SAS is a partner that is dedicated to helping companies grow. It has over 50,000 customers globally including 97 of the Fortune 100 companies. SAS is committed to advanced business analytics and has been for over 35 years. With big data and advanced analytics, SAS provides high performance analytics solutions that allow previously impossible services and applications to become possible. SAS fully utilizes parallel processing resources to perform efficient advanced analytics, allowing analysts to solve more complex business problems and integrate results into decision making.
The document discusses SAS High-Performance Analytics, a product that leverages in-memory architecture through a dedicated software and hardware appliance to drive high-performance analytics. It highlights how the product addresses the entire analytical lifecycle from data exploration to model deployment to achieve insights at breakthrough speed. Key differentiators of the product include being the only in-memory offering that can develop and deploy high-end analytics models.
This document discusses big data and analytics. It notes that big data refers to large volumes of both structured and unstructured data that exceed typical storage and processing capacities. Key considerations for big data and analytics include data, analytics techniques, and platforms. Trends include growth in data size and velocity, declining storage costs, and multicore processors. Common challenges in analytics involve flexible models, powerful algorithms, and effective visualization to solve large, complex business problems. The document promotes SAS's high-performance analytics approach.
SAS Modernization architectures - Big Data AnalyticsDeepak Ramanathan
The document discusses strategies and technologies for scalable analytics using modern data architectures like Hadoop. It describes how declining storage costs and increasing CPU speeds have enabled organizations to leverage huge amounts of data through platforms like Hadoop. The document also summarizes SAS's big data strategy, how its technologies integrate with Hadoop, and how organizations can use SAS solutions to extract insights from data through the entire analytics lifecycle including data preparation, modeling, visualization and more.
SAS aster data big data dc presentation publicTeradata Aster
This document discusses SAS In-Database, which allows SAS functions, models, and code to run directly inside databases. Key points include:
- SAS In-Database aims to streamline analytics workflows, improve performance, and ensure data consistency.
- It works by embedding SAS capabilities like scoring functions, modeling, and data preparation directly into databases.
- SAS has partnered with Aster Data to enable these in-database analytics using Aster's nCluster platform.
Here are the key steps to enable ad-hoc reporting with relational data using a BusinessObjects universe:
1. Create a BusinessObjects universe that defines the logical data model and relationships between tables/views in the underlying database (e.g. SAP Sybase IQ).
2. Map the universe objects (e.g. tables, columns) to the physical database tables and columns.
3. Define joins, filters and calculations in the universe that simplify querying for business users.
4. Deploy the universe to the BI platform (e.g. SAP BusinessObjects BI 4.1).
5. Business users can then access the universe objects through BI tools like Web Intelligence, Crystal Reports to
Big Data Analytics in a Heterogeneous World - Joydeep Das of SybaseBigDataCloud
This document discusses big data analytics in a heterogeneous world. It covers the variety of solutions available for big data analytics including changes in hardware, software, execution characteristics, and results. It also discusses building bridges across heterogeneous systems through comprehensive frameworks, reliable data management, versatile application services, and rich ecosystems.
This document discusses the benefits of Oracle Essbase for business analysis. It highlights that Essbase provides (1) a rich user experience through integration with Microsoft Office and advanced reporting and visualization tools, (2) a highly advanced calculation engine with over 350 functions for financial, time, and custom calculations, and (3) a flexible and optimized environment for custom analytic applications and modeling across data sources at enterprise scale. Essbase allows analysts to have "speed of thought" conversations with data and gain insights to make better decisions.
Supply Chain Council Presentation For Indianapolis 2 March 2012Arnold Mark Wells
This document discusses how advanced analytics can be leveraged to improve supply chain performance when used in conjunction with the SCOR model. It provides examples of how analytics can be applied to optimize metrics like perfect order fulfillment, upside flexibility, and return on working capital by factoring in various business decisions. The SCOR model provides best practices and metrics, while analytics can help determine the best ways to achieve goals and measure performance.
The document discusses SAS High-Performance Analytics, a product that leverages in-memory architecture through a dedicated software and hardware appliance to drive high-performance analytics. It highlights how the product addresses the entire analytical lifecycle from data exploration to model deployment to achieve insights at breakthrough speed. Key differentiators of the product include being the only in-memory offering that can develop and deploy high-end analytics models.
This document discusses big data and analytics. It notes that big data refers to large volumes of both structured and unstructured data that exceed typical storage and processing capacities. Key considerations for big data and analytics include data, analytics techniques, and platforms. Trends include growth in data size and velocity, declining storage costs, and multicore processors. Common challenges in analytics involve flexible models, powerful algorithms, and effective visualization to solve large, complex business problems. The document promotes SAS's high-performance analytics approach.
