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Applies aggregate functions (group by, sum, average, count, count distinct, minimum, and maximum) against column values provided by the source adapter Allows you to capture and include environmental data within the data flow With this task, you can designate additional fields to be captured during the data flow (such as the package name, version, and execution start time) and send them to the destination adapter (or as an input to the next transformation) Enables the storage of data from a connected data source into a cache connection manager This data can then be used by a Lookup transformation within a Data Flow task Allows you to apply character modifications to existing string datatype columns or to a separate copy of an existing column Modifications include changing text to all uppercase or lowercase, byte reversal, and other language conversions.

A unique number generated sequentially from the last number used. An abbreviation for the name of the account; AR for accounts receivable and OS for office supplies, for example. The full names can be looked up in a chart of accounts. The date, in international YYYY-MM-DD format, on which the transaction took place. The amount of the transaction expressed in cents, so that integer arithmetic can be used for calculations. An abbreviation for the name of the client or supplier. Information about the transaction. This can include a reference to another transaction (as when a check is received to pay off an invoice) or an invoice number for a payable amount. The date on which the transaction was added to the database. For accounts receivable, this is usually e-mailed. Other actions include cash payment, check deposited, and invoice received.

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Allows you to route data rows to different outputs based on a condition/expression you define For example, you can use this task to route all employees who belong to the East sales territory to their own table and those belonging to the West sales territory to their own table Allows you to create new columns in the output by copying input columns The new copies are added to the transformation output and can be modified without changing the original column data Modifies the datatype of the input column and copies it to an output column Used to perform prediction queries against data-mining models, using Data Mining Extensions (DMX) This transformation is available only in SQL Server Enterprise and Development editions Creates a new column by using expressions against input columns and variables The expression can be built using the original input column, variables, functions, and operators.

Somewhat abbreviated, and with tabs reduced to two spaces, here is an excerpt from the file GL.2004. The transaction records for invoices are created automatically by the same scripts that e-mail my crossword puzzles along with their invoices: 10411 10412 10413 10414 10415 10416 10417 10418 10419 AR RE PR AR AR RE OS PR AR 2004-01-01 2004-01-01 2004-01-21 2004-01-27 2004-02-01 2004-02-01 2004-02-11 2004-02-18 2004-02-24 50000 50000 50000 40000 40000 50000 1979 40000 40000 SS TL SS GT SS TL BB GT GT 5 Puzzles, Jan 2004 January office rent INV: 10411 Puzzles G200403 4 Puzzles, Feb 2004 Rent for February Printer paper INV: 10414 Puzzles G200404 2004-01-01 2004-01-01 2004-01-22 2004-01-27 2004-02-01 2004-02-01 2004-02-13 2004-02-18 2004-02-24 E-mailed Check sent Check deposited E-mailed E-mailed Check mailed Debit card Check deposited E-mailed

Used to read data from the data adapter to generate a file for each row For example, if your Employee table contains a BLOB image column, you could use the Export Column task to extract the BLOB into an individual file for each Employee row Does the reverse of the Export Column; it reads data from separate files and adds the data columns to the existing data flow Uses an algorithm to identify duplicate rows of data within a data source This would be particularly useful if you were interested in creating a single customer master by combining many customer files from divisions of a large multinational organization, for example This transformation requires a connection to SQL Server to create the temporary tables and indexes it needs to perform its operations This transformation is available only in SQL Server Enterprise and Development Editions..

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