Delimited format flat file

For each data source the server will access, you create a synonym that describes the structure of the data source and the server mapping of the Flat or Delimited Flat Files data types. Synonyms define unique names or aliases for each Flat or Delimited Flat File that is accessible from the server. Synonyms are useful because they hide the underlying data source location and identity from client applications. They also provide support for extended metadata features of the server, such as virtual fields and additional security mechanisms.

Using synonyms allows an object to be moved or renamed while allowing client applications to continue functioning without modification. The only modification required is a redefinition of the synonym on the server. The result of creating a synonym is a Master File that represents the server metadata.

As defined in this chapter, a Flat file is a fixed-format sequential file in which each field occupies a predefined position in the record, with unoccupied positions filled by blanks to maintain the fixed structure. To create a synonym, you must have previously configured the adapter. Depending on the type of adapter you chose, one of the following options appears on the context menu. The Status pane indicates that the synonym was created successfully.

The synonym is created and added under the specified application directory. Note: When creating a synonym, if you choose the Validate check box, the server adjusts special characters and checks for reserved words.

The following list describes the parameters for which you will need to supply values, and related tasks you will need to complete in order to create a synonym for the adapter. These options may appear on multiple panes. To advance from pane to pane, click the buttons provided, ending with the Create Synonym button, which generates the synonym based on your entries. If you do not select this check box, default translation settings are applied. Indicates how often the listener will check for the presence of a file in number of seconds.

The default is 10 seconds. Indicates the timeout interval for the listener in number of seconds. Indicates the number of records processed within one request. This option is available for single file processing. Indicates number of files processed within one request. This option is available for processing collection of files.

The default is 99,, Indicates the mechanism used by files agents to pick up files from their directories. For example, if a user enters file1. Indicates the extension of the trigger file. The trigger file extension is added to the full name of the file being listened for. Indicates how the file is handled after it has been processed by a file agent.

This can be an application directory, a mapped application name, or a directory location on your file system. When using a directory on a file system for local files, the name of the physical directory can be used. When using a directory on a file system for remote data files, a directory relative to the initial directory can be used. Defines the location of data files for polling. A full name returns just that entry. A name with a wildcard symbol may return many entries. Select the Validate check box if you wish to convert all special characters to underscores and perform a name check to prevent the use of reserved names.

This is accomplished by adding numbers to the names. This parameter ensures that names adhere to specifications. No checking is performed for names.

Select the Make unique check box if you wish to set the scope for field and group names to the entire synonym. This ensures that no duplicate names are used, even in different segments of the synonym. When this option is unchecked, the scope is the segment. Select an application directory. The default value is baseapp. If you have tables with identical table names, assign a prefix or a suffix to distinguish them. For example, if you have identically named human resources and payroll tables, assign the prefix HR to distinguish the synonyms for the human resources tables.

Note that the resulting synonym name cannot exceed 64 characters. If all tables and views have unique names, leave the prefix and suffix fields blank. To specify that this synonym should overwrite any earlier synonym with the same fully qualified name, select the Overwrite existing synonyms check box. Note: The connected user must have operating system write privileges in order to recreate a synonym. This option provides data portability between servers by enabling processing of data loaded in different encoding systems.

Click the Extended data format attributes check box to display the Codepage option. In the input box provided, enter the code page of the stored data. Your entry is added to the Master File of the generated synonym. Note: The code page must have a customized conversion table as part of your NLS configuration. If you have not already requested a conversion table for the designated code page, you can do so now.

Click Customize code page conversion tables and select the check box for the code page you wish to use. Click the Save and Restart button to implement your new setting. If you do not select the check box, default translation settings are applied. Note: The code page you specify here must be one for which you have requested a customized code page conversion table as part of your NLS configuration.

This column displays the name that will be assigned to each synonym. To assign a different name, replace the displayed value. After choosing files, choose the corresponding data file name from the drop-down list.

Once you have created a synonym, you can right-click the synonym name in the Adapter navigation pane of either the Web Console or the Data Management Console to access the available options.

