This document discusses how SQL Server AlwaysOn solutions in SQL Server 2012 can be used to provide high availability and disaster recovery capabilities. It describes the different layers of protection provided, including infrastructure availability with Windows Server Failover Clustering, SQL Server instance level protection with AlwaysOn Failover Cluster Instances, and database availability with AlwaysOn Availability Groups. The paper also touches on concepts like planned vs unplanned downtime, recovery time objectives, and recovery point objectives that are important considerations for high availability and disaster recovery planning.
This report is intended primarily for business executives who are making important decisions with the results generated from data analysts and data scientists.
How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered by researchers and practitioners alike. In this talk (presented at QConSF 2018), we first present an overview of privacy breaches as well as algorithmic bias / discrimination issues observed in the Internet industry over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving privacy and fairness in data-driven systems. We motivate the need for adopting a "privacy and fairness by design" approach when developing data-driven AI/ML models and systems for different consumer and enterprise applications. We also focus on the application of privacy-preserving data mining and fairness-aware machine learning techniques in practice, by presenting case studies spanning different LinkedIn applications, and conclude with the key takeaways and open challenges.
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Overview of mit sloan case study on ge data and analytics initiative titled g...Gregg Barrett
GE collects sensor data from industrial equipment to analyze equipment performance and predict failures. It created a "data lake" to integrate raw flight data from 3.4 million flights with other data sources. This allows data scientists to identify issues reducing equipment uptime for customers. However, GE faces challenges in finding qualified analytics talent and establishing effective data governance as it scales its data and analytics efforts.
Intro to big data and applications - day 1Parviz Vakili
This document provides an overview and introduction to big data and its applications. It defines key concepts related to big data, including the five V's of big data (volume, velocity, variety, veracity, and value). It also discusses where big data comes from, different data types (structured, semi-structured, unstructured), and common applications of big data across different industries. Finally, it introduces concepts of data governance, data strategy, and how big data can support digital transformation.
Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN
Data Science and its Relationship to Big Data and Data-Driven Decision Making
To cite this article:
Foster Provost and Tom Fawcett. Big Data. February 2013, 1(1): 51-59. doi:10.1089/big.2013.1508.
Foster Provost and Tom Fawcett
Published in Volume: 1 Issue 1: February 13, 2013
ref:http://online.liebertpub.com/doi/full/10.1089/big.2013.1508
https://www.researchgate.net/publication/256439081_Data_Science_and_Its_Relationship_to_Big_Data_and_Data-Driven_Decision_Making
This document summarizes the key findings of Kaspersky Lab's 2014 IT Security Risks Survey. Some of the main points include:
1) Protection of confidential data against targeted attacks was the top priority for 38% of IT managers surveyed, compared to not being a priority in previous years.
2) 94% of companies encountered cybersecurity issues originating outside their networks, up from 91% in 2013. About 12% faced targeted attacks, up from 9% previously.
3) The average cost of a data security incident was estimated at $720,000, while a successful targeted attack could cost over $2.5 million. Losses often included internal data, client data, and financial information.
This report is intended primarily for business executives who are making important decisions with the results generated from data analysts and data scientists.
How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered by researchers and practitioners alike. In this talk (presented at QConSF 2018), we first present an overview of privacy breaches as well as algorithmic bias / discrimination issues observed in the Internet industry over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving privacy and fairness in data-driven systems. We motivate the need for adopting a "privacy and fairness by design" approach when developing data-driven AI/ML models and systems for different consumer and enterprise applications. We also focus on the application of privacy-preserving data mining and fairness-aware machine learning techniques in practice, by presenting case studies spanning different LinkedIn applications, and conclude with the key takeaways and open challenges.
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Overview of mit sloan case study on ge data and analytics initiative titled g...Gregg Barrett
GE collects sensor data from industrial equipment to analyze equipment performance and predict failures. It created a "data lake" to integrate raw flight data from 3.4 million flights with other data sources. This allows data scientists to identify issues reducing equipment uptime for customers. However, GE faces challenges in finding qualified analytics talent and establishing effective data governance as it scales its data and analytics efforts.
Intro to big data and applications - day 1Parviz Vakili
This document provides an overview and introduction to big data and its applications. It defines key concepts related to big data, including the five V's of big data (volume, velocity, variety, veracity, and value). It also discusses where big data comes from, different data types (structured, semi-structured, unstructured), and common applications of big data across different industries. Finally, it introduces concepts of data governance, data strategy, and how big data can support digital transformation.
Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN
Data Science and its Relationship to Big Data and Data-Driven Decision Making
To cite this article:
Foster Provost and Tom Fawcett. Big Data. February 2013, 1(1): 51-59. doi:10.1089/big.2013.1508.
