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07-Aug-2017 19:11

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Two categories of transformation components are available in SSIS; Synchronous and Asynchronous.Synchronous transformations are those components which process each row and push down to the next component/destination, it uses allocated buffer memory and doesn’t require additional memory as it is direct relation between input/output data row which fits completely into allocated memory.You can design a package in such a way that it can pull data from non-dependent tables or files in parallel, which will help to reduce overall ETL execution time.#2, Extract required data; pull only the required set of data from any table or file.

Additional buffer memory is required to complete the task and until the buffer memory is available it holds up the entire data in memory and blocks the transaction, also known as blocking transformation. Overall, you should avoid Asynchronous transformations but still, if you get into a situation where you don’t have any other choice then you must aware of how to deal with the available property values of these components. #4, Optimum use of event in event handlers; to track package execution progress or take any other appropriate action on a specific event, SSIS provides a set of events.Here is the setup I used: CREATE TABLE numbers ( number int not null ) GO insert into numbers (number) select number from ( select row_number() over (order by as number from sysobjects s1 cross join sysobjects s2 cross join sysobjects s3 cross join sysobjects s4 cross join sysobjects s5 ) as numbers where number between 000; GO select number from numbers where number In the OLE DB Command "Column Mappings" tab, I mapped the incoming column, new_number, to the Param_0 parameter, and the incoming column, number, to the parameter, Param_1. Map the data flow column, new_number, to the new_number field in the staging table, as shown below: Then, in the control flow, hook up an Execute SQL Task to the new Data Flow task, so that the Execute SQL Task executes after the Data Flow, as shown below: The Execute SQL Task SQL is below: Upon executing the package, the execution time shrank to under five seconds: So, the moral of this story is that whenever you think you need to use an OLE DB Command component, ask yourself, "Do I *really* need to use this component, or should I use a different approach?Extraction Transformation Load (ETL) is the backbone for any data warehouse.To complete the task SSIS engine (data flow pipeline engine) will allocate extra buffer memory, which is again an overhead to the ETL system. Events are very useful but excess use of events will cost extra overhead on ETL execution.

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Here, you need to validate all traits before enabling an event in the SSIS package.

This recommendation is built on the fact that using an OLE DB Command component ensures that your update operations are done row-by-row, rather than in a batch operation. How much faster is the batch operation compared to the OLE DB Command operation? Use the same setup as above, except replace the OLE DB Command component with an OLE DB Destination, and push the two data flow columns to a staging table.

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