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Netezza integration with SAS software

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  • Netezza has created an extremely flexible analytics platform that offers orders of magnitude performance at petascale. The integrated, easy-to-use appliance dramatically accelerates the entire analytics process. The programming interfaces and parallelization primitives offered make it straightforward to move a majority of analytics inside appliance, regardless of whether they are being performed in SAS and R or written in Java, Python or Fortran. By bringing analytics to the data, modelers and quants teams can operate on the data directly inside the appliance instead of having to move it to a different location and dealing with the associated data pre-processing and transformation. More importantly, modelers can take full advantage of the MPP architecture to ask the most complex questions on all the enterprise data, without the infrastructure coming in the way. They can iterate through different models more quickly to find the best fit. Administrators can easily create marts and sandboxes inside the appliance to allow modelers to work on their analytics problems without disrupting normal business operations. Data stays within a central repository instead of getting distributed all over the organization.Once the model is developed, it is seamless to put it into prediction mode. The prediction and scoring can be done right where the data resides, inline with other processing, on an as-needed basis. Users can get the results of prediction scores in near real-time, helping operationalize advanced analytics and making it available throughout the enterprise.
  • One of the biggest obstacles to build a truly Analytic Enterprise is the technology infrastructure. As user and data volumes grow and the questions organizations ask of the data become much more sophisticated, the technology infrastructure to support this need must evolve as well. Today, organizations have to make a choice between data volume and analytic complexity. Most analytics performed on large data is relatively simple. The infrastructure, based on traditional database technology, easily gets overextended just keeping up with the growth in user and data volumes, leaving no room to accommodate the increasing analytic complexity.On the other hand, most complex analytics is done on small data sets by groups of specialized users on underpowered systems. The analytic complexity overpowers the weak infrastructure, forcing users to limit the amount of data they operate on. Even with relatively small data volumes, users have to resort to all kinds of machinations to get the insights they want from the data. In fact quants and modelers spend 80% of their time preparing and cleaning data rather than doing actual modeling tasks. With Netezza, organizations no longer have to make a choice between Big Data and Big Math. Netezza’s business since day one has been all about removing bottlenecks from the infrastructure to allow analytics without any constraints delivering powerful business insight. We have now created the scalable analytics infrastructure for customers to drive towards truly massive data volumes, with large numbers of users, asking questions of that data that could not even be contemplated on other architectures.
  • SAS Scoring AcceleratorCannot be used for all models that have been created within Enterprise Miner. Each customers models need to be evaluated before selling the SAS Scoring Accelerator. Information on what would or would not be appropriate is documented: http://support.sas.com/documentation/cdl/en/scraccltdug/62161/PDF/default/scraccltdug.pdf (page 15).
  • Transcript

    • 1. © 2010 Netezza, Inc. All rights reserved
      Analytics Without Constraints
      Netezza Integration with SAS Software
    • 2. Data Analysis before Netezza
    • 3. Where were we before Netezza?