Foster Provost and Tom Fawcett
Published in Volume: 1 Issue 1: February 13, 2013
ref:http://online.liebertpub.com/doi/full/10.1089/big.2013.1508
https://www.researchgate.net/publication/256439081_Data_Science_and_Its_Relationship_to_Big_Data_and_Data-Driven_Decision_Making
This document summarizes the key findings of Kaspersky Lab's 2014 IT Security Risks Survey. Some of the main points include:
1) Protection of confidential data against targeted attacks was the top priority for 38% of IT managers surveyed, compared to not being a priority in previous years.
2) 94% of companies encountered cybersecurity issues originating outside their networks, up from 91% in 2013. About 12% faced targeted attacks, up from 9% previously.
3) The average cost of a data security incident was estimated at $720,000, while a successful targeted attack could cost over $2.5 million. Losses often included internal data, client data, and financial information.
This document discusses big data challenges for data management at an NHS Trust in London. It begins with an introduction explaining why data has become a valuable asset for organizations. It then summarizes three articles on big data management. The first article describes using cloud computing for big data storage and processing. The second provides an overview of big data sources and management research. The third discusses opportunities for IT professionals in big data. It concludes by analyzing solutions the articles propose for the NHS Trust's big data challenges, such as cloud computing and improved network architecture, and discusses implementing changes to data management policies.
This document provides an overview of big data concepts and Hadoop. It discusses the characteristics of big data including volume, variety and velocity. It compares traditional data warehouses to Hadoop and explains when each is best suited. Use cases of big data from various companies are presented. The document also summarizes a survey on big data adoption trends and priorities across industries. Finally, it provides details on the Hadoop framework and its key components.
Data pricing and data license agreements
Magdalena Balazinska, U. Washington (videoconference)
-Key note-
International conference on
“DATA, DIGITAL BUSINESS MODELS, CLOUD COMPUTING AND ORGANIZATIONAL DESIGN”
24-25 November 2014 ,
Université Paris–Sud
Big data offers opportunities but also security and privacy issues due to its large volume, velocity, and variety. Some key security issues include insecure computation, lack of input validation and filtering, and privacy concerns in data mining and analytics. Recommendations to enhance big data security include securing computation code, implementing comprehensive input validation and filtering, granular access controls, and securing data storage and computation. Case studies on security issues include vulnerability to fake data generation, challenges with Amazon's data lakes, possibility of sensitive information mining, and the rapid evolution of NoSQL databases lacking security focus.
The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
The Post-Relational Reality Sets In: 2011 Survey on Unstructured DataMarkLogic Corporation
The "Big Data" influx is upon us—terabytes and gigabytes of bits and bytes that are overwhelming IT infrastructures. This growth is unprecedented and much of it consists of unstructured information, which is creating new types of challenges in terms of governance, management and security practices. A new survey finds that companies are only beginning to grasp the complexities created by all this new unstructured data. Even the most mature organizations that acknowledge they depend on unstructured data still do not have effective governance or best practices in place. The survey results imply that companies are missing the opportunity to leverage the full value of this unstructured data. Download the complete survey report.
Delivering High Availability and Performance with SQL Server 2014 (Silviu Nic...ITCamp
A deep dive into the new features in SQL 2014, and how those can be used to increase the performance and reliability of transactional databases and multi-terabyte data warehouses.
Tips to install and manage always on availability groups in sql server 2012 &...Antonios Chatzipavlis
This document provides an overview and agenda for a presentation on AlwaysOn Availability Groups in SQL Server 2012 and 2014. It covers topics such as: understanding AlwaysOn availability groups; availability modes; active secondary replicas; failover options; flexible failover policies; enhancements in SQL Server 2014; prerequisites; installation scenarios; and tips from experience implementing availability groups. The presenter's background and certifications are also listed.
This document provides an overview and demonstration of Oracle's .NET stored procedures and Oracle Developer Tools for Visual Studio .NET. It outlines the key features and benefits, demonstrates the developer tools through examples, and discusses how to write, deploy, and debug .NET stored procedures within Oracle Database. The presentation is intended for informational purposes only and should not be relied upon for purchasing decisions.
Sql server’s high availability technologiesvenkatchs
SQL Server provides several high availability technologies to protect against server, site, and database failures including failover clustering, database mirroring, log shipping, peer-to-peer replication, database snapshots, and backup and restore. Failover clustering protects at the server-level by allowing nodes to failover. Database mirroring and log shipping protect databases by copying transaction logs from a principal database to a mirror or secondary database. Peer-to-peer replication replicates changes between databases for availability. Snapshots enable quick recovery of databases. Backup and restore reduces recovery time through different backup types and transaction log application.
This document discusses the high availability and disaster recovery solution implemented at QR to support their mission critical SAP application. The solution involved migrating from a mainframe database to a SQL Server database, with databases clustered across two data centers for redundancy. The new solution met all of QR's requirements including zero data loss, recovery within 5 minutes, and the ability to run the full solution on a single node.
SPSMEL 2012 - SQL 2012 AlwaysOn Availability Groups for SharePoint 2010 / 2013Michael Noel
This document discusses SQL Server 2012 AlwaysOn Availability Groups which can be used to provide high availability and disaster recovery for SharePoint 2010/2013 farms. It covers what AlwaysOn is, the requirements to implement it, different design options, and how it improves upon previous SQL mirroring technologies. A sample multi-replica design is presented with synchronous and asynchronous copies across primary, DR, and read-only farms.