SAS Modernization architectures - Big Data AnalyticsDeepak Ramanathan
The document discusses strategies and technologies for scalable analytics using modern data architectures like Hadoop. It describes how declining storage costs and increasing CPU speeds have enabled organizations to leverage huge amounts of data through platforms like Hadoop. The document also summarizes SAS's big data strategy, how its technologies integrate with Hadoop, and how organizations can use SAS solutions to extract insights from data through the entire analytics lifecycle including data preparation, modeling, visualization and more.
SAS aster data big data dc presentation publicTeradata Aster
This document discusses SAS In-Database, which allows SAS functions, models, and code to run directly inside databases. Key points include:
- SAS In-Database aims to streamline analytics workflows, improve performance, and ensure data consistency.
- It works by embedding SAS capabilities like scoring functions, modeling, and data preparation directly into databases.
- SAS has partnered with Aster Data to enable these in-database analytics using Aster's nCluster platform.
Here are the key steps to enable ad-hoc reporting with relational data using a BusinessObjects universe:
1. Create a BusinessObjects universe that defines the logical data model and relationships between tables/views in the underlying database (e.g. SAP Sybase IQ).
2. Map the universe objects (e.g. tables, columns) to the physical database tables and columns.
3. Define joins, filters and calculations in the universe that simplify querying for business users.
4. Deploy the universe to the BI platform (e.g. SAP BusinessObjects BI 4.1).
5. Business users can then access the universe objects through BI tools like Web Intelligence, Crystal Reports to
Big Data Analytics in a Heterogeneous World - Joydeep Das of SybaseBigDataCloud
This document discusses big data analytics in a heterogeneous world. It covers the variety of solutions available for big data analytics including changes in hardware, software, execution characteristics, and results. It also discusses building bridges across heterogeneous systems through comprehensive frameworks, reliable data management, versatile application services, and rich ecosystems.
This document discusses the benefits of Oracle Essbase for business analysis. It highlights that Essbase provides (1) a rich user experience through integration with Microsoft Office and advanced reporting and visualization tools, (2) a highly advanced calculation engine with over 350 functions for financial, time, and custom calculations, and (3) a flexible and optimized environment for custom analytic applications and modeling across data sources at enterprise scale. Essbase allows analysts to have "speed of thought" conversations with data and gain insights to make better decisions.
Supply Chain Council Presentation For Indianapolis 2 March 2012Arnold Mark Wells
This document discusses how advanced analytics can be leveraged to improve supply chain performance when used in conjunction with the SCOR model. It provides examples of how analytics can be applied to optimize metrics like perfect order fulfillment, upside flexibility, and return on working capital by factoring in various business decisions. The SCOR model provides best practices and metrics, while analytics can help determine the best ways to achieve goals and measure performance.
Banks can leverage big data by building analytics platforms to gain insights from large, diverse datasets in real-time. The key is having solutions that can handle high volumes, varieties and velocities of data. SAS provides products to help banks with every stage of the analytics lifecycle from data preparation to predictive modeling to deployment. This allows banks to better assess risks like credit default and improve processes like customer scoring.
'The future of analytics is here', was presented by Mr. Deepak Ramanathan, Head - Information Management, SAS Asia Pacific (North). This document emphasizes on how SAS Visual Analytics empowers business to analyze data in seconds or minutes, which earlier used to take hours or days. This presentation was displayed at CIO - The Year Ahead conference, Mumbai, 29 - 30 Nov'12.
The document discusses embedding business intelligence (BI) capabilities into software applications. It describes 5 levels of embedded BI, ranging from static reporting to advanced analytics. Level 1 involves static reporting using a reporting library, while Level 2 adds interactive reporting capabilities. Level 3 introduces interactive dashboards. Levels 4 and 5 enable self-service reporting, data exploration, and advanced analytics. Embedding BI can help automate processes, generate insights from data, and support better decision making.
An introduction to Jaspersoft 5, a web-based data analysis solution for visualizing relational, OLAP, or Big Data environments such as Hadoop, MongoDB, Google BigQuery, Amazon Redshift, and many other engines
This document provides an overview and agenda for a presentation on exploring data with Jaspersoft. The presentation will include introductions to Jaspersoft, in-memory analysis with a demo, online analytical processing (OLAP) analysis with a demo, and a question and answer section. Jaspersoft is presented as a self-service business intelligence and embeddable reporting and analytics platform that provides solutions for data exploration and OLAP analysis through interactive visualizations and metadata-driven models.
Hadoop World 2011: Big Data Analytics – Data Professionals: The New Enterpris...Cloudera, Inc.
