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
Related
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 attempting to pull in data from a CSV file that is stored in an Azure Blob container and when I try to query the file I get an error of
File 'https://<storageaccount>.blob.core.windows.net/<container>/Sales/2020-10-01/Iris.csv' cannot be opened because it does not exist or it is used by another process.
The file does exist and as far as I know of it is not being used by anything else.
I am using SSMS and also a SQL On-Demand endpoint from Azure Synapse.
What I did in SSMS was run the following commands after connecting to the endpoint:
CREATE DATABASE [Demo2];
CREATE EXTERNAL DATA SOURCE AzureBlob WITH ( LOCATION 'wasbs://<container>#<storageaccount>.blob.core.windows.net/' )
SELECT * FROM OPENROWSET (
BULK 'Sales/2020-10-01/Iris.csv',
DATA_SOURCE = 'AzureBlob',
FORMAT = '*'
) AS tv1;
I am not sure of where my issue is at or where to go next. Did I mess up anything with creating the external data source? Do I need to use a SAS token there and if so what is the syntax for that?
#Ubiquitinoob44, you need to create a database credential:
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/develop-storage-files-storage-access-control?tabs=shared-access-signature
I figured out what the issue was. I haven't tried Armando's suggestion yet.
First I had to go to the container and edit IAM policies to give my Active Directory login a Blob Data Contributor role. The user to give access to will be your email address for logging in to your portal.
https://learn.microsoft.com/en-us/azure/storage/common/storage-auth-aad-rbac-portal?toc=/azure/synapse-analytics/toc.json&bc=/azure/synapse-analytics/breadcrumb/toc.json
After that I had to re-connect to the On-Demand endpoint in SSMS. Make sure you login through the Azure AD - MFA option. Originally I was using the On-Demand endpoint username and password which was not given access to the Blob Data Contributor role for the container.
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/resources-self-help-sql-on-demand
I've been reading BigQuery > Documentation > Exporting Table Data > Exporting table data and now understand that I can export data from a bigquery table into a GCS storage bucket. The example Python code provided there demonstrates how to do this when the bigquery table and the storage bucket are accessed using the same credentials:
destination_uri = 'gs://{}/{}'.format(bucket_name, 'shakespeare.csv')
dataset_ref = client.dataset(dataset_id, project=project)
table_ref = dataset_ref.table(table_id)
extract_job = client.extract_table(
table_ref,
destination_uri,
# Location must match that of the source table.
location='US') # API request
extract_job.result() # Waits for job to complete.
I am wondering if it is possible to accomplish this when the bigquery table and the storage bucket are accessed using different credentials. In my real-world situation what I want to do is export data from bigquery in projectA and store it in a bucket owned by projectB. Both projectA & projectB are accessed using different credentials. ProjectA is owned by a 3rd party and I have only been given access to bigquery, not to any of the storage buckets.
Is this possible, and if so, how? I suspect the answer is no, but just thought I would ask.
You cannot do that if you don’t have access to the 3rd party bucket. As Mikhail has pointed out it would be a big security hole if you could upload to any bucket without permission.
For the time being you can extract queries and save them in your bucket, and once you have permissions you can transfer the files to the 3rd party bucket with either Cloud Storage API or gsutil command.
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