Code understanding and systems design with visual studio 2010Spiffy
Hammad Rajjoub is a Microsoft MVP for Connected Systems with over 6 years of experience. He is a member of the Connected Systems Advisory Board at Microsoft and is also an author. His website is www.hammadrajjoub.net and he can be followed on Twitter @HammadRajjoub or searched on Bing at http://www.bing.com/search?q=hammadrajjoub. The document discusses Microsoft Visual Studio tools that can be used to visualize relationships in code.
This document provides an overview of the key features and architecture of Visual Studio Team System 2010. It discusses how Visual Studio Team System addresses common business problems around application lifecycle management. The overview then explores features for architecture/modeling, development, testing, lab management, and use of Team Foundation Server. Specific features highlighted include the architecture explorer, layer diagrams, UML support, historical debugging, test impact analysis, database extensibility, lab management capabilities, and manual/automated testing tools.
SQL Server AlwaysOn for Dummies SQLSaturday #202 EditionMark Broadbent
Welcome to Microsoft's world of the buzzword. Yes, they've done it again and created another ambiguous term that no one really understands. AlwaysOn is a powerful group of highly available technologies, and in this presentation, we will delve into their murky world & reveal the technology behind the buzz. Focusing specifically on the two key components of SQL Server 2012 AlwaysOn in Failover Clustered Instances and Availability Groups, we will investigate their pre-requisites, setup, administration, use & drawbacks. We will look at: Using Windows 2008, 2012 and Server Core Windows Clustering Quorum Failover Clustered Instances Availability Groups Readable Secondaries Clustering Tools and PowerShell Dummies and higher are welcome.
SQL 2012 AlwaysOn Availability Groups for SharePoint 2013 - SharePoint Connec...Michael Noel
Using SQL Server 2012 AlwaysOn Availability Groups allows for high availability and disaster recovery of SharePoint 2013 farms. It provides zero data loss failover between nodes and readable secondary replicas. The document outlines the requirements and provides a step-by-step guide to implementing AlwaysOn Availability Groups for a SharePoint farm, including creating an availability group, adding databases, and creating an availability group listener.
SQL Server 2012 High Availability with AlwaysOn Availability GroupsEdwin M Sarmiento
This document discusses high availability options in SQL Server, including database mirroring, replication, and log shipping. It notes challenges with database mirroring, including inefficient resource utilization, multiple copies of data, and lack of automatic failover. It then introduces AlwaysOn Availability Groups, a new high availability feature in SQL Server 2012 that uses failover clustering and allows for multiple synchronized secondary replicas with automatic failover. A demo of AlwaysOn Availability Groups is provided.
SQL Server High Availability Solutions (Pros & Cons)Hamid J. Fard
Proper SQL Server High Availability Solution Is Highly Depends on the Business Objective and IT Operation Objectives. It Happens Sometimes that We Might Have Few Solutions on the Table to Implement.
SQL Server 2016 AlwaysOn Availability Groups New FeaturesJohn Martin
This deck was presented at SQL Relay 2015 in Bristol;
In this deck we will look at some of the new capabilities that are slated for release as part of the Microsoft SQL Server 2016 platform.
Demo code for this deck can be found at: http://1drv.ms/1PC8707
This document discusses SQL Server 2012 AlwaysOn, a high availability and disaster recovery solution. It provides an overview of AlwaysOn availability groups, which allow for multiple synchronous or asynchronous copies of databases across instances. Key features include readable secondary replicas, automatic instance and database failover, and the ability to perform backups on secondary replicas. The document also demonstrates AlwaysOn configuration and functionality through a virtual machine-based lab environment.
This document provides a summary of the Top 100 Tools for Learning in 2011 as compiled by Jane Hart from the Centre for Learning & Performance Technologies. 531 learning professionals shared their top 10 tools, from which the Top 100 list was created. The list includes tools such as Twitter, YouTube, Google Docs, Skype, WordPress, Dropbox, and Prezi. Jane Hart is the founder of the Centre for Learning & Performance Technologies and an independent consultant who writes and speaks about learning tools.
This document discusses big data challenges for data management at an NHS Trust in London. It begins with an introduction explaining why data has become a valuable asset for organizations. It then summarizes three articles on big data management. The first article describes using cloud computing for big data storage and processing. The second provides an overview of big data sources and management research. The third discusses opportunities for IT professionals in big data. It concludes by analyzing solutions the articles propose for the NHS Trust's big data challenges, such as cloud computing and improved network architecture, and discusses implementing changes to data management policies.
This document provides an overview of big data concepts and Hadoop. It discusses the characteristics of big data including volume, variety and velocity. It compares traditional data warehouses to Hadoop and explains when each is best suited. Use cases of big data from various companies are presented. The document also summarizes a survey on big data adoption trends and priorities across industries. Finally, it provides details on the Hadoop framework and its key components.