This presentation will explore how Hadoop and Big Data are re-inventing enterprise workflows, and the pivotal role of the Data Analyst. It will examine the changing face of analytics and the streamlining of iterative queries through evolved user interfaces. The speaker will cut through hype around “shorter time to insight” and explain how combining Hadoop and SQL-based analytics help companies discover emergent trends hidden in unstructured data, without having to retrain data miners or restaff. In particular, it will highlight changes to Big Data analysis from this paradigm and illustrate stepwise how analysts can now connect to Big Data platforms, assemble working data sets from disparate sources, analyze and mine that data for actionable insight, publish the results as visualizations and for feeding reporting tools, and operationalize Map-Reduce and Big Data outcomes into company workflows – all without touching the command line.
The document discusses the emergence of big data and new data architectures needed to handle large, diverse datasets. It notes that internet companies built their own data systems like Hadoop to process massive amounts of unstructured data across thousands of servers in a fault-tolerant, scalable way. These systems use a map-reduce programming model and distributed file systems like HDFS to store and process data in a parallel, distributed manner.
This document discusses goals, trends, and complexities of supporting an integrated student data warehouse. It covers project visions and baselines, Oracle Streams concepts for feeding data into the data warehouse, metadata, configuration issues, validation steps, and the importance of securing and monitoring the data stream for quality and compliance. Technical challenges include testing changes and their impact on the different systems.
AGC helps customers gain business intelligence insights from their data by transforming volumes of data into intuitive dashboards and reports. They have a team of SAP BI/BO experts who implement business intelligence platforms to enhance enterprise performance. These platforms provide personalized views of key metrics, detailed reports for decision makers, analytics capabilities, and access to real-time data to enable informed decisions.
This document discusses maximizing returns from a data warehouse. It covers the need for real-time data integration to power business intelligence and enable timely, trusted decisions. It outlines challenges with traditional batch-based approaches and how Oracle's data integration solutions address these through products that enable real-time data capture and delivery, bulk data movement, and data quality profiling to build an enterprise data warehouse.
The document discusses a unified data architecture that enables any user to access and analyze any data type from data capture through analysis. It describes using a discovery platform to enable interactive data discovery on structured and unstructured data without extensive modeling. It also describes using an integrated data warehouse for cross-functional analysis, shared analytics, and lowest total cost of ownership. Finally, it provides examples of using the architecture for IPTV quality of service analysis, including predictive models using decision trees and naive Bayes.
This document summarizes a project between Vistex, SAP, and IBM to use SAP HANA for real-time profitability analytics. The traditional approach of replicating operational data to data warehouses for reporting was slow. With SAP HANA, a proof of concept showed gross-to-net profitability reports could be generated in under one second using 20 million transaction records, versus the minutes required traditionally. The in-memory capabilities of SAP HANA enabled fast, detailed analysis without pre-aggregation.
The document compares the 3-year costs of the IBM Smart Analytics System 7700, Oracle Exadata Database Machine, and Teradata Active Enterprise Data Warehouse 6650. It finds that the IBM system provides better performance for complex workloads at a lower cost. Specifically, the IBM system has initial costs that are 11-16% lower and 3-year costs that are 40-43% lower than the Oracle and Teradata systems. The lower 3-year costs are primarily due to lower maintenance and support pricing from IBM, as well as lower personnel and facility expenses.
Innovation Webinar - Using IFS Applications BI to drive business excellenceIFS
Studies show that best-in-class businesses—those with the best operating margins and turnover growth in their industries—have clearly defined objectives supported by a Business Intelligence solution. In this session, we’ll look at specific features in the IFS Applications Business Intelligence solution. See how easily these features can help you support strategic business initiatives and reach improved operational results.
This document outlines Oracle's product direction for driving management excellence through its EPM&BI solutions. It discusses Oracle's framework for achieving management excellence, which includes rationalizing management systems, integrating systems with operational systems, and sharing insights across the extended enterprise. The document also summarizes Oracle's complete, open, and integrated capabilities for performance management and business intelligence.
This document discusses the rise of big data and how organizations are adapting. It notes that in 2000, the world generated 2 exabytes of new information and by 2012 that amount was generated every day. It also discusses how EMC acquired Isilon to help customers address the growing need for file-based storage solutions to manage big data. The document outlines the journey organizations are taking to leverage big data, moving from a focus on infrastructure to analytics to predictive applications. It emphasizes how data science teams now collaborate in new, agile ways compared to traditional IT approaches.
This document discusses Documentum xCP and its applications for big data. It begins by introducing Anthony Ng and Huang Xianchun as the authors. The rest of the document discusses how xCP can be used to ingest, analyze, and act on big data from various sources. It provides examples of using big data for applications in various industries like banking, insurance, retail, manufacturing and more. It also outlines EMC's big data stack that includes tools for storing, analyzing, and collaborating on large datasets.
