Netezza integration with SAS software
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Netezza integration with SAS software
- 1. © 2010 Netezza, Inc. All rights reserved Analytics Without Constraints Netezza Integration with SAS Software
- 2. Data Analysis before Netezza
- 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
- 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
- 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
- 21. Netezza Database
- 22. SAS/Access to Netezza
- 23. Netezza Database
- 24. SAS Scoring Accelerator for Netezza
- 25. Netezza Database
- 27. Scalability
- 28. Low TCO9
- 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
Hinweis der Redaktion
- 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).