Data pricing and data license agreements
Magdalena Balazinska, U. Washington (videoconference)
-Key note-
International conference on
“DATA, DIGITAL BUSINESS MODELS, CLOUD COMPUTING AND ORGANIZATIONAL DESIGN”
24-25 November 2014 ,
Université Paris–Sud
Big data offers opportunities but also security and privacy issues due to its large volume, velocity, and variety. Some key security issues include insecure computation, lack of input validation and filtering, and privacy concerns in data mining and analytics. Recommendations to enhance big data security include securing computation code, implementing comprehensive input validation and filtering, granular access controls, and securing data storage and computation. Case studies on security issues include vulnerability to fake data generation, challenges with Amazon's data lakes, possibility of sensitive information mining, and the rapid evolution of NoSQL databases lacking security focus.
The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
The Post-Relational Reality Sets In: 2011 Survey on Unstructured DataMarkLogic Corporation
The "Big Data" influx is upon us—terabytes and gigabytes of bits and bytes that are overwhelming IT infrastructures. This growth is unprecedented and much of it consists of unstructured information, which is creating new types of challenges in terms of governance, management and security practices. A new survey finds that companies are only beginning to grasp the complexities created by all this new unstructured data. Even the most mature organizations that acknowledge they depend on unstructured data still do not have effective governance or best practices in place. The survey results imply that companies are missing the opportunity to leverage the full value of this unstructured data. Download the complete survey report.
Delivering High Availability and Performance with SQL Server 2014 (Silviu Nic...ITCamp
A deep dive into the new features in SQL 2014, and how those can be used to increase the performance and reliability of transactional databases and multi-terabyte data warehouses.
Tips to install and manage always on availability groups in sql server 2012 &...Antonios Chatzipavlis
This document provides an overview and agenda for a presentation on AlwaysOn Availability Groups in SQL Server 2012 and 2014. It covers topics such as: understanding AlwaysOn availability groups; availability modes; active secondary replicas; failover options; flexible failover policies; enhancements in SQL Server 2014; prerequisites; installation scenarios; and tips from experience implementing availability groups. The presenter's background and certifications are also listed.
This document provides an overview and demonstration of Oracle's .NET stored procedures and Oracle Developer Tools for Visual Studio .NET. It outlines the key features and benefits, demonstrates the developer tools through examples, and discusses how to write, deploy, and debug .NET stored procedures within Oracle Database. The presentation is intended for informational purposes only and should not be relied upon for purchasing decisions.
Sql server’s high availability technologiesvenkatchs
SQL Server provides several high availability technologies to protect against server, site, and database failures including failover clustering, database mirroring, log shipping, peer-to-peer replication, database snapshots, and backup and restore. Failover clustering protects at the server-level by allowing nodes to failover. Database mirroring and log shipping protect databases by copying transaction logs from a principal database to a mirror or secondary database. Peer-to-peer replication replicates changes between databases for availability. Snapshots enable quick recovery of databases. Backup and restore reduces recovery time through different backup types and transaction log application.
This document discusses the high availability and disaster recovery solution implemented at QR to support their mission critical SAP application. The solution involved migrating from a mainframe database to a SQL Server database, with databases clustered across two data centers for redundancy. The new solution met all of QR's requirements including zero data loss, recovery within 5 minutes, and the ability to run the full solution on a single node.
SPSMEL 2012 - SQL 2012 AlwaysOn Availability Groups for SharePoint 2010 / 2013Michael Noel
This document discusses SQL Server 2012 AlwaysOn Availability Groups which can be used to provide high availability and disaster recovery for SharePoint 2010/2013 farms. It covers what AlwaysOn is, the requirements to implement it, different design options, and how it improves upon previous SQL mirroring technologies. A sample multi-replica design is presented with synchronous and asynchronous copies across primary, DR, and read-only farms.
Code understanding and systems design with visual studio 2010Spiffy
Hammad Rajjoub is a Microsoft MVP for Connected Systems with over 6 years of experience. He is a member of the Connected Systems Advisory Board at Microsoft and is also an author. His website is www.hammadrajjoub.net and he can be followed on Twitter @HammadRajjoub or searched on Bing at http://www.bing.com/search?q=hammadrajjoub. The document discusses Microsoft Visual Studio tools that can be used to visualize relationships in code.
This document provides an overview of the key features and architecture of Visual Studio Team System 2010. It discusses how Visual Studio Team System addresses common business problems around application lifecycle management. The overview then explores features for architecture/modeling, development, testing, lab management, and use of Team Foundation Server. Specific features highlighted include the architecture explorer, layer diagrams, UML support, historical debugging, test impact analysis, database extensibility, lab management capabilities, and manual/automated testing tools.
