How do I load a file from Cloud Storage into memory
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I have end users that are going to be uploading a csv file into a bucket which will then be loaded to BigQuery.
The issue is the content of the data is unreliable.
i.e. it contains fields with free text that may contain linefeeds,extra commas, invalid date formats e.t.c. e.t.c.
I have a python script that will pre-process the file and write out a new one with all errors corrected.
I need to be able to automate this into the cloud.
I was thinking I could load the contents of the file (it's only small) into memory and process the records then write it back out to the Bucket.
I do not want to process the file locally.
Despite extensive searching I can't find how to load a file in a bucket into memory and then write it back out again.
Can anyone help ?
python-3.x google-cloud-storage
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I have end users that are going to be uploading a csv file into a bucket which will then be loaded to BigQuery.
The issue is the content of the data is unreliable.
i.e. it contains fields with free text that may contain linefeeds,extra commas, invalid date formats e.t.c. e.t.c.
I have a python script that will pre-process the file and write out a new one with all errors corrected.
I need to be able to automate this into the cloud.
I was thinking I could load the contents of the file (it's only small) into memory and process the records then write it back out to the Bucket.
I do not want to process the file locally.
Despite extensive searching I can't find how to load a file in a bucket into memory and then write it back out again.
Can anyone help ?
python-3.x google-cloud-storage
You can do this usngdownload_as_string
detailed answer here: stackoverflow.com/a/48279267/6039925
– Gaurav Taneja
Nov 29 '18 at 8:23
add a comment |
I have end users that are going to be uploading a csv file into a bucket which will then be loaded to BigQuery.
The issue is the content of the data is unreliable.
i.e. it contains fields with free text that may contain linefeeds,extra commas, invalid date formats e.t.c. e.t.c.
I have a python script that will pre-process the file and write out a new one with all errors corrected.
I need to be able to automate this into the cloud.
I was thinking I could load the contents of the file (it's only small) into memory and process the records then write it back out to the Bucket.
I do not want to process the file locally.
Despite extensive searching I can't find how to load a file in a bucket into memory and then write it back out again.
Can anyone help ?
python-3.x google-cloud-storage
I have end users that are going to be uploading a csv file into a bucket which will then be loaded to BigQuery.
The issue is the content of the data is unreliable.
i.e. it contains fields with free text that may contain linefeeds,extra commas, invalid date formats e.t.c. e.t.c.
I have a python script that will pre-process the file and write out a new one with all errors corrected.
I need to be able to automate this into the cloud.
I was thinking I could load the contents of the file (it's only small) into memory and process the records then write it back out to the Bucket.
I do not want to process the file locally.
Despite extensive searching I can't find how to load a file in a bucket into memory and then write it back out again.
Can anyone help ?
python-3.x google-cloud-storage
python-3.x google-cloud-storage
asked Nov 23 '18 at 8:17
mez63mez63
4510
4510
You can do this usngdownload_as_string
detailed answer here: stackoverflow.com/a/48279267/6039925
– Gaurav Taneja
Nov 29 '18 at 8:23
add a comment |
You can do this usngdownload_as_string
detailed answer here: stackoverflow.com/a/48279267/6039925
– Gaurav Taneja
Nov 29 '18 at 8:23
You can do this usng
download_as_string
detailed answer here: stackoverflow.com/a/48279267/6039925– Gaurav Taneja
Nov 29 '18 at 8:23
You can do this usng
download_as_string
detailed answer here: stackoverflow.com/a/48279267/6039925– Gaurav Taneja
Nov 29 '18 at 8:23
add a comment |
1 Answer
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I believe what you’re looking for is Google Cloud Functions. You can set a Cloud Function to be triggered by an upload to the GCS bucket, and use your Python code in the same Cloud Function to process the .csv and upload it to BigQuery, however, please bear in mind that Python 3.7.1 support for Cloud Functions is currently in a Beta state of development.
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I believe what you’re looking for is Google Cloud Functions. You can set a Cloud Function to be triggered by an upload to the GCS bucket, and use your Python code in the same Cloud Function to process the .csv and upload it to BigQuery, however, please bear in mind that Python 3.7.1 support for Cloud Functions is currently in a Beta state of development.
add a comment |
I believe what you’re looking for is Google Cloud Functions. You can set a Cloud Function to be triggered by an upload to the GCS bucket, and use your Python code in the same Cloud Function to process the .csv and upload it to BigQuery, however, please bear in mind that Python 3.7.1 support for Cloud Functions is currently in a Beta state of development.
add a comment |
I believe what you’re looking for is Google Cloud Functions. You can set a Cloud Function to be triggered by an upload to the GCS bucket, and use your Python code in the same Cloud Function to process the .csv and upload it to BigQuery, however, please bear in mind that Python 3.7.1 support for Cloud Functions is currently in a Beta state of development.
I believe what you’re looking for is Google Cloud Functions. You can set a Cloud Function to be triggered by an upload to the GCS bucket, and use your Python code in the same Cloud Function to process the .csv and upload it to BigQuery, however, please bear in mind that Python 3.7.1 support for Cloud Functions is currently in a Beta state of development.
edited Nov 25 '18 at 9:02
answered Nov 23 '18 at 11:38
MaximMaxim
1,573211
1,573211
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You can do this usng
download_as_string
detailed answer here: stackoverflow.com/a/48279267/6039925– Gaurav Taneja
Nov 29 '18 at 8:23