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).
  • Netezza integration with SAS software

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

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