SQL Server AlwaysOn for Dummies SQLSaturday #202 EditionMark Broadbent
Welcome to Microsoft's world of the buzzword. Yes, they've done it again and created another ambiguous term that no one really understands. AlwaysOn is a powerful group of highly available technologies, and in this presentation, we will delve into their murky world & reveal the technology behind the buzz. Focusing specifically on the two key components of SQL Server 2012 AlwaysOn in Failover Clustered Instances and Availability Groups, we will investigate their pre-requisites, setup, administration, use & drawbacks. We will look at: Using Windows 2008, 2012 and Server Core Windows Clustering Quorum Failover Clustered Instances Availability Groups Readable Secondaries Clustering Tools and PowerShell Dummies and higher are welcome.
SQL 2012 AlwaysOn Availability Groups for SharePoint 2013 - SharePoint Connec...Michael Noel
Using SQL Server 2012 AlwaysOn Availability Groups allows for high availability and disaster recovery of SharePoint 2013 farms. It provides zero data loss failover between nodes and readable secondary replicas. The document outlines the requirements and provides a step-by-step guide to implementing AlwaysOn Availability Groups for a SharePoint farm, including creating an availability group, adding databases, and creating an availability group listener.
SQL Server 2012 High Availability with AlwaysOn Availability GroupsEdwin M Sarmiento
This document discusses high availability options in SQL Server, including database mirroring, replication, and log shipping. It notes challenges with database mirroring, including inefficient resource utilization, multiple copies of data, and lack of automatic failover. It then introduces AlwaysOn Availability Groups, a new high availability feature in SQL Server 2012 that uses failover clustering and allows for multiple synchronized secondary replicas with automatic failover. A demo of AlwaysOn Availability Groups is provided.
SQL Server High Availability Solutions (Pros & Cons)Hamid J. Fard
Proper SQL Server High Availability Solution Is Highly Depends on the Business Objective and IT Operation Objectives. It Happens Sometimes that We Might Have Few Solutions on the Table to Implement.
SQL Server 2016 AlwaysOn Availability Groups New FeaturesJohn Martin
This deck was presented at SQL Relay 2015 in Bristol;
In this deck we will look at some of the new capabilities that are slated for release as part of the Microsoft SQL Server 2016 platform.
Demo code for this deck can be found at: http://1drv.ms/1PC8707
This document discusses SQL Server 2012 AlwaysOn, a high availability and disaster recovery solution. It provides an overview of AlwaysOn availability groups, which allow for multiple synchronous or asynchronous copies of databases across instances. Key features include readable secondary replicas, automatic instance and database failover, and the ability to perform backups on secondary replicas. The document also demonstrates AlwaysOn configuration and functionality through a virtual machine-based lab environment.
This document provides a summary of the Top 100 Tools for Learning in 2011 as compiled by Jane Hart from the Centre for Learning & Performance Technologies. 531 learning professionals shared their top 10 tools, from which the Top 100 list was created. The list includes tools such as Twitter, YouTube, Google Docs, Skype, WordPress, Dropbox, and Prezi. Jane Hart is the founder of the Centre for Learning & Performance Technologies and an independent consultant who writes and speaks about learning tools.
This document provides a guide for optimizing the performance of Analysis Services multidimensional models from both a development and operational perspective. It covers best practices for cube design, testing, and tuning query and processing performance. The guide is split into two parts - Part 1 focuses on building high-performance cubes through techniques like dimension design, aggregations, and efficient MDX; Part 2 addresses running cubes in production through server configuration, monitoring, maintenance and diagnostics. The document aims to help BI developers and operations specialists optimize Analysis Services solutions.
This document provides a developer's introduction to writing queries for Microsoft StreamInsight, an event processing engine. It outlines a 5-step process for developing StreamInsight queries: 1) model input and output events, 2) understand required query semantics by building sample tables, 3) gather query logic elements, 4) compose the query, and 5) specify timeliness of output. The document walks through a toll booth monitoring example, defining an input stream of vehicle passage events and a query to count vehicles every 3 minutes. Code examples and explanations demonstrate how to program a basic StreamInsight application.
This document provides guidance on developing modern mobile web apps using HTML5, CSS3, and JavaScript. It discusses choosing between native, web, and hybrid platforms and determining which browsers and devices to support. It also covers delivering mobile-friendly markup, forms, images and responsive design. The goal is to support various mobile browsers while keeping code simple to ensure compatibility across many devices.
This document provides guidance on developing modern mobile web apps using HTML5, CSS3, and JavaScript. It discusses choosing between native, web, and hybrid platforms and defining a mobile-friendly experience. It also covers determining device support, delivering mobile-friendly markup, forms, images and responsive design. The goal is to support various mobile browsers while keeping code simple to ensure compatibility across devices.
Microsoft SLQ Server 2014 - Faster Insights from Any data - Technical White ...David J Rosenthal
This document summarizes new features in SQL Server 2014 that provide faster insights from data. It discusses enhanced self-service BI capabilities in Excel including Power View, Power Query, and Power Map. It also covers managed self-service BI where IT can govern user-generated content. Additionally, it outlines tools for ensuring credible, consistent data and analyzing both structured and unstructured big data through SQL Server and Hadoop integration.