Banks can leverage big data by building analytics platforms to gain insights from large, diverse datasets in real-time. The key is having solutions that can handle high volumes, varieties and velocities of data. SAS provides products to help banks with every stage of the analytics lifecycle from data preparation to predictive modeling to deployment. This allows banks to better assess risks like credit default and improve processes like customer scoring.
'The future of analytics is here', was presented by Mr. Deepak Ramanathan, Head - Information Management, SAS Asia Pacific (North). This document emphasizes on how SAS Visual Analytics empowers business to analyze data in seconds or minutes, which earlier used to take hours or days. This presentation was displayed at CIO - The Year Ahead conference, Mumbai, 29 - 30 Nov'12.
The document discusses embedding business intelligence (BI) capabilities into software applications. It describes 5 levels of embedded BI, ranging from static reporting to advanced analytics. Level 1 involves static reporting using a reporting library, while Level 2 adds interactive reporting capabilities. Level 3 introduces interactive dashboards. Levels 4 and 5 enable self-service reporting, data exploration, and advanced analytics. Embedding BI can help automate processes, generate insights from data, and support better decision making.
An introduction to Jaspersoft 5, a web-based data analysis solution for visualizing relational, OLAP, or Big Data environments such as Hadoop, MongoDB, Google BigQuery, Amazon Redshift, and many other engines
This document provides an overview and agenda for a presentation on exploring data with Jaspersoft. The presentation will include introductions to Jaspersoft, in-memory analysis with a demo, online analytical processing (OLAP) analysis with a demo, and a question and answer section. Jaspersoft is presented as a self-service business intelligence and embeddable reporting and analytics platform that provides solutions for data exploration and OLAP analysis through interactive visualizations and metadata-driven models.
Hadoop World 2011: Big Data Analytics – Data Professionals: The New Enterpris...Cloudera, Inc.
This presentation will explore how Hadoop and Big Data are re-inventing enterprise workflows, and the pivotal role of the Data Analyst. It will examine the changing face of analytics and the streamlining of iterative queries through evolved user interfaces. The speaker will cut through hype around “shorter time to insight” and explain how combining Hadoop and SQL-based analytics help companies discover emergent trends hidden in unstructured data, without having to retrain data miners or restaff. In particular, it will highlight changes to Big Data analysis from this paradigm and illustrate stepwise how analysts can now connect to Big Data platforms, assemble working data sets from disparate sources, analyze and mine that data for actionable insight, publish the results as visualizations and for feeding reporting tools, and operationalize Map-Reduce and Big Data outcomes into company workflows – all without touching the command line.
The document discusses the emergence of big data and new data architectures needed to handle large, diverse datasets. It notes that internet companies built their own data systems like Hadoop to process massive amounts of unstructured data across thousands of servers in a fault-tolerant, scalable way. These systems use a map-reduce programming model and distributed file systems like HDFS to store and process data in a parallel, distributed manner.
This document discusses goals, trends, and complexities of supporting an integrated student data warehouse. It covers project visions and baselines, Oracle Streams concepts for feeding data into the data warehouse, metadata, configuration issues, validation steps, and the importance of securing and monitoring the data stream for quality and compliance. Technical challenges include testing changes and their impact on the different systems.
AGC helps customers gain business intelligence insights from their data by transforming volumes of data into intuitive dashboards and reports. They have a team of SAP BI/BO experts who implement business intelligence platforms to enhance enterprise performance. These platforms provide personalized views of key metrics, detailed reports for decision makers, analytics capabilities, and access to real-time data to enable informed decisions.
This document discusses maximizing returns from a data warehouse. It covers the need for real-time data integration to power business intelligence and enable timely, trusted decisions. It outlines challenges with traditional batch-based approaches and how Oracle's data integration solutions address these through products that enable real-time data capture and delivery, bulk data movement, and data quality profiling to build an enterprise data warehouse.
The document discusses a unified data architecture that enables any user to access and analyze any data type from data capture through analysis. It describes using a discovery platform to enable interactive data discovery on structured and unstructured data without extensive modeling. It also describes using an integrated data warehouse for cross-functional analysis, shared analytics, and lowest total cost of ownership. Finally, it provides examples of using the architecture for IPTV quality of service analysis, including predictive models using decision trees and naive Bayes.
This document summarizes a project between Vistex, SAP, and IBM to use SAP HANA for real-time profitability analytics. The traditional approach of replicating operational data to data warehouses for reporting was slow. With SAP HANA, a proof of concept showed gross-to-net profitability reports could be generated in under one second using 20 million transaction records, versus the minutes required traditionally. The in-memory capabilities of SAP HANA enabled fast, detailed analysis without pre-aggregation.
