Uploaded a csv file to postgresql database and trying to change the columns that contain numbers from default text to numeric datatype.
I understand that you can manually alter each individual column datatype using ALTER and adding "," for multiple columns. However, the csv file contains 50 columns so trying to find out if there's a more DRY approach to changing the datatypes.
I was thinking about looping through certain sections of the csv file that need to be changed from text to numeric. For example, if columns 10 through 25 (out of 50) need to be changed, how would I select the starting point at column 10 and end at column 25?
If there's a way to loop through all columns and change the data type depending on the values in the column, that would be good to know. I was thinking this might be a problem because all values are set to default "text", so determining datatype based on values would be problematic. If this is not the case, would like to know how to approach the problem this way. Thanks!
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I need to get the column number of a staged file on Snowflake.
The main idea behind it, is that I need to automate getting this field in other queries rather than using t.$3 whereas 3 is the position of the field, that might be changed because we are having an expandable surveys (more or less questions depending on the situation).
So what I need is something like that:
SELECT COL_NUMBER FROM #my_stage/myfile.csv WHERE value = 'my_column_name`
-- Without any file format to read the header
And then this COL_NUMBER could be user as t.$"+COL_NUMBER+" inside merge queries.
I have a some file in an azure data lake 2 and I want to load them as a column value nvarchar(max) in AzureSQLDW. The table in AzureSQLDW is heap. I couldn't find any way to do it? All I see is column delimited when load them into multiple rows instead of one row in single column. How I achieve this?
I don't guarantee this will work, but try using COPY INTO and define non-present values for row and column delimiters. Make your target a single column table.
I would create a Source Dataset with a single column. You do this by specifying "No delimiter":
Next, go to the "Schema" tab and Import the schema, which should create a single column called "Prop_0":
Now the data should come through as a single string instead of delimited columns.
I have a CSV file having more than 700 columns. I just want 175 columns from them to be inserted into a RDBMS table or a flat file usingPentaho (PDI). Now, the source CSV file has variable columns i.e. the columns can keep adding or deleting but have some specific keywords that remain constant throughout. I have the list of keywords which are present in column names that have to excluded, e.g. starts_with("avgbal_"), starts_with("emi_"), starts_with("delinq_prin_"), starts_with("total_utilization_"), starts_with("min_overdue_"), starts_with("payment_received_")
Any column which have the above keywords have to be excluded and should not pass onto my RDBMS table or a flat file. Is there any way to remove the above columns by writing some SQL query in PDI? Selecting specific 175 columns is not possible as they are variable in nature.
I think your example is fit to use meta data injection you can refer to example shared below
https://help.pentaho.com/Documentation/7.1/0L0/0Y0/0K0/ETL_Metadata_Injection
two things you need to be careful
maintain list of columns you need to push in.
since you have changing column names so you may face issue with valid columns as well which you want to import or work with. in order to do so make sure you generate the meta data file every time so you are sure about the column names you want to push out from the flat file.
I loaded an Excel file into an SQL table. The Excel file, one field consists of VARCHAR data (of data type general). When loaded into an SQL table, some of these values are prefixed with zero.
Example: in the Excel file it is 1081999 the same value become 01081999 in the SQL table.
What might be the reason for this ?
Excel will hide leading 0's as it identifies the fields content as a number and displays it as such. I would assume that the excel worksheet does indeed contain these leading 0's and they are simply not shown by Excel. If you change the type of the column from General to Text do they show up??
As a side note, if these are indeed numbers you should be storing them in a numeric datatype in the database...
I am exporting a file from a system as .csv. My aim is to link to this file as a table (which matches the output field for field) and then run the queries and export.
The problem I am having is that, upon import, all the fields are 255 bytes wide rather than what they need to be.
Here's what I've tried so far:
I've looked at ALTER TABLE but I cannot run multiple ALTER TABLE statements in one macro.
I've also tried appending the table into another table with the correct structure but it seems to overwrite the structure.
I've also tried using the Left function with the appropriate field length, but when I try to export, I pretty much just see 5 bytes per column.
What I would like is a suggestion as to what is the best path to take given my situation. I am not able to amend the initial .csv export, and I would like to avoid VBA if possible, as I am not at all familiar with it.
You don't really need to worry about the size of Text fields in an Access linked table that is connected to a CSV file. Access simply assigns each Text field the largest possible maximum size: 255. It does not mean that every value is actually 255 characters long, it just means that any values in those fields can be at most 255 characters long.
Even if you could change the structure of the linked table (which you can't), it wouldn't make any real difference except to possibly truncate longer Text values, and you could easily do that with a String function. For example, if a particular field had to be restricted to 15 characters then you could simply use Left([fieldName], 15) as a query column or as the control source in a report.
In the end, as the data set is not that large, I have set this up to append from my source data into a table with the correct structure. I can now run my processes against this table as per normal.