IT Auditing, Second Edition Reviews This guidance will .docxchristiandean12115
This document contains reviews and endorsements of the book "IT Auditing, Second Edition" from IT professionals in various industries. It provides positive feedback on the book's ability to guide auditors in effectively auditing today's complex computing environments, including new areas like cloud computing. The reviews praise its comprehensive yet easy-to-follow approach and valuable insights and recommendations for addressing challenges in auditing distributed cloud-based systems.
The document discusses how big data and machine learning are contributing to rapid changes in the world. It provides examples of how industries like lending, education, insurance, and retail have been disrupted by new business models enabled by technologies like mobile, social media, cloud computing, and the internet of things. The rise of startups exploiting big data through applications of machine learning like recommendation engines, image recognition, and autonomous vehicles is also covered. Finally, the document presents an approach for enterprises to harvest big data through a data platform that enables descriptive, advanced, and streaming analytics.
This document provides a framework for health organizations in Europe to assess privacy and security risks when adopting cloud services. It discusses relevant legislation and international standards, as well as traditional and cloud-related security risks. The framework consists of four steps: identify organizational assets, assess vulnerabilities and threats, implement controls, and monitor risks. It also describes Microsoft's approach to privacy, security, and shared responsibility in the cloud.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
This document summarizes an electronic library called "Librus" located at http://librus.ru. It began as a repository for computer literature but now contains books on various subjects like medicine, technology, humanities, home, and education. It aims to introduce readers to a variety of publications and help them choose quality books to purchase legally from publishers and retailers. The library warns that files are for preview only and must be deleted, as distributing copyrighted content would be illegal. It invites authors and publishers to advertise and promote their books through inclusion in the library's catalog.
RUNNING HEADER: Analytics Ecosystem 1
Analytics Ecosystem 4
Analytics Ecosystem
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course.
This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor.
They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012).
While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012).
They can be represented as follows:-
References
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics.
Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008.
Infrastructure
Executive Sponsor Cluster
End-user tools and dashboards cluster
operational data stores
Data Owners Cluster
Business users' cluster
Business systems and applications cluster
Developers Cluster
Analysts Cluster
SME cluster
4
Running head: Sentiment analysis
Sentiment Analysis
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course.
Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence, whereby the end users of given particulars the feedback pertaining a used.
Enterprise Social Collaboration Progression ModelMicrosoft
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This document describes the registration process for a free membership to a solutions program run by Syngress publishing. The summary is:
1. Syngress has published several best-selling books on topics like ISA Server 2000 and intrusion detection and one reason for their success is the solutions program.
2. As a registered owner of this Syngress book, the reader can qualify for free access to the member-only solutions program which provides downloadable e-books, an FAQ page, and a forum for interaction with the book authors.
3. To register for the free membership, the reader needs to visit the listed website and go through a simple registration process using the book.
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product on Amazon.com results in the retailer also suggesting similar products that a customer might be interested in. Predictive analytics can be used in E-commerce to solve the following problems
1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
Fundamentals of Database Systems questions and answers with explanation for fresher's and experienced for interview, competitive examination and entrance test.
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11.
Microsoft SQL Server AlwaysOn Solutions Guide for High Availability and Disaster Recovery 6
Rolling Upgrade and Patching.AlwaysOn features facilitate rolling upgrades and patching of
instances, which helps significantly to reduce application downtime.
SQL Server on Hyper‐V.SQL Server instances hosted in the Hyper‐V environment receive the
additional benefit of Live Migration, which enables you to migrate virtual machines between hosts
with zero downtime. Administrators can perform maintenance operations on the host without
impacting applications.
Eliminate Idle Hardware and Improve Cost Efficiency and Performance
Typical high availability solutions involve deployment of costly, redundant, passive servers.AlwaysOn
Availability Groups enable you to utilize secondary database replicas on otherwise passive or idle servers
for read‐only workloads such as SQL Server Reporting Services report queries or backup operations.The
ability to simultaneously utilize both the primary and secondary database replicas helps improve
performance of all workloads due to better resource balancing across your server hardware
investments.
Easy Deployment and Management
Features such as the Configuration Wizard, support for the Windows PowerShell command‐line
interface, dashboards, dynamic management views (DMVs), policy‐based management, and System
Center integration help simplifydeployment and management of availability groups.
Contrasting RPO and RTO Capabilities
The business goals for Recovery Point Objective (RPO) and Recovery Time Objective (RTO)should be key
drivers in selecting a SQL Server technology for your high availability and disaster recovery solution.
