815 search results for "SQL"

Mango at SQL Relay!

October 22, 2015
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Mango at SQL Relay!

By Steph Locke SQL Relay is an annual conference around SQL Server and related tech. It hits six to ten cities in the space of a fortnight, touring the country offering free and awesome training on SQL Server, Business Intelligence … Continue reading →

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A preview of using Revolution R Enterprise inside SQL Server

October 14, 2015
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by Andrie de Vries The second week of SQLRelay (#SQLRelay) kicked off in London earlier this week. SQLRelay is a series of conferences, spanning 10 cities in the United Kingdom over two weeks. The London agenda included 4 different streams, with tracks for the DBA, BI and Analytics users, as well as a workshop track with two separate tutorials....

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Treasure Trove of R Scripts for Auto Classification, Chart Generation, Solr, Mongo, MySQL and Ton More

October 7, 2015
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In this repository hosted at github, the datadolph.in team is sharing all of the R codebase that it developed to analyze large quantities of data.datadolph.in team has benefited tremendously from fellow R bloggers and other open source communities and ...

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Connecting R to MySQL/MariaDB

September 2, 2015
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Connecting R to MySQL/MariaDB

Overview Relational databases, such as MySQL, organize data into tables consisting of rows and columns similarly to an R data.frame. While many beginners will be satisfied with R’s native RData storage format, storing data in a flat-file has its disadvantages. First, they are difficult to share among collaborators. .RData files can contain many matrices and

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Using Azure as an R datasource, Part 4 – Pulling data from SQL Server to Linux

August 18, 2015
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Using Azure as an R datasource, Part 4 – Pulling data from SQL Server to Linux

by Gregory Vandenbrouck Software Engineer, Microsoft This post is the fourth in a series that covers pulling data from Microsoft SQL Server or MySQL/MariaDB on Azure to an R client on Windows or Linux. In the previous posts, we covered pulling data from SQL Server to Windows and from MySQL/MariaDB to both Windows and Linux. This time we’ll be...

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Using Azure as an R datasource Part 3: Pulling data from MySQL/MariaDB to Linux

July 21, 2015
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Using Azure as an R datasource Part 3: Pulling data from MySQL/MariaDB to Linux

by Gregory Vandenbrouck Software Engineer, Microsoft This post is the third in a series that covers pulling data from Microsoft SQL Server or MySQL/MariaDB on Azure to an R client on Windows or Linux. In the previous two, we covered pulling data from SQL Server and MySQL/MariaDB to an R client on Windows. This time we’ll be pulling data...

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Working with MS SQL server on non-windows systems

July 15, 2015
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As I know, there are few choices to connect from R to MS SQL Server: RODBC RJDBC rsqlserver But only second option can be used on mac and linux machines. Here is nice stackoverflow thread. Most of the people suggest to use microsoft sql java driver. But there is a case when this will not...

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Using Azure as an R datasource: Part 2 – Pulling data from MySQL/MariaDB

June 2, 2015
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Using Azure as an R datasource: Part 2 – Pulling data from MySQL/MariaDB

by Gregory Vandenbrouck Software Engineer, Mirosoft This post is the second in a series that covers pulling data from various Windows Azure hosted storage solutions (such as MySQL, or Microsoft SQL Server) to an R client on Windows or Linux. Last time we covered pulling data from SQL Azure to an R client on Windows. This time we’ll be...

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Getting started with PostgreSQL in R

May 18, 2015
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Getting started with PostgreSQL in R

When dealing with large datasets that potentially exceed the memory of your machine it is nice to have another possibility such as your own server with an SQL/PostgreSQL database on it, where you can query the data in smaller digestible chunks. For example, recently I was facing a financial dataset of 5 GB. Although 5

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Getting started with PostgreSQL in R

May 18, 2015
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Getting started with PostgreSQL in R

When dealing with large datasets that potentially exceed the memory of your machine it is nice to have another possibility such as your own server with an SQL/PostgreSQL database on it, where you can query the data in smaller digestible chunks. For example, recently I was facing a financial dataset of 5 GB. Although 5

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