PostgreSQL on Google Cloud SQL to BigQuery: 2 Ways to Integrate Data

Businesses are increasingly adopting cloud solutions to leverage benefits such as enhanced scalability, cost-efficiency, and cutting-edge analytics. Google Cloud SQL for PostgreSQL is one such cloud solution that serves as a robust platform for managing relational data. By integrating PostgreSQL on Google Cloud SQL to BigQuery, you can harness the potential of marketing, sales, and customer data stored on the cloud.

This allows you to derive actionable insights, predict future trends, and make informed decisions, which will help drive organizational growth and innovation.

BigQuery is a popular choice for its lightning-fast query performance across petabyte-scale datasets. With a seamless transfer of data from Google Cloud PostgreSQL to BigQuery, your business can harness the full potential of cloud-based data warehouse solutions.

Methods to Load Data from PostgreSQL on Google Cloud SQL to BigQuery

Here, we’ll cover two methods to move data from Google Cloud PostgreSQL to BigQuery. Before you get started with any of these methods, make sure you keep the following prerequisites in mind:

• PostgreSQL version of 9.4 or higher.
• Host name or IP address of your PostgreSQL server is available.
• A GCP project, with essential roles for the project assigned to the connecting Google account.
• An active billing account associated with the GCP project.

Method 1: Use CSV Files to Move Data from PostgreSQL on Google Cloud SQL to BigQuery

You can export data from PostgreSQL on Google Cloud SQL as CSV files and then load these files to BigQuery tables. This will help in seamless data transfers between both platforms. Here are the steps involved in this process:

Step 1: Export Data from PostgreSQL on Google Cloud SQL as a CSV File

To export data from a PostgreSQL instance on Google Cloud SQL to a CSV file, follow these steps:

• Log in to your Google Cloud account.
• Navigate to the Google Cloud Console > Cloud SQL Instances.
• Click on Export > Offload export.
• In the Cloud Storage export location section, specify the name of the bucket, folder, or file that you want to export. Alternatively, click on Browse to create or find a bucket, folder, or file.

If you select Browse:

• For Location, select a GCS bucket or folder for the export.
• In the Name box, add a name for the CSV file. You can also use the Location section to select an existing file.
• Click Select.
• For Format, click on CSV.
• In the Database to export section, use the drop-down menu to select the name of the database.
• Click on Export to start the export.

This will export data from the PostgreSQL instance on Google Cloud SQL as a CSV file to a GCS bucket.

Step 2: Load the CSV File to BigQuery

Now, you must load the CSV file into a BigQuery table. Here are the steps involved in this process:

• Navigate to the BigQuery Console.
• If you don’t already have a dataset in BigQuery, create one. Use the Create data set option of a project to proceed with this step.
• For the dataset to which you want to migrate your data, click on Create table for each table you want to load in this dataset.
• In the Source section of the Create table pane, select the Google Cloud Storage option.
• Click on BROWSE to select the file to load from the GCS bucket. Alternatively, you can provide a URI, like gs://mybucket/fed-samples/*.csv. This allows you to select only files with a .csv extension in the folder fed-samples and any of its subfolders.
• Specify the File format as CSV.
• Provide a name in the Table box.
• For Schema, you can select Auto-detect to automatically generate the schema.
• Click on CREATE TABLE to start the load job.

This will load the CSV files containing data from your Google Cloud PostgreSQL instance to a BigQuery table.

Here are the benefits associated with using CSV export/import for a PostgreSQL on Google Cloud SQL to BigQuery integration:

• This method is ideal for one-time or infrequent data transfers, especially of smaller datasets, between the platforms. The associated latencies of the data export or import won’t impact operations significantly.
• You don’t require any scripting or programming knowledge to use this method. Both platforms provide simple UIs, making it easy to export or import data.
• There is no need for ongoing maintenance efforts since there is no pipeline, system, or tool involved in the process. This also reduces the costs associated with this migration.
• The comprehensive documentation of Google Cloud PostgreSQL and BigQuery will help simplify the process of handling CSV exports and imports.