Thistable offers a rough comparison of the type of results that those different solutions may achieve:
High Availability and Disaster Recovery
SQL Server Solution
Potential
Data Loss
(RPO)
Potential
Recovery
Time (RTO)
Automatic
Failover
Readable
Secondaries(1)
AlwaysOn Availability Group‐ synchronous‐commit
Zero Seconds Yes
(4)
0 ‐ 2
AlwaysOn Availability Group‐ asynchronous‐commit
Seconds Minutes No 0 ‐ 4
AlwaysOn Failover Cluster Instance NA
(5)
Seconds
‐to‐minutes
Yes NA
Database Mirroring(2)
‐ High‐safety (sync + witness)
Zero Seconds Yes NA
Database Mirroring(2)
‐ High‐performance (async)
Seconds
(6)
Minutes
(6)
No NA
Log Shipping Minutes
(6)
Minutes
‐to‐hours(6)
No Not during
a restore
Backup, Copy, Restore(3)
Hours
(6)
Hours
‐to‐days
(6)
No Not during
a restore
(1)
An AlwaysOn Availability Group can have no more than a total of four secondary replicas, regardless of type.
(2)
This feature will be removed in a future version of Microsoft SQL Server. Use AlwaysOn Availability Groups instead.
(3)
Backup, Copy, Restore is appropriate for disaster recovery, but not for high availability.
(4)
Automatic failover of an availability group is not supported to or from a failover cluster instance.
(5)
The FCI itself doesn’t provide data protection; data loss is dependent upon the storage system implementation.
(6)
Highly dependent upon the workload, data volume, and failover procedures.
15.
Microsoft SQL Server AlwaysOn Solutions Guide for High Availability and Disaster Recovery 10
SQL Server AlwaysOn solutions both leverage and are restricted to certain WSFC storage configuration
combinations, including:
Direct‐attached vs. remote.Storage devices are directly physically attached to the server, or they
are presented by a remote device through a network or host bus adaptor (HBA).Remote storage
technologies include Storage Area Network (SAN) based solutions such as iSCSI or Fibre Channel, as
well as Server Messaging Block (SMB) file share based solutions.
Symmetric vs. asymmetric.Storage devices are considered symmetric if exactly the same logical disk
volume configuration and file paths are presented to each node in the cluster. The physical
implementation and capacity of the underlying disk volumes can vary.
Dedicated vs. shared.Dedicated storage is reserved for use and assigned to a single node in the
cluster.Shared storage is accessible to multiple nodes in the cluster. Control and ownership of
compliant shared storage devices can be transferred from one node to another using SCSI‐3
protocols.WSFC supports the concurrent multi‐node hosting of cluster shared volumes for file
sharing purposes.However, SQL Server does not support concurrent multi‐node access to a shared
volume.
Note:SQL Server FCIs still require symmetrical shared storage to be accessible by all possible node
owners of the instance.However, with the introduction of AlwaysOn Availability Groups, you can now
deploy different non‐FCI instances of SQL Server in a WSFC cluster, each with its own unique, dedicated,
local or remote storage.
WSFC Resource Health Detection and Failover
Each resource in a WSFC cluster node can report its status and health, periodically or on‐demand.A
variety of circumstances may indicate a cluster resource failure, including: power failure, disk or memory
errors, network communication errors, misconfiguration, or nonresponsive services.
You can make WSFC cluster resources such as networks, storage, or services dependent upon one
another. The cumulative health of a resource is determined by successive rollup of its health with the
health of each of its resource dependencies.
For AlwaysOn Availability Groups, the availability group and the availability group listener are registered
as WSFC cluster resources.For AlwaysOn Failover Cluster Instances, the SQL Server service and the SQL
Server Agent service are registered as WSFC cluster resources, and both are made dependent upon the
instance’s virtual network name resource.
If a WSFC cluster resource experiences a set number of errors or failures over a period of time, the
configured failover policy causes the cluster service to do one of the following:
Restart the resource on the current node.
Set the resource offline.
Initiate an automatic failover of the resource and its dependencies to another node.
19.
Microsoft SQL Server AlwaysOn Solutions Guide for High Availability and Disaster Recovery 14
Recommended Adjustments to Quorum Voting
To determine the recommended quorum voting configuration for the cluster, apply these guidelines, in
sequential order:
1. No vote by default. Assume that each node should not vote without explicit justification.
2. Include all primary nodes.Each node that hosts an AlwaysOn Availability Group primary replica or is
the preferred owner of the AlwaysOn Failover Cluster Instance should have a vote.
3. Include possible automatic failover owners.Each node that could host a primary replica or FCI, as
the result of an automatic failover, should have a vote.
4. Exclude secondary site nodes.In general, do not give votes to nodes that reside at a secondary
disaster recovery site.You do not want nodes in the secondary site to contribute to a decision to
take the cluster offline when there is nothing wrong with the primary site.
5. Odd number of votes.If necessary, add a witness file share, a witness node (with or without a SQL
Server instance), or a witness disk to the cluster and adjust the quorum mode to prevent possible
ties in the quorum vote.
6. Reassess vote assignments post‐failover.You do not want to fail over into a cluster configuration
that does not support a healthy quorum.
For more information on adjusting node votes, see Configure Cluster Quorum NodeWeight
Settings(http://msdn.microsoft.com/en‐us/library/hh270281(SQL.110).aspx).
