Create SSIS Import parsing based on text file...

I have a text file listing 500 field names and their lengths.  (In a few months we will begin receiving monthly data files matching that schema). I can create an SSIS 2008 project and manually parse the columns (inside SSIS)  to match that
schema but I wonder if there is an easier way to do what I want (e.g. if I created a table using t-sql commands could SSIS somehow "pickup" the SSIS parsing from the table schema?)
TIA,
edm2

Not exactly. What I was hoping for is that instead of pointing SSIS to the data file and manually parsing all the fields that  I could create an (empty) table with the right schema and have SSIS use that table schema to define how it should
parse the input data. (Kind of backwards from the usual approach.)
edm2
Sorry you cant parse text file for metadata like that as metadata has to fixed in SSIS
However one way you can implement this is as follows
1. Create your table with required schema
2. In SSIS have a data flow task with flat file source which points to your file. Choose only a row delimiter and no column delimiter
3. Put a oledb destination and point to staging table with an identity column
4. Put a Execute SQL Task  with query as below
SELECT CASE WHEN (SELECT Column FROM Staging WHERE IDCol=1) = STUFF((SELECT ',' + COLUMN_NAME
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'Your schema table name'
AND TABLE_SCHEMA = 'dbo'
FOR XML PATH('')),1,1,'')
THEN 0
ELSE 1
END AS SchemaDiff
FROM (SELECT 1)t
Then map SchemaDiff column in the resultset to a boolean variable created in ssis. Also make sure you set resultset option to single row
Then you can use this boolean variable to check if schema is same. If False means no difference in schema else there's difference.
just a caution thar this will only compare column details without comparing their datatypes,lengths etc
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    Kevin

    I am with Chris on this one.
    If those headers are repeating in your text file, you could run a simple script to cleanse the data first and make the file fit for SQL*Loader.
    Here's an example with Perl, I hope the header of Page 2 and onwards resembles what you have in your file. I've snipped some data from the far right so that it fits on my terminal screen:
    $
    $ # show the contents of the data file
    $
    $ cat report.dat
    weekly_eft_repo  1.0                                                                                        Page: 1
    CDC:00304 / Sat Oct-31-2009 Weekly EFT Sweep for 25/10/09 - 31/10/09  Effective Date 03/11/09 Sat Oct-31-2009 22:06:14
    Bill to
    Retailer Retailer Name                  Name on Bank Account           Bank ABA   Bank Acct            On-line Amount
    ======== ============================== ============================== ========== ==================== ===============
    0200101 Triolet Popular Store          Triolet Popular Store          111111111  62030100130659            10,868.00
    0200103 Le Cacharel Snack              Le Cacharel Snack              111111111  62030100130813             9,728.00
    0200104 Advanced Co-operative Self Ser Advanced Co-operative Self Ser 111111111  111111111                  7,334.00
    0200105 Chez Popo Supermarket          Chez Popo Supermarket          111111111  61030100044898            30,932.00
    0200106 Vana Supermarket               Vana Supermarket               111111111  111111111                 17,775.00
    weekly_eft_repo  1.0                                                                                        Page: 2
    CDC:00304 / Sat Oct-31-2009 Weekly EFT Sweep for 25/10/09 - 31/10/09  Effective Date 03/11/09 Sat Oct-31-2009 22:06:14
    Bill to
    Retailer Retailer Name                  Name on Bank Account           Bank ABA   Bank Acct            On-line Amount
    ======== ============================== ============================== ========== ==================== ===============
    0200107 Mont Choisy Store              Mont Choisy Store              111111111  62030100130804             8,840.00
    0200108 Vijay Store                    Vijay Store                    111111111  62030100131229            16,416.00
    0200109 Neptune Confection             Neptune Confection             111111111  62030100130931            11,077.00
    0200110 Antoine Store                  Antoine Store                  111111111  111111111                  2,470.00
    0200111 P.S.C Cold Storage             P.S.C Cold Storage             111111111  111111111                 10,431.00
    weekly_eft_repo  1.0                                                                                        Page: 3
    CDC:00304 / Sat Oct-31-2009 Weekly EFT Sweep for 25/10/09 - 31/10/09  Effective Date 03/11/09 Sat Oct-31-2009 22:06:14
    Bill to
    Retailer Retailer Name                  Name on Bank Account           Bank ABA   Bank Acct            On-line Amount
    ======== ============================== ============================== ========== ==================== ===============
    0200113 Mini Prix Boutique             Mini Prix Boutique             111111111  62030100131501            26,315.00
    0200114 Hotel Cassim                   Hotel Cassim                   111111111  111111111                135,147.00
    0200116 Aman Snack                     Aman Snack                     111111111  62030100129481             7,334.00
    0200117 Best For Less Company Ltd      Best For Less Company Ltd      111111111  111111111                  3,325.00
    0200118 Central Way                    Central Way                    111111111  111111111                 25,137.00
    $
    $
    $ # snip off the headers using Perl so that the transformed file has just the data to load
    $
    $ perl -lne 'BEGIN{$/=""} s/^weekly.*?=\n//msg; print' report.dat
    0200101 Triolet Popular Store          Triolet Popular Store          111111111  62030100130659            10,868.00
    0200103 Le Cacharel Snack              Le Cacharel Snack              111111111  62030100130813             9,728.00
    0200104 Advanced Co-operative Self Ser Advanced Co-operative Self Ser 111111111  111111111                  7,334.00
    0200105 Chez Popo Supermarket          Chez Popo Supermarket          111111111  61030100044898            30,932.00
    0200106 Vana Supermarket               Vana Supermarket               111111111  111111111                 17,775.00
    0200107 Mont Choisy Store              Mont Choisy Store              111111111  62030100130804             8,840.00
    0200108 Vijay Store                    Vijay Store                    111111111  62030100131229            16,416.00
    0200109 Neptune Confection             Neptune Confection             111111111  62030100130931            11,077.00
    0200110 Antoine Store                  Antoine Store                  111111111  111111111                  2,470.00
    0200111 P.S.C Cold Storage             P.S.C Cold Storage             111111111  111111111                 10,431.00
    0200113 Mini Prix Boutique             Mini Prix Boutique             111111111  62030100131501            26,315.00
    0200114 Hotel Cassim                   Hotel Cassim                   111111111  111111111                135,147.00
    0200116 Aman Snack                     Aman Snack                     111111111  62030100129481             7,334.00
    0200117 Best For Less Company Ltd      Best For Less Company Ltd      111111111  111111111                  3,325.00
    0200118 Central Way                    Central Way                    111111111  111111111                 25,137.00
    $
    $ You could do an inline substitution with Perl, or redirect it to a temp file and then move back to the original file. In either case, you have just the relevant data for the next step.
    Again, Perl is just one option. On Unix/Linux systems, you have a large number of utilities/scripting languages that can perform almost any kind of data transformation.
    HTH,
    isotope

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