      • Extremely long processing cycles for Data analysis – long waits…
      • 4. Working with small datasets (Subsets) – very restrictive
      • 5. Analytic Models restricted to tests against small datasets
      • 6. Too much data is being moved between servers
      • 7. Data Models run for long time periods against full data base – processing of other jobs impacted severely
      • 8. Data Models sometimes re-coded in C/C++ for better performance
      • 9. Productivity of the Data Analysts is low
    • SAS Software and Netezza DWH Appliance
      Page 4
      SAS
      SAS for Fraud Detection
      SQL
      SAS/Access to Netezza
      SAS for Customer Scoring
      SAS
      SAS Score Code
      Confidential Briefing for Customer
    • 10. Big Data Meets Big Math – Netezza + SAS Software
      Page 5
      Analytics Without Constraints
      Confidential Briefing for Customer
    • 11. SAS Support on Netezza for Analytics Processing
      Page 6
    • 12. Netezza Enabled with SAS Software
      Page 7
      Data Extraction
      Database Connector
      In-Database Analytics
      Pulled from
      Pushed down
      Pulled from
      Scoring
      Algorithms & Transforms
      Analyst
      Analyst
      Scoring
      Algorithms & Transforms
      Analyst
      011011010010100101110011011010010100101110
      01101101001010 LOTS of DATA Movement
      01101101000101110000101110
      Less Data
      AND Faster
      Process
      Automation
      DW Developer
      More Transforms
      In-Database
      DW Developer
      Scoring Algorithms & Transforms
      Published to Database as Scoring Processes
      Recoded Scoring Processes
      Recoded Scoring Processes
      011011010010100101110011011010010100101110011011010010100110110100101001011100110110010100101110011011010010100110110100101001011100110110100101001011100110110100101001101101001010010111001101101001010010111001101101001010011011010010100101110011010100101001011100110110100101011111000
      Data Warehouse
    • 13. Netezza and SAS Connected
      Page 8
      Data Extraction
      Database Connector
      In-Database Analytics
      Data Extraction
      Data Extraction
      Data Extraction
      Analytics Server
      • Base SAS – DATA STEP
      • 14. Base SAS – PROC SQL
      • 15. SAS/Access to ODBC
      • 16. Base SAS – Proc SQL
      • 17. SQL Pass-Thru Option
      • 18. SAS/Access to Netezza
      • 19. SAS Enterprise Miner
      • 20. SAS Scoring Accelerator for Netezza
      ODBC
      FAST
      FASTER
      Data Extraction
      Data Extraction
      Data Extraction
      Data Warehouse
      • ODBC
      • 21. Netezza Database
      • 22. SAS/Access to Netezza
      • 23. Netezza Database
      • 24. SAS Scoring Accelerator for Netezza
      • 25. Netezza Database
      • 26. In-Database Analytics
    • Netezza in the SAS Ecosystem
      Open standards platform to enable actionable intelligence “out of the box”
      Business Analytics Applications
      NetezzaPerformance Server
      Enterprise Applications
      Transactional Databases
      Users
      ETL – SAS DI Studio, or other
      CRM
      Billing
      Employees
      ERP
      RFID
      Customers
      Suppliers
      HR
      SCM
      NPS
      External Source Systems
      Representative vendors
      Supplier Data
      E-Commerce
      Key Advantages
      9
    • 29. Catalina Marketing Case Study
      Page 10
    • 30. Value Proposition
      Supercharge SAS with Netezza’s high performance, scalable scoring
      Effective resource utilization via automatic code generation
      Reduced end-to-end processing time
      Reduced time-to-model implementation
      Simplified infrastructure to maintain and administer
      SAS Enterprise Miner model development process is simple and easy to manage
      Leverage existing SAS knowledge and Netezza high-performance
      Increase Analyst Productivity
      Score Database more frequently for better results
      Page 11
    • 31. Model Scoring In-Database
      SAS Scoring Accelerator for Netezza
      Page 12
    • 32. SAS® In-Database
      Traditional Architecture In-Database Architecture
      Analytic Modeling
      Analytic Modeling
      SAS Scoring
      Data
      Preparation
      Data
      Preparation
      SAS Scoring
      SAS Modeling
      SAS C & PMML Scoring
      Data Preparation
      NetezzaTwinFin™
      NetezzaTwinFin™
    • 33. SAS® Scoring Accelerator for Netezza1.6
      BIClient
      SAS Enterprise Miner
      SAS 9.2
      Model
      NZ TwinFin™
      Export
      SAS Model ManagerorSAS publishing agent
      sas_score()
      Score asDATA Stepcode
      SASFormatLibrary
      SASDATA StepEngine
      ScoreDefinitions
      FormatDefinitions
      Publishing Macro
    • 34. Benefits
      Achieve higher model-scoring performance and faster time to results
      Improve accuracy and effectiveness of analytic models
      Reduce data movement and latency
      Eliminate model score code rewrite and model re-validation efforts (i.e. labor costs and error prone)
      Consolidate data to improve regulatory compliance
      Better manage, provision and govern data