You cannot adjust the vote of a file share witness. Instead, you must select a different quorum mode to
include or exclude its vote.
Note:SQL Server exposes several system dynamic management views (DMVs) that can help you
administer settings related WSFC cluster configuration and node quorum voting.
For more information, seeMonitor Availability Groups(http://msdn.microsoft.com/en‐
us/library/ff878305(SQL.110).aspx).
24.
Microsoft SQL Server AlwaysOn Solutions Guide for High Availability and Disaster Recovery 19
5) SQL Server is started on the new node.The SQL Server instance goes through its normal startup
procedures.If it does not come back online within a pending timeout period, the cluster service puts
the resource on this new node in a failed state.
6) User databases are recovered on the new node.Each user database is placed in recovery mode
while transaction log redo operations are applied and uncommitted transactions are rolled back.
FCI Improvements
Previous versions of SQL Server have offered a FCI installation option; however, several feature
enhancements in SQL Server 2012 improve availability robustness and serviceability:
Multi‐subnet clustering.SQL Server 2012 supports WSFC cluster nodes that reside in more than one
subnet.A given SQL Server instancethat resides on a WSFC cluster node can start if any network
interface is available; this is known as an ‘OR’ cluster resource dependency.
Prior versions of SQL Server required that all network interfaces be functional for the SQL Server
service to start or failover, and that they all existon the same subnet or VLAN.
Note:Storage‐level replication between cluster nodes is not implicitly enabled with multi‐subnet
clustering.Your multi‐subnet FCIsolution must leverage a third‐party SAN‐based solution to replicate
data and coordinate storage failover between cluster nodes.
For more information, seeSQL Server 2012 AlwaysOn: Multisite Failover Cluster
Instance(http://sqlcat.com/sqlcat/b/whitepapers/archive/2011/12/22/sql‐server‐2012‐
alwayson_3a00_‐multisite‐failover‐cluster‐instance.aspx).
Robust failure detection.The WSFC cluster service maintains a dedicated administrative connection
to each SQL Server 2012 FCI on the node.On this connection, a periodical call to a special system
stored procedure, sp_server_diagnostics, returns a rich array of system health diagnostic
information.
Prior to SQL Server 2012, the primary health detection mechanism for a FCI was implemented as a
simple one‐way polling process.In this process, the WSFC cluster service periodically created a new
SQL client connection to the instance, queried the server name, and then disconnected.A failure to
connect, or a query timeout, for whatever reason, triggered a failover with very little available
diagnostic information.
For more information, seesql_server_diagnostics (http://msdn.microsoft.com/en‐
us/library/ff878233(SQL.110).aspx).
There is now broader support for FCI storage scenarios:
Better mount point support.SQL Server setup now recognizes cluster disk mount point settings.The
specified cluster disks and all disks mounted to it areautomatically added to the SQL Server resource
dependency during setup.
tempdb on local storage.FCIs now support placement of tempdbon local non‐shared storage, such
as a local solid‐state‐drive, potentially offloading a significant amount of I/O from a shared SAN.
29.
Microsoft SQL Server AlwaysOn Solutions Guide for High Availability and Disaster Recovery 24
You must perform a manual failover if any of the following conditions are true about either the primary
replica or the secondary replica that you want to fail over to:
Failover mode is set to manual.
Availability mode is set to asynchronous‐commit.
Replica resides on an FCI.
For more information, seeFailover Modes (AlwaysOn Availability
Groups)(http://msdn.microsoft.com/en‐us/library/hh213151(SQL.110).aspx).
Note: After a failover, if the new primary replica is not set to the synchronous‐commit mode, the
secondary replicas will indicate a ‘Suspended’ synchronization state. No data will flow to the secondary
replicas until the primary replica is set to synchronous‐commit mode.
Availability Group Listener
An availability group listener is a WSFC virtual network name (VNN) that clients can use to access a
database in the availability group.The VNN cluster resource is owned by the SQL Server instance on
which the primary replica resides.
The virtual network name is registered with DNS only during availability group listener creation or during
configuration changes.All virtual IP addresses that are defined in the availability group listener are
registered with DNS under the same virtual network name.
To use the availability group listener, a client connection request must specify the virtual network name
as the server, and a database name that is in the availability group.By default, this should result in a
connection to the SQL Server instance that is hosting the primary replica.
At runtime, the client uses its local DNS resolver to get a list of IP addresses and TCP ports that map to
the virtual network name.The client then attempts to connect to each of the IP addresses, until it is
successful, or until it reaches the connection timeout. The client will attempt to make these connections
in parallel if the MultiSubnetFailover parameter is set to true, enabling much faster client failovers.
In the event of a failover, client connectionsarereset on the server, ownership of the availability group
listener moves with the primary replica role to a new SQL Server instance, and the VNN endpoint is
bound to the new instance’s virtual IP addresses and TCP ports.
For more information, seeClient Connectivity and Application Failover(http://msdn.microsoft.com/en‐
us/library/hh213417(SQL.110).aspx).