The document compares the 3-year costs of the IBM Smart Analytics System 7700, Oracle Exadata Database Machine, and Teradata Active Enterprise Data Warehouse 6650. It finds that the IBM system provides better performance for complex workloads at a lower cost. Specifically, the IBM system has initial costs that are 11-16% lower and 3-year costs that are 40-43% lower than the Oracle and Teradata systems. The lower 3-year costs are primarily due to lower maintenance and support pricing from IBM, as well as lower personnel and facility expenses.
Innovation Webinar - Using IFS Applications BI to drive business excellenceIFS
Studies show that best-in-class businesses—those with the best operating margins and turnover growth in their industries—have clearly defined objectives supported by a Business Intelligence solution. In this session, we’ll look at specific features in the IFS Applications Business Intelligence solution. See how easily these features can help you support strategic business initiatives and reach improved operational results.
This document outlines Oracle's product direction for driving management excellence through its EPM&BI solutions. It discusses Oracle's framework for achieving management excellence, which includes rationalizing management systems, integrating systems with operational systems, and sharing insights across the extended enterprise. The document also summarizes Oracle's complete, open, and integrated capabilities for performance management and business intelligence.
This document discusses the rise of big data and how organizations are adapting. It notes that in 2000, the world generated 2 exabytes of new information and by 2012 that amount was generated every day. It also discusses how EMC acquired Isilon to help customers address the growing need for file-based storage solutions to manage big data. The document outlines the journey organizations are taking to leverage big data, moving from a focus on infrastructure to analytics to predictive applications. It emphasizes how data science teams now collaborate in new, agile ways compared to traditional IT approaches.
This document discusses Documentum xCP and its applications for big data. It begins by introducing Anthony Ng and Huang Xianchun as the authors. The rest of the document discusses how xCP can be used to ingest, analyze, and act on big data from various sources. It provides examples of using big data for applications in various industries like banking, insurance, retail, manufacturing and more. It also outlines EMC's big data stack that includes tools for storing, analyzing, and collaborating on large datasets.
Los arrecifes de coral de Hawái están en peligro debido al cambio climático y la contaminación. El aumento de las temperaturas del océano está blanqueando los corales, matándolos. La contaminación por plásticos y desechos también está dañando los delicados ecosistemas de los arrecifes de coral. Se necesitan esfuerzos para reducir las emisiones de gases de efecto invernadero y la contaminación marina para proteger estos importantes arrecifes de coral.
The document discusses the advantages of 64-bit ARMv8-A architecture for Android. It describes how Android Lollipop provides support for both 32-bit and 64-bit applications. Native and ART applications can see performance gains by taking advantage of the ARMv8-A architecture's modern instruction set and use of more registers. The document encourages developers to explore 64-bit development and provides additional resources.
Este documento resume las vidas y contribuciones pedagógicas de Juan Amos Comenio y Juan Luis Vives, dos importantes pensadores de los siglos XV-XVII. Comenio propuso un nuevo método de enseñanza basado en la comprensión, retención y práctica. Abogó por escuelas accesibles para todos y una enseñanza activa y centrada en el estudiante. Vives también hizo contribuciones significativas a la educación, incluida la formación de la mujer y los necesitados. Ambos buscaron mejorar
The document discusses big data and big analytics. It notes that big data refers to situations where the volume, velocity, and variety of data exceeds an organization's storage and processing capabilities. It then outlines SAS's approach to high-performance analytics, including in-memory architecture, grid computing, and in-database analytics to enable real-time insights from large and diverse datasets. Several case studies demonstrate how SAS solutions have helped customers significantly reduce analytics processing times and improve outcomes.
AGC helps customers gain business intelligence insights from their data by transforming volumes of data into intuitive dashboards and reports. They have a team of SAP BI/BO experts who implement business intelligence platforms to enhance enterprise performance. These platforms provide personalized views of key metrics, detailed reports for decision makers, analytics capabilities, and access to real-time data to enable informed decisions.
The document discusses predictive analytics techniques including data preparation, modeling, and model monitoring. It describes preparing data through transformation, deriving behavioral variables, and quality checks. Modeling techniques covered include decision trees, regression, neural networks, and ensemble modeling in SAS Enterprise Miner or other software. Model monitoring compares actual and predicted values, analyzes variable distributions in scored data, and monitors model performance metrics.
Predictive Analytics: Advanced techniques in data miningSAS Asia Pacific
The document discusses predictive analytics techniques including defining objectives, data preparation, modeling, deployment, and model monitoring. It describes preparing data through transformation, deriving behavioral variables, and quality checks. Modeling techniques covered include decision trees, regression, neural networks, and ensemble modeling. Model monitoring compares actual and predicted values, and analyzes variable distributions and predicted scores.
This presentation was made by Mr. Deepak Ramanathan, Information Practice Head - SAS North Asia at SAS Forum India 2013. The presentation presents the current scenario on Big Data and how SAS High-Performance Analytics can empower organisations to derive value from this information explosion.
SAS Big Data Forum - Transforming Big Data into Corporate GoldLouis Fernandes
Synopsis: How SAS believes organisations can turn Big Data in to competitive advantage through the use of High Performance Analytics.
In this presentations, we look at how SAS is seeing organisations take the outputs from big data analysis and turn them into tangible business outcomes through real-time decision-making.
In it, we explore:
- Why we believe organisations need to exploit their data assets to create the insights that build competitive advantage
- How to develop infrastructures required to support multi-dimentsional insight
- What SAS is doing to make this a reality
Key topics include:
- Data governance
- Big data infrastructure
- High performance analytics
- Data visualisation
About SAS:
- World’s largest, privately held software company,
- 35 years old
- Focus on advanced and predictive analytics right from the word go
- Big data has been in our DNA before it became mainstream
This document discusses big data solutions and analytics. It defines big data in terms of volume, velocity, and variety of data. It contrasts big data analytics with traditional business intelligence, noting that big data looks for untapped insights rather than dashboards. It also provides examples of scalable big data platform architectures and advanced analytics capabilities. Finally, it outlines Anexinet's big data offerings including strategy, starter solutions, projects, and partnerships.
Farklı Ortamlarda Büyük Veri Kavramı -Big Data by Sybase Sybase Türkiye
This document discusses big data analytics in a heterogeneous world. It covers the issues of dealing with volume, variety and velocity of big data. It also discusses the growing trends in big data analytics solutions including NoSQL databases, Hadoop, columnar databases and in-memory analytics. Finally, it proposes a comprehensive three-tier framework using commercial and open source software to provide reliable data management, application services and business intelligence tools to build bridges across heterogeneous data environments.
Predictive Analytics: A New Wealth of OptionsInside Analysis
The Briefing Room with Gregory Piatetsky and Sybase, an SAP Company
Slides from the Live Webcast on Apr. 17, 2012
Predicting the future happens all the time these days, in all kinds of business situations. And now, there are more options than ever for leveraging the power of predictive analytics. That's partly due to the rise of open-source projects like Hadoop and partly because of the maturation of traditional analytical platforms. The bottom line is that predictive power is now at the fingertips of a much broader audience.
Register for this episode of The Briefing Room to hear KDnuggets purveyor Gregory Piatetsky explain why data mining and predictive analytics are experiencing a renaissance. He'll be briefed by Joydeep Das of Sybase, an SAP Company, who will outline how Sybase IQ facilitates three approaches: the traditional "pull" model used by SAS, SPSS and other analytic workbenches; a newer "push" model where much of the heavy lifting takes place in the database; and a very novel hybrid "push-pull" model that leverages Hadoop.
The document discusses new features and enhancements in SAS analytics software. Key updates include new access engines and transformations in Data Integration Studio 4.3, metadata search improvements, enhanced SAS code importing, and new reporting features in Enterprise Guide 5.1 and the Microsoft Office add-in. Analytics software such as Enterprise Miner and Model Manager received new capabilities as well. SAS also introduced its new High-Performance Analytics product to handle large-scale data.
The document discusses new features and enhancements in SAS analytics software. Key updates include new access engines and transformations in Data Integration Studio 4.3, metadata search improvements, enhanced SAS code importing, and new reporting capabilities in Enterprise Guide 5.1 and the Microsoft Office add-in. Analytics software such as Enterprise Miner and Model Manager received new functionality. SAS also introduced its new High-Performance Analytics product.
This document discusses the benefits of Oracle Essbase for business analysis. It highlights that Essbase provides (1) a rich user experience through integration with Microsoft Office and advanced reporting and visualization tools, (2) a highly advanced calculation engine with over 350 functions for financial, time, and custom calculations, and (3) a flexible and optimized environment for custom analytic applications and modeling across data sources at enterprise scale. Essbase allows analysts to have "speed of thought" conversations with data and gain insights to make better decisions.
Technology Strategies for Big Data Analytics, Teradata Aster
SAS Presentation delivered at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: Technology Strategies for Big Data Analytics, by Bernard Blais, Global Strategist and Principal Manager, SAS
The exploding volume, complexity and velocity of big data present an increasing challenge to organizations, but also a significant opportunity to derive valuable insights. As organizations are tasked with managing massive data sets, it’s clear that the value of big data will be derived from the analytics that can be performed on it. Analytics is the key to identifying patterns, managing risks and tackling previously unsolvable problems. This presentation provides an overview of how to comprehensively tackle big data, including emerging strategies for information management, analytics, and high performance analytics.
