Microsoft SQL Server 2008 com fim do suporte em Julho/2016

O produto Microsoft SQL Server 2008 já não tem mais suporte, conforme abaixo:

• Microsoft SQL Server 2008 – Julho/2016 fim do suporte
• Microsoft SQL Server 2008 R2 – Julho/2016 fim do suporte

Apesar disso, eu notei em 2016/2017 em mensagens dentro dos fórums técnicos da Microsoft que ainda existe vários técnicos e várias empresas utilizando o Microsoft SQL Server 2008, mesmo estando com o suporte técnico finalizado desde Julho/2016.

Quem ainda não fêz o upgrade do Microsoft SQL Server 2008 para o novo Microsoft SQL Server 2016, o procedimento para conseguir transferir os dados com segurança é:

1º Passo) No servidor com o Microsoft SQL Server 2008 instalado, tem que fazer um backup total da base de dados e guardar os arquivos do backup em um HD externo USB.

2º Passo) Anotar a configuração de memória mínima e memória máxima e anotar a configuração de páginação de idioma, que está configurado, e sendo utilizado pelo Microsoft SQL Server 2008.

3º Passo) Comprar uma máquina nova, para uso como novo servidor do banco de dados, paraq uso com os seguintes processadores (Quad Core com 4 núcleos de processamento ou Octa Core com 8 núcleos de processamento): 

• motherboard para uso com o processador Intel XEON E5 (Quad Core)
• motherboard para uso com o  processador Intel XEON E7 (Quad Core)
• motherboard para uso com o processador AMD Quad Core
• motherboard para uso com o processador AMD Octa Core
• motherboard para uso com o processador Intel Core i5 Quad Core 
• motherboard para uso com o processador Intel Core i7 Quad Core

4º Passo) Instalar nesse novo servidor o Microsoft Windows Server 2016 Essentials, e instalar o Microsoft SQL Server 2016, depois disso, criar uma pasta com o nome de backup e copiar os arquivos do backup que estão guardados no HD externo USB.

5º Passo) No Microsoft SQL Server 2016 copiar a configuração de memória mínima e memória máxima que estava sendo utilizado no Microsoft SQL Server 2008, e analisar se será preciso atualizar esses valores de memória mínima e memória máxima.

6º Passo) No Microsoft SQL Server 2016 tentar fazer o restauro dos arquivos do backup feito no Microsoft SQL Server 2008. Tudo estando configurado de forma correta, o upgrade do banco de dados irá funcionar.

Produtos que chegarão ao fim do suporte em 2017

Service Packs que chegarão ao fim do suporte	Data de efetivação
Microsoft SQL Server 2012 Service Pack 2	10 de janeiro de 2017
Microsoft SQL Server 2014 Service Pack 1	10 de outubro de 2017

Produtos que chegarão ao fim do suporte: os produtos a seguir chegarão ao fim do suporte em 2017. Não haverá novas atualizações de segurança, atualizações não relacionadas à segurança, opções de suporte assistido pagas ou gratuitas, nem atualizações de conteúdo técnico online.

Produtos que chegarão ao fim do suporte	Data de efetivação
Microsoft Excel para Mac 2011  Microsoft Lync para Mac 2011  Microsoft Office for Mac 2011  Microsoft Outlook para Mac 2011  Microsoft PowerPoint para Mac 2011  Microsoft Word para Mac 2011 ---------------------------------------------------------------------------------------	10 de outubro de 2017
Microsoft Customer Care Framework 2005 .NET 2.0 Edition Microsoft Internet Security and Acceleration Server 2006 Microsoft Voice Command 1.6 ---------------------------------------------------------------------------------------	10 de janeiro de 2017
Microsoft BizTalk Adapters for Host Systems Microsoft BizTalk FileAct and InterAct Adapters for SWIFT Microsoft Dynamics NAV 5.0 Microsoft Exchange Server 2007 Microsoft Expression Web Microsoft Host Integration Server 2006 Microsoft Office Communicator Phone Edition Microsoft Office InterConnect 2007 Microsoft Visual Studio 2005 Team Edition for Database Professionals Windows Vista -----------------------------------------------------------------------------------------	11 de abril de 2017
Engyro Product Connectors for Microsoft System Center Operations Manager 2007 Microsoft Identity Lifecycle Manager 2007 Microsoft Integration Designer 2.5 Microsoft Intelligent Application Gateway 2007 Microsoft ProClarity Analytics Server 6.3 Microsoft ProClarity Desktop Professional 6.3 Microsoft ProClarity SharePoint Viewer 6.3 Microsoft SoftGrid Application Virtualization 4.1 for Desktops Microsoft SoftGrid Application Virtualization 4.2 for Desktops Microsoft SoftGrid Application Virtualization for Terminal Services 4.1 Microsoft Virtual PC 2007 Microsoft Visual SourceSafe 2005 Standard Edition ----------------------------------------------------------------------------------------	11 de julho de 2017
2007 Microsoft Office Servers 2007 Microsoft Office Suite Microsoft Customer Care Framework 2008 Microsoft Dynamics GP 10.0 Microsoft Dynamics SL 7.0 Microsoft Expression BlendMicrosoft expressão Design Microsoft Expression MediaMicrosoft expressão Studio Microsoft Office Groove 2007 Microsoft Office Groove Server 2007 Microsoft Office Project 2007 Microsoft Office Project Server 2007 Microsoft Office Project Portfolio Server 2007 Microsoft Office SharePoint Designer 2007 Microsoft Office Visio 2007 Microsoft SharePoint Server 2007 Microsoft Visual J# Version 2.0 Redistributable Package Second Edition Microsoft Windows SharePoint Services 3.0	10 de outubro de 2017