For a list of options, see Synonym Management Options. However, there are reasonable constraints or rules that can be applied to detect situations where the data is clearly wrong. Instances of fields containing values violating the validation rules defined represent a quality gap that can impact inbound flat file processing. Example: Date of birth DOB. This is defined as the DATE datatype and can assume any valid date.

However, a DOB in the future, or more than years in the past are probably invalid. Also, the date of birth of the child is should not be greater than that of their parents. The goal is to identify orphan records in the child entity with a foreign key to the parent entity.

Example: Consider a file import process for a CRM application which imports contact lists for existing Accounts. ETL Validator supports defining of data quality rules in Flat File Component for automating the data quality testing without writing any database queries.

Custom rules can be defined and added to the Data Model template. Data in the inbound flat files is generally processed and loaded into a database. In some cases the output may also be another flat file. The purpose of Data Completeness tests are to verify that all the expected data is loaded in the target from the inbound flat file.

Some of the tests that can be run are : Compare and Validate counts, aggregates min, max, sum, avg and actual data between the flat file and target. Column or attribute level data profiling is an effective tool to compare source and target data without actually comparing the entire data. It is similar to comparing the checksum of your source and target data. These tests are essential when testing large amounts of data.

Some of the common data profile comparisons that can be done between the flat file and target are:. Example 1: Compare column counts with values non null values between source and target for each column based on the mapping.

It is also a key requirement for data migration projects. Example: Write a source query on the flat file that matches the data in the target table after transformation. It takes care of loading the flat file data into a table for running validations. Data in the inbound Flat File is transformed by the consuming process and loaded into the target table or file.

It is important to test the transformed data. There are two approaches for testing transformations — white box testing and black box testing. For transformation testing, this involves reviewing the transformation logic from the flat file data ingestion design document and corresponding code to come up with test cases. The advantage with this approach is that the tests can be rerun easily on a larger data set.

The disadvantage of this approach is that the tester has to reimplement the transformation logic. Example: In a financial company, the interest earned on the savings account is dependent the daily balance in the account for the month.

The daily balance for the month is part of an inbound CSV file for the process that computes the interest. Review the requirement and design for calculating the interest. Implement the logic using your favourite programming language.

Compare your output with data in the target table. Black-box testing is a method of software testing that examines the functionality of an application without peering into its internal structures or workings. For transformation testing, this involves reviewing the transformation logic from the mapping design document setting up the test data appropriately. The advantage with this approach is that the transformation logic does not need to be reimplemented during the testing.

The disadvantage of this approach is that the tester needs to setup test data for each transformation scenario and come up with the expected values for the transformed data manually. Review the requirement for calculating the interest. Setup test data in the flat file for various scenarios of daily account balance.

Compare the transformed data in the target table with the expected values for the test data. The goal of performance testing is to validate that the process consuming the inbound flat files is able to handle flat files with the expected data volumes and inbound arrival frequency. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Feedback will be sent to Microsoft: By pressing the submit button, your feedback will be used to improve Microsoft products and services.

Privacy policy. The Flat File source reads data from a text file. The text file can be in delimited, fixed width, or mixed format. Fixed width format uses width to define columns and rows. This format also includes a character for padding fields to their maximum width. Ragged right format uses width to define all columns, except for the last column, which is delimited by the row delimiter.

Add a column to the transformation output that contains the name of the text file from which the Flat File source extracts data. The Flat File connection manager that the Flat File source uses must be configured to use a delimited format to interpret zero-length strings as nulls. If the connection manager uses the fixed width or ragged right formats, data that consists of spaces cannot be interpreted as null values.

The output columns in the output of the Flat File source include the FastParse property. FastParse indicates whether the column uses the quicker, but locale-insensitive, fast parsing routines that Integration Services provides or the locale-sensitive standard parsing routines. For more information, see Fast Parse and Standard Parse.

Output columns also include the UseBinaryFormat property. You use this property to implement support for binary data, such as data with the packed decimal format, in files. By default UseBinaryFormat is set to false. When you do this, the Flat File source skips the data conversion and passes the data to the output column as is. You can also write a custom data flow component to interpret the data. This source uses a Flat File connection manager to access the text file.