I have the table created in BigQuery with partitioned by date and it has the Date type. DataPrep also has the same column with same data type. When i try to load the data from dataprep to bigquery table i am getting the error like "The column datatypes in the dataset must match the destination column datatypes". Screenshot also been attached, please go through it and give me a solution.enter image description here
As the screenshot says, one is a TIME, DATETIME, or TIMESTAMP
the other is STRING as noted by the icons in front of your columns.
You need to make sure at dataset that you've chosen the right data type. Dataprep may infer wrong automatically sometimes your data type.
In this this thread it is mentioned that you need to convert both types to TIMESTAMP in order to make this work. In my case this did the trick, but it is kind of cumbersome. Hopefully they will support this for simple DATE columns soon.
Related
Been struggling with some datasets I want to use which have a problem with the date format.
Bigquery could not load the files and returned the following error:
Could not parse '4/12/2016 2:47:30 AM' as TIMESTAMP for field date (position 1) starting at location 21 with message 'Invalid time zone:
AM'
I have been able to upload the file manually but as strings, and now would like to set the fields back to the proper format, However, I just could not find a way to change the format of the date column from string to proper DateTime format.
Would love to know if this is possible as the file is just too long to be formatted in excel or sheets (as I have done with the smaller files from this dataset).
now would like to set the fields back to the proper format ... from string to proper DateTime format
Use parse_datetime('%m/%d/%Y %r', string_col) to parse datetime out of string
If applied to sample string in your question - you got
As #Mikhail Berlyant rightly said, using the parse_datetime('%m/%d/%Y %r', string_col)
function will convert your badly formatted dates to a standard format as per ISO 8601 accepted by Google Bigquery . the best option will then be to save these query results to a new table on the database in your Bigquery Project.
I had a similar issue.
Below is an image of my table which i uploaded with all columns in String format .
Next up was that i applied the following settings to the query below
The Settings below stored the query output to a new table called heartrateSeconds_clean on the same dataset
The Write if empty option is a good option to avoid overwriting the existing raw data or just arbitrarily writing output to a temporary table, except if you are sure you want to do so. Save the settings and proceed to Run your Query.
As seen below, the output schema of the new table is automatically updated
Below is the new preview of the resulting table
NB: I did not apply an ORDER BY clause to the Results hence the data is not ordered by any specific column in both versions of the same table.
This dataset has over 2M rows.
Been struggling with some datasets I want to use which have a problem with the date format.
Bigquery could not load the files and returned the following error:
Could not parse '4/12/2016 2:47:30 AM' as TIMESTAMP for field date (position 1) starting at location 21 with message 'Invalid time zone:
AM'
I have been able to upload the file manually but as strings, and now would like to set the fields back to the proper format, However, I just could not find a way to change the format of the date column from string to proper DateTime format.
Would love to know if this is possible as the file is just too long to be formatted in excel or sheets (as I have done with the smaller files from this dataset).
now would like to set the fields back to the proper format ... from string to proper DateTime format
Use parse_datetime('%m/%d/%Y %r', string_col) to parse datetime out of string
If applied to sample string in your question - you got
As #Mikhail Berlyant rightly said, using the parse_datetime('%m/%d/%Y %r', string_col)
function will convert your badly formatted dates to a standard format as per ISO 8601 accepted by Google Bigquery . the best option will then be to save these query results to a new table on the database in your Bigquery Project.
I had a similar issue.
Below is an image of my table which i uploaded with all columns in String format .
Next up was that i applied the following settings to the query below
The Settings below stored the query output to a new table called heartrateSeconds_clean on the same dataset
The Write if empty option is a good option to avoid overwriting the existing raw data or just arbitrarily writing output to a temporary table, except if you are sure you want to do so. Save the settings and proceed to Run your Query.
As seen below, the output schema of the new table is automatically updated
Below is the new preview of the resulting table
NB: I did not apply an ORDER BY clause to the Results hence the data is not ordered by any specific column in both versions of the same table.
This dataset has over 2M rows.
While uploading my dataset from Google drive to Google cloud platform, I failed to edit the schema. Every time I uploaded the dataset, I was asked to edit the schema. For example, column: yearinjob was in the type of float. But while executing the query SELECT * FROM ...
it always says Error while reading the table: XXX.demo1.wkfc3, error message: Could not convert the value to float. Row 1888; Col 19.
I changed the schema for a datatype to integer, numeric, but none works except string.
Can anyone help me with it?
You might ensure that the info in the dataset that you are uploading is aligned to your Specified Schema.
When you use Schema auto-detection, BigQuery starts the inference process by selecting a random file in the data source and scanning up to 100 rows of data to use as a representative sample. BigQuery then examines each field and attempts to assign a data type to that field based on the values in the sample.
Check that the value at Row 1888; Col 19 matches with the assigned type for that field. That might be the cause of the error “Could not convert the value to float”.
This docs that may be helpful.
This is the documentation for Data Types and allowed values.
And this is about managing datasets.
One of the columns I send (in my code) to BigQuery is integers. I added the columns to BigQuery and I was too fast and added them as type string.
Will they be automatically converted? Or will the data be totally corrupted (= I cannot trust at all the resulting string)?
Data shouldn't be automatically converted as this would destroy the purpose of having a table schema.
What I've seen people doing is saving a whole json line as string and then processing this string inside of BigQuery. Other than that, if you try to save values not correspondent to the field schema definition, you should see an error being thrown, like so:
If you need to change a table schema's definition, you can check this tutorial on updating a table schema.
Actually BigQuery converted automatically the integers that I have sent it to string, so my table populates ok
I have some data which was dumped from a PostgreSQL database (allegedly, using pg_dump) which needs to get imported into SQL Server.
While the data types are ok, I am running into an issue where there seems to be a placeholder for a NULL. I see a backslash followed by an uppercase N in many fields. Below is a snippet of the data, as viewed from within Excel. Left column has a Boolean data type, and the right one has an integer as the data type
Some of these are supposed to be of the Boolean datatype, and having two characters in there is most certainly not going to fly.
Here's what I tried so far:
Import via dirty read - keeping whatever datatypes SSIS decided each field had; to no avail. There were error messages about truncation on all of the boolean fields.
Creating a table for the data based on the correct data types, though this was more fun... I needed to do the same as in the dirty read, as the source would otherwise not load properly. There was also a need to transform the data into the correct data type for insertion into the destination data source; yet, I am getting truncation issues, when it most certainly shouldn't be.
Here is a sample expression in my derived column transformation editor:
(DT_BOOL)REPLACE(observation,"\\N","")
The data type should be Boolean.
Any suggestion would be really helpful!
Thanks!
Since I was unable to circumvent the SSIS rules in order to get my data into my tables without an error, I took the quick-and-dirty approach.
The solution which worked for me was to have the source data read each column as if it were a string, and the destination table had all fields be of the datatype VARCHAR. This destination table will be used as a staging table, once in SS, I can manipulate as needed.
Thank you #cha for your input.