HTML com UTF-8-pt-br (CGI-Bin)

Um servidor com o Microsoft Windows Server e o IIS (Internet Information Services) instalado, nele vem como padrão de instalação reconhecer o texto do HTML no idioma inglês. 

Recentemente, tive problemas de meu código HTML parar de reconhecer os acentos utilizados do idioma português-brasileiro.  Tentei de tudo, e nada fazia os acentos voltar a funcionar. Pesquisei em dois livros velhos de HTML da década de 80, e lá tinha um capitulo falando de CGI, então eu resolvi fazer um arquivo CGI para testar, e funcionou, a acentuação das palavras voltaram a funcionar perfeito novamente no HTML.

Caso algum de vocês passem pelo mesmo problema que eu, podem testar esta solução.

Esse arquivo paginacao.cgi tem que ser feito o upload para dentro do servidor web.

Criei um arquivo CGI-Bin chamado paginacao.cgi com o seguinte conteúdo abaixo, ele funcionou perfeito:

#!/bin/sh
Content-type: text/html
Language: pt-br
Charset: UTF-8-pt-br

========================================================================

Translation:

A server that has Microsoft Windows Server and Internet Information Services (IIS) installed, it is by default that the installation recognizes English-language HTML text.

I recently had problems with my HTML code failing to recognize the accents used in the Portuguese-Brazilian language. I tried everything, and nothing made the accents work again. I researched two old HTML books from the 80's, and there was a chapter talking about CGI, so I decided to make a CGI file to test, and it worked, the accentuation of the words came back perfect working again in HTML.

If any of you go through the same problem as me, you can test this solution.

This paginacao.cgi file has to be uploaded to the web server.

I created a CGI-Bin file called paginacao.cgi with the following content below, it worked perfect:

#! / Bin / sh

Content-type: text / html

Language: pt-br

Charset: UTF-8-pt-br

========================================================================

IIS configuration for UTF-8-pt-br (CGI-Bin) 

https://social.technet.microsoft.com/wiki/pt-br/contents/articles/37754.iis-configuration-for-utf-8-pt-br-cgi-bin.aspx

========================================================================

Por Ana Mercedes Gauna (18/04/2017)
Senior Business Systems Analyst
Twitter: @amgauna @AnaGaunaTech
WebSite: http://www.amgauna.eti.br
Blog: http://anagaunatech.blogspot.com
Blog: anagauna.wordpress.com

Data Science (Books)

Bibliography Recommendation Data Science

 Gregory Piatetsky-Shapiro (Analytics, Data Mining, Data Science Expert, KDnuggets President) em More Free Data Mining, Data Science Books and Resources

The list below based on the list compiled by Pedro Martins, but we added the book authors and year, sorted alphabetically by title, fixed spelling, and removed the links that did not work.

1. An Introduction to Data Science by Jeffrey Stanton, Robert De Graaf, 2013.

1. An introductory level resource developed by Syracuse University

1. An Introduction to Statistical Learning: with Applications in R by G. Casella, S, Fienberg, I Olkin, 2013.

1. Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.

1. A Programmer’s Guide to Data Mining by Ron Zacharski, 2012.

1. A guide through data mining concepts in a programming point of view. It provides several hands-on problems to practice and test the subjects taught on this online book.

1. Bayesian Reasoning and Machine Learning by David Barber, 2012.

1. focusing on applying it to machine learning algorithms and processes. It is a hands-on resource, great to absorb all the knowledge in the book.

1. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners by Jared Dean, 2014.

1. On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to storage these kind of data and algorithms to process it, based on data mining and machine learning.

1. Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014.

1. A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.

1. Data Mining and Business Analytics with R by Johannes Ledolter, 2013.

1. Another R based book describing all processes and implementations to explore, transform and store information. It also focus on the concept of Business Analytics.

1. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J.A. Berry, Gordon S. Linoff, 2004.

1. A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world.

1. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery by Graham Williams, 2011.

1. The objective of this book is to provide you lots of information on data manipulation. It focus on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.

1. Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams, 2006.

1. This is a theoretical book approaching learning algorithms based on probabilistic Gaussian processes. It’s about supervised learning problems, describing models and solutions related to machine learning.

• Data Science by Analyticbridge (internal to DSC, one of the first books about data science)

• Data Science 2.0 (internal to DSC, check the red-starred articles)

• 27 free Data Mining books

1. An Introduction to Data Science (Jeffrey Stanton, 2013)

• School of Data Handbook (2015)

• Data Jujitsu: The Art of Turning Data into Product (DJ Patil, 2012)

• Art of Data Science (Roger D. Peng & Elizabeth Matsui, 2015)
• 
  

Distributed Computing Tools

• Hadoop: The Definitive Guide (Tom White, 2011)

• Data-Intensive Text Processing with MapReduce (Jimmy Lin & Chris Dyer, 2010)
• 
  

Data Mining and Machine Learning

• Introduction to Machine Learning (Amnon Shashua, 2008)

• Machine Learning (Abdelhamid Mellouk & Abdennacer Chebira)

• Machine Learning — The Complete Guide (Wikipedia)

• Social Media Mining An Introduction (Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014)

• Data Mining: Practical Machine Learning Tools and Techniques (Ian H. Witten & Eibe Frank, 2005)

• Mining of Massive Datasets (Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014)

• A Programmer’s Guide to Data Mining (Ron Zacharski, 2015)

• Data Mining with Rattle and R (Graham Williams, 2011)

• Data Mining and Analysis: Fundamental Concepts and Algorithms(Mohammed J. Zaki & Wagner Meria Jr., 2014)

• Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Goo…(Matthew A. Russell, 2014)

• Probabilistic Programming & Bayesian Methods for Hackers (Cam Davidson-Pilon, 2015)

• Data Mining Techniques For Marketing, Sales, and Customer Relations…(Michael J.A. Berry & Gordon S. Linoff, 2004)

• Inductive Logic Programming: Techniques and Applications (Nada Lavrac & Saso Dzeroski, 1994)

• Pattern Recognition and Machine Learning (Christopher M. Bishop, 2006)

• Machine Learning, Neural and Statistical Classification (D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999)

• Information Theory, Inference, and Learning Algorithms (David J.C. MacKay, 2005)

• Data Mining and Business Analytics with R (Johannes Ledolter, 2013)

• Bayesian Reasoning and Machine Learning (David Barber, 2014)

• Gaussian Processes for Machine Learning (C. E. Rasmussen & C. K. I. Williams, 2006)

• Reinforcement Learning: An Introduction (Richard S. Sutton & Andrew G. Barto, 2012)

• Algorithms for Reinforcement Learning (Csaba Szepesvari, 2009)

• Big Data, Data Mining, and Machine Learning (Jared Dean, 2014)

• Modeling With Data (Ben Klemens, 2008)

• KB — Neural Data Mining with Python Sources (Roberto Bello, 2013)

• Deep Learning (Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015)

• Neural Networks and Deep Learning (Michael Nielsen, 2015)

• Data Mining Algorithms In R (Wikibooks, 2014)

• Data Mining and Analysis: Fundamental Concepts and Algorithms(Mohammed J. Zaki & Wagner Meira Jr., 2014)

• Theory and Applications for Advanced Text Mining (Shigeaki Sakurai, 2012)

• DSC Resources

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Read the full post on KDnuggets: http://www.kdnuggets.com/2015/03/free-data-mining-data-science-books-resources.html

Gregory Piatetsky-Shapiro (Analytics, Data Mining, Data Science Expert, KDnuggets President)

=======================================

Recomendação de Bibliografia Data Science = Kirk Borne‏@KirkDBorne
Download 50+ Free #DataScienceBooks:http://bit.ly/1Or1j5Z #abdsc#BigData #Analytics

Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors — usually academics — are more prone to making their books available for free). Many are O’Reilly books freely available. Here we display those most relevant to data science. I haven’t checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below. Note that at DSC, we also have our free books: