For example, even if you just want to quickly explore your data in a file or remote data source, you need to create catalog objects to schematize file data or remote sources before you can query them, which reduces your agility. However, their extensibility model and support for non-structured data and files are often bolted on and harder to use. This makes them easy to use, familiar to a wide range of developers, and powerful for many standard types of analytics and warehousing. SQL-based languages (such as Hive and others) provide you with a declarative approach that natively does the scaling, parallel execution, and optimizations for you. How do existing Big Data languages stack up to these requirements? Scale efficiently to any size of data without you focusing on scale-out topologies, plumbing code, or limitations of a specific distributed infrastructure.The example scenarios above may all require custom processing that is often not easily expressed in standard query languages, ranging from user defined functions, to custom input and output formats.
#Microsoft sql studio code#
Use custom code easily to express your complex, often proprietary business algorithms.From analyzing BotNet attack patterns from security logs to extracting features from images and videos for machine learning, the language needs to enable you to work on any data. If you analyze the characteristics of Big Data analytics, several requirements arise naturally for an easy to use, yet powerful language: In this blog post I will outline the motivation for U-SQL, some of our inspiration, and design philosophy behind the language, and show you a few examples of the major aspects of the language.
U-SQL’s scalable distributed query capability enables you to efficiently analyze data in the store and across relational stores such as Azure SQL Database. Azure Data Lake Analytics includes U-SQL, a language that unifies the benefits of SQL with the expressive power of your own code.
#Microsoft sql studio archive#
You can now send it to our support team (please remember that you can create ZIP archive to reduce the file size).Microsoft announced the new Azure Data Lake services for analytics in the cloud that includes a hyper-scale repository, a new analytics service built on YARN that allows data developers and data scientists to analyze all data, and HDInsight, a fully managed Hadoop, Spark, Storm and HBase service. Congratulations! You have just created SQL dump file.Please go to location specified in settings and check if the file is there.Click Finish button to close the dialog window. Once it is completed you should see a confirmation screen similar to the one below. This operation may take a while (depends on the table size). If you are happy with the settings – click Next button. You can just check if all options are correct. There is no need to set/change anything here. Now click OK to accept the settings and click Next to continue. In Advanced Scripting Options window scroll down and find ‘ Types of data to script’ option and set it to ‘Schema and data’.Set name and path for the file and click Advanced button. Please make sure that your settings are the same as on the image below. Now you need to specify all settings for the file that you want to generate.if you are happy with the settings – click Next button.find desired table on the list and tick the checkbox next to table name.click small + icon next to Tables to expand tables list.click Select specific database object option.If you want to export just one table please do the following steps: This is a Select the databases object to script screen.Please click Next button on the initial screen. Now you should see Generate and Publish Scripts tool dialog window.Now right-click on the database and in context menu please go to Tasks section and select Generate Scripts option.Please find the database on the list which store the table that you want to export. Once you are connected you will see a list of all databases (on the left panel).To create SQL dump of desired table please follow these steps.
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