There are some limitations associated with the CSV export/import method to connect PostgreSQL on Google Cloud SQL to BigQuery:

• The CSV export process in Cloud SQL can take an hour or more for large databases. During this data export, you cannot perform any other operations.
• This method isn’t suitable for real-time or near-real-time data migrations owing to the latencies involved in the export and manual import.
• You cannot automate the migration of data from PostgreSQL on Google Cloud SQL to BigQuery. Every time you want to move data, you must perform the repetitive tasks manually, making it effort-intensive and time-consuming.

Method 2: Use a No-Code Tool to Automate the Migration Process from PostgreSQL on Google Cloud SQL to BigQuery

No-code tools are an efficient alternative to the previous method. Some essential features of no-code tools include:

• Fully Managed: No-code tools are often fully managed, and the platform providers offer constant updates, maintenance, and bug fixes. This ensures you have access to the latest features, integrations, and security measures. 
• Customization: Some no-code tools offer pre-built transformation options, allowing you to use pre-defined logic for your data transformation requirements.
• Scalability: No-code tools are designed to scale up or scale down to flexibly adapt to changing data integration needs. Not only can these tools handle small-scale migrations, but they can also accommodate growing datasets for large-scale data migrations.
• Reduced Errors: No-code tools typically use pre-built connectors, which help reduce the possibility of errors in the ETL process when compared to solutions that involve manual efforts.
• Rapid Deployment and Setup: No-code tools, with an intuitive interface and readily available connectors, help quickly set up data integration pipelines. You can set up and deploy your ETL pipelines in just a few clicks.

Hevo Data, an effective no-code integration tool, can help you set up a PostgreSQL on Google Cloud SQL to BigQuery ETL pipeline with minimal effort. It is a fully managed platform that automates the data integration process. This includes extracting data from the source, transforming it to match the schema of the destination, and loading it into the destination.

A PostgreSQL on Google Cloud SQL to BigQuery migration with Hevo Data will involve the following steps:

Additional Prerequisites

• Whitelist Hevo’s IP addresses.
• Grant SELECT, USAGE, and CONNECT privileges to the database user.
• For Logical Replication Pipeline mode,
  • Enable Log-based incremental replication.
  • Make the PostgreSQL database instance as the master instance.

Step 1: Configure Google Cloud PostgreSQL as the Data Source

Step 2: Configure BigQuery as the Data Destination

What Can You Achieve With PostgreSQL on Google Cloud SQL BigQuery Integration?

A PostgreSQL on Google Cloud SQL to BigQuery integration can help data analysts answer questions such as:

• Are there any recurring patterns in customers’ purchase behavior, such as a correlation with certain marketing campaigns or seasonality?
• Which teams have the quickest response times?
  • How often do customer complaints or queries get escalated, and which teams handle the highest number of escalations?
• What is the lifetime value of customers acquired through different marketing channels?
  • Which marketing channels have the highest customer acquisition rate?

Conclusion

Migrating data from PostgreSQL on Google Cloud SQL to BigQuery can provide your business with real-time insights to stay ahead of the competition.

You can integrate these two platforms using two methods. The first method involves the exporting of Google Cloud PostgreSQL data as CSV files and loading these files to BigQuery tables. This method is associated with drawbacks such as being time-consuming, effort-intensive, and lacking real-time and automation capabilities.

Consider using no-code tools like Hevo Data to overcome these limitations of the CSV export/import method. It is a fully managed tool and includes benefits like readily available built-in integrations, auto schema management, and scalability to simplify the migration process.

Hevo Data can also help you set up a near-real-time data transfer pipeline between any two platforms. With an intuitive interface, 150+ built-in connectors, and data transformation capabilities, Hevo is an effective solution for your data integration needs.

If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest up to 1 million records.

Schedule a demo to see if Hevo would be a good fit for you, today!