Install SQL Server 2017 Developer (Free)

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Install SQL Server 2017 for Windows












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Enable logging for package execution in SSIS

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Logging can be enabled and used at design time and runtime. To begin logging, in the Visual  Studio menu select SSIS > Logging to open the Configure SSIS Logs window. In  the Containers section, select the Control Flow objects for logging—the overall package itself  and/or each of the individual tasks within the package. Then in the Providers and Logs tab,  under the Add A New Log > Provider Type, select where you want the logging to be saved to,  and click the Add button: After adding each of the Provider Types selected you want to log to, go to the Select the  Logs to Use for the Container section to set up the Connection in the Configuration column.  In the case of a Text file, select a file to write to. For SQL Server DB as the target,  simply select the target database. You are not limited to selecting only one target location for  the logging, multiple destinations can be added and they will all be wri...
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Non-blocking, semi-blocking, and blocking transforms in SSIS

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Not all Data Flow transforms are created equal. In fact, some are much faster and require a  lot less memory (non-blocking transforms). Others are slower while consuming much more  memory (blocking transforms), and some are in between (semi-blocking transforms). We don’t  always have a choice if we need to use a certain transform to do a certain specified task, but  it is good to know the differences between these types of transforms so that we can better  understand performance and resource utilization issues. Synchronous and asynchronous There is another related aspect about Data Flow transforms, which is their ability to quickly  process a row as they are coming into the transform, independently of any other rows that  came before or after (synchronous transforms). The other type of transform needs to be  dependent on some or all of the rows that come before and after (asynchronous transforms). On the whole, non-blocking transforms a...
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Perform knowledge discovery in DQS

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The Data Quality knowledge base can be populated using interactive or computer-assisted  processes. During the installation process of SQL Server 2016 Data Quality Services, you can  choose to install the Data Quality Client to interactively create and maintain a DQS knowledge  base. The DQS Client can be used to create data domains and add domain values manually or  by importing them from an Excel spreadsheet or a data cleansing project. In addition to interactively maintaining data through the DQS Client, you can maintain data  by running a computer-assisted activity known as Knowledge Discovery. The Knowledge Discovery  activity analyzes a sample of data that is used for data quality criteria. The algorithms  built into DQS look for data inconsistencies and syntax errors and then propose changes to the  data. You can then approve or reject the proposed changes or apply corrections manually. To perform a Knowledge Discovery activity for...
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