporting Structured data Analytical Warehouse Predictive Analytics Visualization & Discovery Custom Applications

28 Data Warehouse Modernization: new challenges Much more data and new data types Structured data More structured data Non-structured data Analytical Warehouse BI & Reporting Predictive Analytics Visualization & Discovery Custom Applications => From 10x TB to 100x TB.. Social media, logs..

29 DWH Modernization: traditional approach Adding HW+SW to DWH is expensive, value of non-structured data is unclear Structured data More structured data BI & Reporting Predictive Analytics Analytical Warehouse Visualization & Discovery Transformation to structured data Custom Applications Non-structured data => Huge amounts of cold data.. Still needed sometimes Integration of unstructured data is complex and expensive..

30 Data Warehouse Modernization: Next Generation Enterprise Data Warehouse Architecture Structured data More structured data Non-structured data Analytical Warehouse Structured data archive Non-structured data BI & Reporting Predictive Analytics Visualization & Discovery Custom Applications Hadoop Platform => Price effective.. All types of data..

31 New analytical applications that were previously difficult or impossible due to scale Recommendation Engines: Retailers and Web services use Hadoop to match customers to products and services based on their user profile and behavioral data Sentiment Analysis: Hadoop combines with text analytics tools to interpret social media and social networking posts to determine user perception of particular companies, brands or products Risk Modeling: Financial services companies use Hadoop to analyze large volumes of transactional data to determine risk and exposure of financial assets, to develop what-if scenarios. and to score customers for credit risk Fraud Detection: Customer behavior can be combined with historical and transactional data to detect fraudulent payment activity or other anomalies Customer Churn Analysis: Customer behavior data is used to identify patterns that indicate which customers are most likely to leave for a competing vendor or service, enabling action to be taken to retain the most profitable customers Customer Experience Analytics: Hadoop can integrate data from previously siloed customer interaction channels such as call centers and e-commerce sites to enable a complete view of the customer Network Monitoring: Hadoop is used to ingest, analyze and display data collected from servers, storage devices and other IT hardware to allow administrators to monitor network activity and diagnose bottlenecks Research And Development: Pharmaceutical manufacturers use Hadoop to explore enormous volumes of text-based research and other historical data to assist in the development of new products Asset management: industrial customers use Hadoop to carry out predictive maintenance, preventing asset/product failure

32 DWH Upgrade: traditional way vs PDA way Traditional way Buy more HW and SW licenses or Buy traditional way on steroids and Keep tuning PDA way Choose critical data marts and try appliance simplicity + performance and Enjoy then Choose co-existence or migration strategy

33 Real life experience with upgrades and migrations Take table creation scripts and create tables in PDA No indexes, no compression, no data partitioning! Do simple 1:1 ETL, load data and run the same queries Remember time from creating tables to running queries, in real life you will need to add ONE day for unpacking PDA and running self-diagnostics Compare query times: it s out of the box PDA vs current tuned system Create and run some ad hoc queries on your data in PDA and then run it on current system Compare query times: it s out of the box PDA vs out of the box current system

34 Real life experience examples General Motors Reduce / contain warehouse cost of existing DWH, ANSI SQL compatibility with existing DWH, run ETL jobs unchanged Seagate Reduce / contain cost of existing DWH, ANSI SQL compatibility with existing DWH, run SQL jobs on existing DWH with little or no change on BigSQL, execute reports with BI platform unchanged BNSF Reduce / contain cost of existing DWH, ANSI SQL compatibility with existing DWH, ensure that nearly 10,000 ETL jobs on existing DWH can move with little or no change on BigSQL

35 Agenda A. IBM PureData for Analytics Netezza B. Scenarij 1.: Novi Data Mart / DWH C. Scenarij 2.: Podrška naprednoj / unaprijeđenoj analitici D. Scenarij 3.: Tradicionalna DWH platforma više nije optimalna E. Zašto Netezza?

36 Tactical CIO s focus shifted from managing the data to value extraction from data Combining internal and external data for better insights Customer analytics drive big data initiatives 96 % more 52 % more 25 % Underperformers 49 % Outperformers 27 % Underperformers 41 % Outperformers CIOs in outperforming enterprises are focusing particularly heavily on developing the resources to acquire deeper customer insights

37 Tactical CIO s focus shifted from managing the data to value extraction from data

38 Operational Demands of a Modern Data Warehouse? Insight Cost Agility Faster Insight Fast response times are expected People are used to an experience as easy as Google Users do not want to wait for query results Lower cost Initial acquisition Ongoing operation and administration Total cost of ownership Added Agility Ability to respond quickly to the needs of the business By simplifying operations, more time is provided for innovation Better business outcomes by utilizing more data sources

39 PureData System for Analytics Family N2002 N3001-xxx DB2 Analytics Accelerator for z/os (now with N3001) N x faster than custom systems 1 3.3x faster I/O scan rate 2 Load and go, no tuning Designed to run complex analytics in minutes, not hours Rich set of in-database analytics...plus Entitled software capability for real-time analytics, Hadoop data services, data movement and business intelligence Advanced security Partial rack to 8-rack configurations plus Rack mountable appliance Ideal for small and medium business with up to 16 TB of user data The hybrid computing platform integrating Netezza technology with zenterprise technology Supports transaction processing and analytic workloads concurrently, efficiently & cost effectively Accelerates complex queries, up to 2000x faster Required security compliance with Data-at-Rest Encryption 1 Based on IBM customers' reported results. "Traditional custom systems" refers to systems that are not professionally pre-built, pre-tested and optimized. Individual results may vary. 2 Comparing N1001 scan rate of 145 TB/hour to N2002 scan rate of 478 TB/hour

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