When exporting data from the Google bigquery table to Google cloud storage in Python, I get the error:
Access Denied: BigQuery BigQuery: Permission denied while writing
data.
I checked the JSON key file and it links to the owner of the storage. What can I do?
there are several reason's for this type of error
1. you give the exact path to the GOOGLE_APPLICATION_CREDENTIALS key.
2. Please check that you have writing permission in your project.
3. You have given a correct schema and their value if you writing a table, many of the times this type of error occurred due to incorrect schema value
Related
I am getting this error at the Source tab at the Use query (Table, Query) Query, when doing a copy data activity at the Azure Synapse pipeline.
Unable to authenticate with Google BigQuery Storage API:
.
The strange thing is I can preview data at the Source dataset, I can also preview data when select the Use query Table option.
I can even run query to select the table's schema
SELECT
*
FROM
`3082`.INFORMATION_SCHEMA.TABLES
WHERE table_type = 'BASE TABLE'
but I get this authentication error when selecting columns
SELECT
*
FROM
`3082.gcp_billing_export_v1_019F74_6EA5E8_C96548`;
ERROR [HY000] [Microsoft][BigQuery] (131) Unable to authenticate with Google BigQuery Storage API. Check your account permissions
The above error is due to issue in authentication of BigQuery Storage API. The permission required to access data from BigQuery are,
bigquery.readsessions.create
bigquery.readsessions.getData
bigquery.readsessions.update
The role BigQuery User will help in giving above permissions.
Reference:
Google cloud doc on Access Control - BigQuery User.
MS doc on Google BigQuery connector issue
I do not understand about the BigQuery Read Session User permission. I wonder if I got assigned this role. Can I query the data set in the Bigquery via python SDK?
I tried:
from google.cloud import bigquery
import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'Path/xxx.json'
project_id = 'Project_ID'
client = bigquery.Client()
client.query(`SQL`)
Got:
Forbidden: 403 Access Denied: Table <> User does not have permission to query table <>, or perhaps it does not exist in location <>.
Location: <>
Job ID: <>
To be clear, I want to know what the read session means in Bigquery.
When the Storage Read API is used, structured data is sent in a binary serialization format which allows parallelism. Storage Read API provides fast access to managed BigQuery storage using RPC protocol.For the usage of Storage Read API, ReadSession has to be created.
The ReadSession message contains the information about maximum number of streams, the snapshot time, the set of columns to return, and the predicate filter which is provided to CreateReadSession RPC. A ReadSession response contains the set of Stream identifiers which is used by Storage API. The Stream identifier that is returned from the ReadSession response is used to read all the data from the table. For more information, you can check this documentation.
Trying to build a data lake using S3 for files that are in .csv.gz format and then further cleansing/processing data in AWS environment itself.
First used AWS Glue to create a data catalog\ (crawler was able to identify all tables).
The tables from catalog are also available in AWS Athena but when i try to run a Select * from the table it gives me following error.
Error opening Hive split s3://BUCKET_NAME/HEADER FOLDER/FILENAME.csv.gz (offset=0, length=44354) using org.apache.hadoop.mapred.TextInputFormat: Permission denied on S3 path: 3://BUCKET_NAME/HEADER FOLDER/FILENAME.csv.gz.
Could it be that the file is in CSV.GZ format and that is why it cannot be accessed as is or do i need to give user or role a specific access for these files?
You need to fix your permissions. The error says the principal (user/role) that ran the query does not have permission to read an object on S3.
I am trying to export data from bigquery to Google Cloud Storage while using command.
EXPORT DATA OPTIONS(
uri='gs://bucket/archivage-base/Bases archivees/*.csv',
format='CSV',
overwrite=true,
header=true,
field_delimiter=';') AS
SELECT * FROM `base-012021.creation_tables.dataext`
And I have this error: Access Denied: BigQuery BigQuery: Permission denied while writing data.
I cannot understand why because the service account seems to have all the grants. And i didn't find any topic that heps me to solve the problem
Thank you !
If this is the live query you're using and you haven't redacted the real bucket name, it's probably because of the bucket string in the URI. The URI should be something like gs://your-bucket-name/prefix/path/to/output/yourfileprefix_*.csv
If you have redacted the bucket name, then check to make sure that the user (or service account) identity issuing the query has the requisite access to the bucket and objects in cloud storage.
i had same issue.
First, you need to check service acct in IAM. enter image description here
after add, you create a file json Certification. and add it in project
I have a big table (About 10 million rows) that I'm trying to pull into my bigquery. I had to upload the CSV into the bucket due to the size constraints when creating the table. When I try to create the table using the Datastore, the job fails with the error:
Error Reason:invalid. Get more information about this error at Troubleshooting Errors: invalid.
Errors:
gs://es_main/provider.csv does not contain valid backup metadata.
Job ID: liquid-cumulus:job_KXxmLZI0Ulch5WmkIthqZ4boGgM
Start Time: Dec 16, 2015, 3:00:51 PM
End Time: Dec 16, 2015, 3:00:51 PM
Destination Table: liquid-cumulus:ES_Main.providercloudtest
Source URI: gs://es_main/provider.csv
Source Format: Datastore Backup
I've troubleshot by using a small sample file of rows from the same table and just uploading using the CSV feature in the table creation without any errors and can view the data just fine.
I'm just wondering what the metadata should be set as with the "Edit metadata" option within the bucket or if there is some other work around I'm missing. Thanks
The error message for the job that you posted is telling you that the file you're providing is not a Datastore Backup file. Note that "Datastore" here means Google Cloud Datastore, which is another storage solution that it sounds like you aren't using. A Cloud Datastore Backup is a specific file type from that storage product which is different from CSV or JSON.
Setting the file metadata within the Google Cloud Storage browser, which is where the "Edit metadata" option you're talking about lives, should have no impact on how BigQuery imports your file. It might be important if you were doing something more involved with your file from Cloud Storage, but it isn't important to BigQuery as far as I know.
To upload a CSV file from Google Cloud Storage to BigQuery, make sure to select the CSV source format and the Google Storage load source as pictured below.