I am doing copy activity to load the data from azure data factory to on premise SQL table.
I could see in copy activity column Mapping, there is warning message like source column is string with date and time value (2022-09-13 12:53:28) so that i created target SQL table column is date data type.
While import mapping in copy activity, i could see the whatever date column i mapped in SQL. there is warning message throwing in ADF. kindly advise, how do we resolve it.
screenshot:
The warning just indicates that it copy data will truncate source column data when additional data information is found in a column value. There would not be any error in this case but there might be data loss.
In your case, since the column value is 2022-09-13 12:53:28, it will be inserted without any issue into the datetime column without truncation.
The following is a demonstration where I try to insert the following source data:
id,first_name,date
1,Wenona,2022-09-13 12:53:28
2,Erhard,2022-09-13 13:53:28
3,Imelda,2022-09-13 14:53:28
The copy activity runs successfully and inserts the data. The following is my target table data after inserting:
When I insert the following data, it would be truncated to just include a precision of 2 digits of milli seconds as shown below.
id,first_name,date
1,Wenona,2022-09-13 12:53:28.11111
2,Erhard,2022-09-13 13:53:28.11111
3,Imelda,2022-09-13 14:53:28.11111
Related
I have below column in my table
I need an output as below
I am using Dataflow in the Azure data factory and unable to get the above output. I used derived column but no success. I used replace function, but it's not coming correct. Can anyone advise how to format this in dataflow?
Source is taken in data flow with data as in below image.
Derived column transformation is added next to source.
New column is added and the expression is given as
iif(left(id,1)=='-', replace(replace(id,"USD",""),"-","-$"), concat("$", replace(id,"USD","")))
Output of Derived Column activity
I have a simple pipeline that loads data from a csv file to an Azure SQL db.
I have added a data flow where I have ensured all schema matches the SQL table. I have a specific field which contains numbers with leading zeros. The data type in the source - projection is set to string. The field is mapped to the SQL sink showing as string data-type. The field in SQL has nvarchar(50) data-type.
Once the pipeline is run, all the leading zeros are lost and the field appears to be treated as decimal:
Original data: 0012345
Inserted data: 12345.0
The CSV data shown in the data preview is showing correctly, however for some reason it loses its formatting during insert.
Any ideas how I can get it to insert correctly?
I had repro’d in my lab and was able to load as expected. Please see the below repro details.
Source file (CSV file):
Sink table (SQL table):
ADF:
Connect the data flow source to the CSV source file. As my file is in text format, all the source columns in the projection are in a string.
Source data preview:
Connect sink to Azure SQL database to load the data to the destination table.
Data in Azure SQL database table.
Note: You can all add derived columns before sink to convert the value to string as the sink data type is a string.
Thank you very much for your response.
As per your post the DF dataflow appears to be working correctly. I have finally discovered an issue with the transformation - I have an Azure batch service which runs a python script, which does a basic transformation and saves the output to a csv file.
Interestingly, when I preview the data in the dataflow, it looks as expected. However, the values stored in SQL are not.
For the sake of others having a similar issue, my existing python script used to convert a 'float' datatype column to string-type. Upon conversion, it used to retain 1 decimal number but as all of my numbers are integers, they were ending up with .0.
The solution was to convert values to integer and then to string:
df['col_name'] = df['col_name'].astype('Int64').astype('str')
I have columns like a int, b varchar,c timestamp in my table x (redshift) and am trying to load those three columns into another table b(redshift) by using a mapping m1, in that am using odbc connection's.
Issue is am able to load the data for all the columns except date fields (c timestamp) whether those are from src or expression.
In place of date null values are storing.
Mapping ran successful without any issue/warning.
Note: am using odbc connection's because I need to call stored procedure in Post sql.
thanks for your response.
As I modified the lookup transformation then I resolved the issue.
In lookup mapping I just modified the multiple matches to return all rows then the date fields are getting loaded from source/expression transformation to target.
I would like to copy the data from local csv file to sql server in Azure Data Factory. The table in sql server is created already. The local csv file is exported from mysql.
When I use copy data in Azure Data Factory, there is an error "Exception occurred when converting value 'NULL' for column name 'deleted' from type 'String' to type 'DateTime'. The string was not recognized as a valid DateTime.
What I have done:
I checked the original value from column name 'deleted' is NULL, without quotes(i.e. not 'NULL').
I cannot change the data type during file format settings. The data type for all column is preset to string as default.
I tried to create data flow instead of copy data. I can change the data type from source projection. But the sink dataset cannot select sql server.
What can I do to copy data from CSV file to sql server via Azure Data Factory?
Data Flow doesn't support on-premise SQL. We can't create the source and sink.
You can use copy active or copy data tool to do that. I made an example data which delete is NULL:
As you said the delete column data is Null or contains NULL, and ALL will be considered as String. The key is that your Sink SQL Server table schema if it allows NULL.
I tested many times and it all works well.
I'm doing an ETL for a job. For the data source stage, I input custom select statement. In the output tab of data source stage, I defined the INCEPTION column data type is Timestamp. The right data type for INCEPTION is date. I check it via DBEAVER. But somehow the IBM Datastage assumes that it is WVARCHAR. It says ODBC_Connector_0: Schema reconciliation detected a type mismatch for field INCEPTION. When moving data from field type WVARCHAR(min=0,max=10) into DATETIME(fraction=6), data corruption can occur (CC_DBSchemaRules::reportTypeMismatch, file CC_DBSchemaRules.cpp, line 1,838). I don't know why it is, since from the database shows that INCEPTION is definitely a Date column. I don't know how to fix this since I don't think I'm making mistake. What did I do wrong and how to fix this?
Where did DataStage get its table definition? DataStage is a computer program; it can't "decide" anything. If you import the table definition from the source, what data type is INCEPTION ? If it is either Date or Timestamp, load that table definition into your DataStage job. Otherwise explicitly convert the string using StringToTimestamp() function in a Transformer stage.