The Essential Guide to SAP Cloud, Data Migration, ABAP, and Reporting.pdfingenxtec
The landscape of enterprise software is in constant flux, and SAP's evolution mirrors this dynamic transformation. Cloud-based innovation, data-driven insights via robust reporting, and the flexibility of ABAP development empower businesses to streamline operations and make impactful decisions.
Information Management: Answering Today’s Enterprise ChallengeBob Rhubart
As presented by George Lumpkin at OTN Architect Day, Redwood Shores, CA, 7/22/09.
Find an OTN Architect Day event near you: http://www.oracle.com/technology/architect/archday.html
Interact with Architect Day presenters and participants on Oracle Mix: https://mix.oracle.com/groups/15511
How PACE Layering bridges the GAP From Systems of Record to Systems of Engage...Jeff Shuey
How PACE Layering bridges the GAP From Systems of Record to Systems of Engagement. Pace Layering is a service mark of the Gartner Group.
This session was presented at the Microsoft SharePoint Conference in Las Vegas in Nov 2012 by Jeff Shuey (@jshuey) - If you have questions about the use case, the products or anything else please contact Jeff Shuey - details are included in the file.
Analytic Platforms in the Real World with 451Research and Calpont_July 2012Calpont Corporation
Matt Aslett of 451 Research discussed the rise of analytic platforms and their role in enabling exploratory analytics on large datasets. Bob Wilkinson from Calpont then presented on InfiniDB, Calpont's columnar analytic platform that provides scalable and fast performance for complex queries. InfiniDB was shown to accelerate analytics for telecommunications customer experience data and online advertising attribution. The discussion highlighted how InfiniDB supports flexible schemas and a spectrum of analytic approaches to enable exploratory analysis on structured data.
Best practices for building and deploying predictive models over big data pre...Kun Le
The tutorial is divided into 12 modules that cover best practices for building and deploying predictive models over big data. It introduces key concepts like predictive analytics, building predictive models, and deploying models. The life cycle of a predictive model is also described, from exploratory data analysis to deployment and operations.
AllAccessSAP 2012 Finale - SAP Slides (incl links)BI Brainz Group
The actual SAP slide deck from our webinar. Click here to gain full details about the event http://everythingxcelsius.com/businessobjects/recording-allaccesssap-xcelsius-sod-update-2013-sap-bi-roadmap-stats/5531
This document discusses moving NEON optimizations to 64-bit ARM architectures. Some key points:
- NEON is an ARM instruction set extension that allows single-instruction multiple data (SIMD) processing. It has more registers and capabilities in AArch64, including double precision floating point.
- Migrating NEON code to AArch64 usually only requires minor changes to assembly code due to compatibility in C/intrinsics code and clearer register mappings. Existing NEON documentation still applies.
- Open source libraries and compilers support NEON optimizations, providing performance boosts such as 3-4x faster video codecs. The Android NDK fully supports 64-bit development.
- Examples show optimized
The document discusses ARM's Intelligent Power Allocation (IPA) technology, which aims to maximize performance within thermal limits. It describes three types of power consumption scenarios and the limitations of the current Linux thermal framework. IPA uses a closed-loop control system to dynamically allocate power between components like the CPU and GPU based on temperature, power estimates, and performance requests. Test results show IPA achieving up to 31% higher FPS in games compared to static thermal policies, with more consistent temperature control.
This document discusses how Serengeti can be used to automate the deployment and management of Hadoop clusters on VMware vSphere. Some key points:
- Serengeti is a virtual appliance that can be deployed on vSphere and automates the provisioning of Hadoop clusters within 10 minutes from templates.
- It allows separating storage and compute by deploying Hadoop data nodes on shared storage and compute nodes as VMs for better elasticity and utilization.
- Serengeti supports elastic scaling of Hadoop clusters, multi-tenancy by isolating tenant workloads, and live configuration changes with rolling upgrades and no downtime.
This document discusses recommended architectures and best practices for deploying Hadoop on VMware vSphere. It recommends deploying Hadoop nodes across multiple virtualization hosts with 10Gb networking for high performance. The standard deployment places data nodes on shared storage and task trackers on local disks. It also discusses planning the cluster size, hardware requirements including CPU, memory, storage and networking considerations. Configuration recommendations include using NTP, proper virtual disk settings, enabling NUMA and avoiding overcommitting resources.
1. beyond mission critical virtualizing big data and hadoopChiou-Nan Chen
Virtualizing big data platforms like Hadoop provides organizations with agility, elasticity, and operational simplicity. It allows clusters to be quickly provisioned on demand, workloads to be independently scaled, and mixed workloads to be consolidated on shared infrastructure. This reduces costs while improving resource utilization for emerging big data use cases across many industries.
