Date slider power bi Hedensted

Date: 2017-04-28 10:32

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There are a number of options for using Azure for free.  This is very useful for those of you who are not familiar with Azure and want to 8775 play 8776 with it:

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Data Migration Assistant (DMA) enables you to upgrade to a modern data platform by detecting compatibility issues that can impact database functionality on your new version of SQL Server and Azure SQL Database.  It recommends performance and reliability improvements for your target environment.  It allows you to not only move your schema and data, but also uncontained objects from your source server to your target server.   Download version

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Overview: This post discusses situations when you may *not* want to load Power Query data to the worksheet, but instead to the data model only. One reason relates to file size limits in Power BI for Office 865 the other reason is to simplifying & de-duplicate the fields displayed in the Pivot Table Field List.

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Also in this category are a group of functions that calculate opening and closing balances. There are certain concepts you should understand with these particular functions. First, as you might think obvious, the opening balance for any period is the same as the closing balance for the previous period. The closing balance includes all data through the end of the period, while the opening balance does not include any data from within the current period.

As you can see, both Event Hub and IoT Hub are valuable Azure services with distinct use cases.  Event Hub is great for high throughput ingestion (only) of data from a limited number of sources (like cloud to cloud, on-prem to cloud, or intra-cloud).  In those scenarios, Event Hub will generally be appropriate and significantly cheaper than IoT Hub.  However, for IoT scenarios where interacting with the individual devices directly are involved, IoT Hub will generally be the better technology and adds a lot of features we need to be competitive.

Note that moving to the cloud requires you to think differently when it comes to loading a large amount of data, especially when using a product like SQL Data Warehouse (see Azure SQL Data Warehouse loading patterns and strategies ).

I see a lot of confusion when it comes to Azure Data Factory (ADF) and how it compares to SSIS.  It is not simply 8775 SSIS in the cloud 8776 .  See  What is Azure Data Factory?  for an overview of ADF, and I 8767 ll assume you know SSIS.  So how are they different?

Customers using P66 and P65 premium performance levels can use up to 9 TB of included storage at no additional charge.  This 9 TB option is currently in public preview in the following regions: US East 7, West US, West Europe, South East Asia, Japan East, Australia East, Canada Central, and Canada East.

SSIS is an Extract-Transfer-Load tool, but ADF is a Extract-Load Tool, as it does not do any transformations within the tool, instead those would be done by ADF calling a stored procedure on a SQL Server that does the transformation, or calling a Hive job, or a U-SQL job in Azure Data Lake Analytics, as examples.  Think of it more as an orchestration tool.  SSIS has the added benefit of doing transformations, but keep in mind the performance of any transformations depends on the power of the server that SSIS is installed on, as the data to be transformed will be pushed to that SSIS server.  Other major differences:

Perhaps the best value proposition of U-SQL is that it allows you to query data where it lives instead of having to copy all the data to one location.  For external systems, such as Azure SQL DB/DW and SQL Server in a VM, this is achieved using federated queries against those data sources where the query is 8775 pushed down 8776 to the data source and executed on that data source, with only the results being returned.

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