Cloud SQL Postgres & BigQuery

[KeiwoKeiwo]

Greetings,

Source: Postgres DB

Destination: Cloud SQL Postgres

Objective: be able to query the data within Cloud SQL Postgres instance from BigQuery

Step 1. Source data team will perform daily drop of entire Postgres database file into gcp storage bucket “gcp_some_bucket_name”
Step 2. GCP Cloud SQL PostGres instance would need to be created
Step 3. This instance will connect to the GCP storage bucket to load/restore the data
Step 4. Create a connection to Cloud SQL PostGres instance through BigQuery
Step 5. In BigQuery, use federated queries to query Cloud SQL Postgres instance directly

Question 1: in Step 3, How do I ensure that the GCP Postgres Cloud SQL instance will always will update itself with the latest file drop within the storage bucket?

Question 2: are there any considerations that I have missed?

Thank you,

[ms4446 · Accepted solution]

Question 1: How do I ensure that the GCP Postgres Cloud SQL instance will always update itself with the latest file drop within the storage bucket?

Method 1: Use Cloud Scheduler to run a custom job

You can create a Cloud Scheduler job that runs a custom script to check for new files in the storage bucket and load them into the Cloud SQL instance. Here's a conceptual approach:

import datetime
from google.cloud import storage

def load_data_from_bucket(bucket_name, file_name):
"""Conceptual function to load data from a storage bucket into a Cloud SQL instance."""
# The actual process of importing data into Cloud SQL from GCS would typically involve
# using the Cloud SQL Admin API or the gcloud command-line tool to trigger a restore operation.

# Check for new files in the storage bucket.
bucket = storage.Client().get_bucket(bucket_name)
blobs = bucket.list_blobs()

# You'd need a mechanism to track which files have already been processed.
# The following is a conceptual representation:
new_blobs = [blob for blob in blobs if not_already_processed(blob)]

for blob in new_blobs:
load_data_from_bucket(bucket_name, blob.name)

Method 2: Use Cloud Functions to trigger a load job

You can use Cloud Functions to trigger a load job when a new file is dropped into the storage bucket. Set up a Cloud Function that is triggered by the OBJECT_FINALIZE event on the storage bucket. The Cloud Function would then initiate the process to load the new file into the Cloud SQL instance.

Method 3: Use a third-party tool

There are several third-party tools that can help automate the process of loading data from a storage bucket into a Cloud SQL instance. For example, Fivetran is one such tool. Others include Alooma, Attunity CloudBeam, Informatica Cloud Data Integration, Matillion ETL, and Talend Open Studio for Big Data. The best tool for you would depend on your specific requirements and budget.

Additional Considerations:

• Performance: Loading a large amount of data into a Cloud SQL instance can be time-consuming, especially if the instance is under load. Consider scheduling the load job during off-peak hours.

• Locking: Loading data might lock database tables, preventing other operations. If continuous read access is needed, consider using a read replica.

• Backup and Recovery: Ensure you have a backup and recovery plan for your Cloud SQL instance. Regularly test your backup and recovery procedures

[ms4446 · Accepted solution]

Question 1: How do I ensure that the GCP Postgres Cloud SQL instance will always update itself with the latest file drop within the storage bucket?

Method 1: Use Cloud Scheduler to run a custom job

You can create a Cloud Scheduler job that runs a custom script to check for new files in the storage bucket and load them into the Cloud SQL instance. Here's a conceptual approach:

import datetime
from google.cloud import storage

def load_data_from_bucket(bucket_name, file_name):
"""Conceptual function to load data from a storage bucket into a Cloud SQL instance."""
# The actual process of importing data into Cloud SQL from GCS would typically involve
# using the Cloud SQL Admin API or the gcloud command-line tool to trigger a restore operation.

# Check for new files in the storage bucket.
bucket = storage.Client().get_bucket(bucket_name)
blobs = bucket.list_blobs()

# You'd need a mechanism to track which files have already been processed.
# The following is a conceptual representation:
new_blobs = [blob for blob in blobs if not_already_processed(blob)]

for blob in new_blobs:
load_data_from_bucket(bucket_name, blob.name)

Method 2: Use Cloud Functions to trigger a load job

You can use Cloud Functions to trigger a load job when a new file is dropped into the storage bucket. Set up a Cloud Function that is triggered by the OBJECT_FINALIZE event on the storage bucket. The Cloud Function would then initiate the process to load the new file into the Cloud SQL instance.

Method 3: Use a third-party tool

There are several third-party tools that can help automate the process of loading data from a storage bucket into a Cloud SQL instance. For example, Fivetran is one such tool. Others include Alooma, Attunity CloudBeam, Informatica Cloud Data Integration, Matillion ETL, and Talend Open Studio for Big Data. The best tool for you would depend on your specific requirements and budget.

Additional Considerations:

• Performance: Loading a large amount of data into a Cloud SQL instance can be time-consuming, especially if the instance is under load. Consider scheduling the load job during off-peak hours.

• Locking: Loading data might lock database tables, preventing other operations. If continuous read access is needed, consider using a read replica.

• Backup and Recovery: Ensure you have a backup and recovery plan for your Cloud SQL instance. Regularly test your backup and recovery procedures

[KeiwoKeiwo]

Thanks ms4446 ... really insightful.  I am very new to the GCP world and any info is much appreciated.

Question 3: if the the requirement is it also load all the the Postgres data into BigQuery, what method can be considered ( assuming budget is not a constraint )

[ms4446 · Accepted solution]

If the requirement is to also load all of the Postgres data into BigQuery, there are a few methods you can consider, assuming that budget is not a constraint:

Method 1: Use the Cloud Data Fusion service

Cloud Data Fusion is a fully-managed, cloud-native data integration service that enables you to build and manage data pipelines. It provides a number of pre-built connectors for various data sources and destinations, including Postgres and BigQuery. However, it's worth noting that Cloud Data Fusion is built on top of CDAP (Cloud Data Application Platform), which means there's a learning curve, especially for those new to GCP. To use Cloud Data Fusion:

• Create connections to your Postgres database and BigQuery dataset.
• Build a pipeline that reads data from Postgres and writes it to BigQuery.
• Utilize features like data transformation, validation, and error handling to enhance pipeline performance.

Method 2: Use the Dataflow service

Dataflow is a fully-managed service for processing streaming and batch data. To use Dataflow to load data from Postgres to BigQuery, you'd typically:

• Write a Python script using Apache Beam's JDBC connector to read from Postgres.
• Process and load the data into BigQuery using Dataflow.
• Take advantage of Dataflow's features like automatic scaling and fault tolerance.

It's important to note that Dataflow doesn't natively support reading directly from Postgres, so using a connector like the JDBC connector is essential.

Additional Consideration:

If your Postgres database is hosted on Cloud SQL for PostgreSQL, you can directly export data from Cloud SQL to BigQuery using the gcloud command-line tool, offering a more direct and straightforward method without the need for extensive pipeline setups.

[KeiwoKeiwo]

Thank you ms4446, I have a clearer understanding of what the architectural solution would encompass.