Pivotal HD is a Hadoop distribution that includes additional components to configure, deploy, monitor and manage Hadoop clusters. It provides tools like the Command Center for visual cluster monitoring and job management, Hadoop Virtualization Extensions to improve resource utilization, and HAWQ for high performance SQL queries and analytics across Hadoop data.
The document discusses EMC's transformation to an IT-as-a-Service model. It summarizes how EMC has virtualized 90% of its server workloads, consolidated data centers, and transformed its IT infrastructure to deliver services through a cloud foundation. This allows EMC to enhance agility, optimize costs, and deliver business value through offerings like infrastructure-as-a-service, platform-as-a-service, and software-as-a-service.
This document discusses how IT is transforming through trends like cloud computing and big data. It summarizes that EMC can help customers navigate these changes by providing solutions like hybrid cloud infrastructure and big data analytics to help businesses transform their applications and IT infrastructure. The document also emphasizes that EMC is committed to innovation through R&D investment and acquisitions to ensure it continues to lead customers on their journey to the cloud and with big data.
The document discusses disaster recovery for mission critical applications. It notes challenges in ensuring application availability with data growth and budget pressures, while meeting regulatory requirements. It discusses using replication, snapshots, and continuous data protection to reduce recovery point objectives (RPO) from hours to minutes or less. EMC provides integrated solutions using technologies like Data Domain, Avamar, RecoverPoint, and VPlex to automate backup, replication, and recovery for applications.
The document discusses desktop virtualization and cloud computing. It compares the PC era to the current cloud era and how workstyles have shifted from PCs to mobile devices that can access cloud services from any location using various devices. It discusses how users can access their desktops, applications, files, and services from any cloud through mobile workstyles. It also mentions some benefits of desktop virtualization like security, collaboration, application migration, integration and managing services from various devices and clouds.
The document discusses virtualizing mission critical applications. It notes that the primary drivers for virtualizing applications are cost savings and service improvement. It provides statistics showing an increasing percentage of workload instances running on VMware for applications like Microsoft Exchange, SharePoint, SQL, Oracle, and SAP. It then discusses EMC IT's journey towards a private cloud, moving from an infrastructure focus to an applications focus to an IT-as-a-service model. The document also discusses challenges around data protection and backup/recovery for virtualized applications and provides solutions using technologies like Avamar, Data Domain, and VFCache. It provides an example case study of EMC IT successfully virtualizing their Oracle 11i CRM system.
The document discusses EMC and Oracle's long-standing partnership in developing solutions to optimize Oracle applications. It outlines three common deployment models for Oracle (aggregation, verticalized, virtualization) and describes the benefits of virtualizing Oracle software, such as 3x higher performance with lower total cost of ownership. It also introduces EMC solutions like Vblock infrastructure platforms, FAST automated storage tiering, and VFCache server flash caching that help address challenges of Oracle I/O performance and optimize storage for virtualized Oracle environments.
This document describes virtualization solutions using Microsoft Hyper-V and System Center with EMC storage components. It provides configuration details for solutions supporting 50 and 100 virtual machines, including servers, hypervisors, networking, storage and backup components. It also discusses features for virtualizing Microsoft applications and the benefits of using System Center for management.
This document discusses the transformation of IT backup and recovery due to trends in data growth and regulations. It presents EMC's backup solutions including Data Domain for disk-based backup with deduplication, Avamar for fast VMware backups, and NetWorker for centralized backup management. These solutions provide faster backups, recovery and scalability compared to traditional tape-based systems. Case studies show customers achieving up to 98% data reduction, replacing tapes completely and saving over $200k annually with EMC's backup products.
The document discusses EMC's strategy called "FLASH 1st" for data storage over the next decade. It argues that traditional hard disk drives will not be able to keep up with rapidly growing data and increasing IO demands. FLASH/solid state technology on the other hand is improving much faster than HDDs and will provide dramatically better performance and cost efficiency. EMC's FLASH 1st strategy leverages automated tiering software to place active "hot" data on high-performance FLASH storage and less active "cold" data on lower-cost capacity HDDs to maximize benefits.
This document discusses Cisco's desktop virtualization solution. It begins with an overview of the desktop virtualization market trends, including rising management costs and the need for access from any device anywhere. It then covers desktop virtualization models and user types. The rest of the document discusses Cisco's vision for desktop virtualization, the challenges it addresses, and how Cisco UCS provides advantages for desktop virtualization deployments, including an end-to-end virtualized solution.
The document discusses VPLEX, EMC's multi-site active-active storage solution. VPLEX allows synchronous data access across data centers for high availability and disaster recovery. It uses clustered controllers and virtualization to provide redundancy. VPLEX can also integrate with RecoverPoint for continuous data protection and replication across three sites.