I'm new in using IBM Data Stage, i need to keep the file name that i set in the unstructured file in filepath as a value. Then i need to insert that value in original_file column of my table for all rows automatically. Is there any way to do this?
Assuming the file name is a job parameter and will be provided each job run you could use a Transformer - add a new column "original_file" and use the parameter name as derivation.
Note: A parameter is provided i.e. file_name will be referenced in DataStage with #file_name# (i.e. in the file stage) but will be referenced in the Transformer as file_name (without the #s)
Related
I have an Azure Data factory trigger that is fired off when a file is placed in blob storage, this trigger will start pipeline execution and pass the file name to the data flow activity. I would like to make sure that all the column names from the header row in the file are in the sink table. There is an identity column in the sink table that should not be in the comparison. Not sure how to tackle this task, I've read about the 'derived column' activity, is that the route I should take?
You can select or filter which columns reside in sink dataset or table by using "Field mapping". You can optionally use "derived columns" transformation, however in the "sink transformation" you will have this by default and is set to "Auto mapping". Here you can add or remove which columns are written to sink.
In the below example the column "id" can be assumed as similar to "Identity" column in your table. Assuming all the files have same columns:
Once you have modified as per your need, you can confirm the same from the "inspect" tab before run.
Strategy:
Use two ADF pipelines, one to get a list of all files and another one to process each file copying its content to a specific SQL table.
Setup:
I’ve created 4 CSV files, following the pattern you need: “[CustomerID][TableName][FileID].csv” and 4 SQL tables, one for each type of file.
A_inventory_0001.csv: inventory records for customer A, to be
inserted into the SQL table “A_Inventory”.
A_sales_0003.csv: sales
records for customer A, to be inserted into the SQL table “A_Sales”.
B_inventory_0002.csv: inventory records for customer B, to be
inserted into the SQL table “B_Inventory”.
B_sales_0004.csv: sales
records for customer B, to be inserted into the SQL table “B_Sales”
Linked Services
In Azure Data Factory, the following linked services were create using Key Vault (Key Vault is optional).
Datasets
The following datasets were created. Note we have created some parameters to allow the pipeline to specify the source file and the destination SQL table.
The dataset “AzureSQLTable” has a parameter to specify the name of the destination SQL table.
The dataset “DelimitedTextFile” has a parameter to specify the name of the source CSV file.
The dataset “DelimitedTextFiles” has no parameter because it will be used to list all files from source folder.
Pipelines
The first pipeline “Get Files” will get the list of CSV files from source folder (Get Metadata activity), and then, for each file, call the second pipeline passing the CSV file name as a parameter.
Inside the foreach loop, there is a call to the second pipeline “Process File” passing the file name as a parameter.
The second pipeline has a parameter “pFileName” to receive the name of the file to be processed and a variable to calculate the name of the destination table based on the file name.
The first activity is to use a split in the file name to extract the parts we need to compose the destination table name.
In the expression bellow we are splitting the file name using the “__” separator and then using the first and second parts to compose the destination table name.
#concat(string(split(pipeline().parameters.pFileName, '_')[0]),'_',string(split(pipeline().parameters.pFileName, '_')[10]))
The second activity will then copy the file from the source “pFileName” to the desnation table “vTableName” using dynamic mapping, ie not adding specific column names as this will be dynamic.
The files I used in this example and the ADF code are available here:
https://github.com/diegoeick/stack-overflow/tree/main/69340699
I hope this will resolve your issue.
In case you still need to save the CustomerID and FileID in the database tables, you can use the dynamic mapping and use the available parameters (filename) and create a json with the dynamic mapping in the mapping tab of your copy activity. You can find more details here: https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-schema-and-type-mapping#parameterize-mapping
I am new to ADF, i want to copy an excel from source to Achieve folder with added timestamp to the file, I tried following set up as parameters for source and target and run copy job. its just copying the file to the target not with timestamp. Not sure what to be done to fix this one right
following is the target filename value
#concat(replace(pipeline().parameters.pTriggerFile,'.csv',''), '_', formatDateTime(convertTimeZone(utcnow(),'UTC','Eastern Standard Time'),'yyyy-MM-ddTHHmmss'), '.csv')
Source Dataset
Target dataset
Follow the below steps to add a timestamp to the source filename when copying it to sink.
Source:
Azure data factory copy activity:
In the source dataset, create a parameter for the source filename and pass it dynamically in the file path.
In Source, create a parameter at the pipeline level and pass the filename dynamically to the dataset parameter.
In the sink dataset, create a dataset parameter and add it dynamically to the sink file path.
In the sink, pass the below dynamic content to add the current timestamp to the filename.
#concat(replace(pipeline().parameters.sourcefilename,'.csv',''), '_', formatDateTime(convertTimeZone(utcnow(),'UTC','Eastern Standard Time'),'yyyy-MM-ddTHHmmss'), '.csv')
When you run the pipeline, you can see the sink file has the timestamp added to it.
I have csv files and I want to move the content of files along with its meta data (File name, source (To be hard coded), control number (Part of file name - to be extracted from file name itself) using NiFi. So here is the sample File name and layout -
File name - 12345_user_data.csv (control_number_user_data.csv)
source - Newyork
CSV File Content/columns -
Fields - abc1, abc2, abc3, abc4
values - 1,2,3,4
Postgres Database table layout
Table name - User_Education
fields name -
control_number, file_name, source, abc1, abc2, abc3, abc4
Values -
12345, 12345_user_data.csv, Newyork, 1,2,3,4
I am planning to use below processors -
ListFile
FetchFile
UpdateAttributes
PutDatabaseRecords
LogAttributes
But I am not sure how to combine the actual content with the meta data to load into one single table. Please help
You can use UpdateRecord before PutDatabaseRecord to add the control_number, file_name, and source fields to each record, setting the populating the "Replacement Value Strategy" property to "Literal Value" and use Expression Language to set the values to the corresponding attributes.
For example, you could have a user-defined property /file_name set to ${filename}, that will add the file_name field to each record and set the value to whatever is in the "filename" attribute of the FlowFile.
I have an ADF pipeline with copy activity, I'm copying data from blob storage CSV file to SQL database, this is working as expected. I need to map Name of the CSV file (this coming from pipeline parameters) and save it in the destination table. I'm wondering if there is a way to map parameters to destination columns.
Column name can't directly use parameters. But you can use parameter for the whole structure property in dataset and columnMappings property in copy activity.
This might be a little tedious as you will need to write the whole structure array and columnMappings on your own and pass them as parameters into pipeline.
In DF v2 in Copy Data activity, it is possible to add a new column to the source with value $$FILEPATH, and then each record will have a name of the input file.
Azure DF v2, CopyData activity -> Source
I need to modify columns based on business rules using RCP. For example, all source columns that end with '_ID' must be changed to '_KEY' to meet the target.
An example: Test_ID in source becomes Test_KEY in target
I have multiple tables, some with 2 "ID" columns, and some with 20. Is there a way to configure the modify stage to bulk rename columns based on wildcard?
If not, is there another way?
Thanks.
I doubt that there is an option using modify stage with wildcards for this.
One alternative could be a schema file which can be used with any of following stages:
Sequential File, File Set, External Source, External Target, Column Import, Column Export
This schema file could also be generated or modified to adjust the column names as needed.
Another way could be to generate the appropriate SQL statement if the data resides in a database or is written to one.