BigQuery to PostgreSQL: 2 Easy Methods of Replication

Are you looking to execute operational Database activities on your BigQuery data by transferring it to a PostgreSQL database? Well, you’ve come to the right place. Data replication from BigQuery to PostgreSQL is now much easier.

This article will provide a quick introduction to PostgreSQL and Google BigQuery. You’ll also learn how to set up your BigQuery to PostgreSQL integration using two different techniques. To figure out which way of connecting BigQuery to PostgreSQL is ideal for you, keep reading.

Prerequisites

If you’ve gone over the following points, you’ll have a much simpler time understanding how to set up the BigQuery to PostgreSQL integration:

• A PostgreSQL account that is operational.
• A Google Cloud Platform account that is active.
• Working knowledge of databases and data warehouses.
• Knowledge of Structured Query Language is required.
• A clear sense of the type of data that will be transferred.

Introduction to PostgreSQL

PostgreSQL is a high-performance, enterprise-level open-source relational database that enables both SQL (relational) and JSON (non-relational) querying. It’s a very reliable database management system, with more than two decades of community work to thank for its high levels of resiliency, integrity, and accuracy. Many online, mobile, geospatial, and analytics applications utilize PostgreSQL as their primary data storage or data warehouse.

The source code for PostgreSQL is freely accessible under an open source license, allowing you to use, change, and implement it as you see appropriate. PostgreSQL has no license fees, thus there’s no risk of over-deployment, saving you on the cost of unused software. PostgreSQL’s passionate community of contributors and enthusiasts finds problems and patches on a regular basis, adding to the database system’s overall security.

To know more about PostgreSQL, visit this link.

Benefits of PostgreSQL

• Code quality: Every line of code that goes into PostgreSQL is evaluated by numerous specialists, and the whole development process is community-driven, allowing issue reporting, patches, and verification to happen promptly.
• SQL and NoSQL: PostgreSQL may be used to store JSON documents and as a typical SQL relational database management system for rows of transactional or statistical data. This adaptability can cut costs while also improving your security. You won’t need to recruit or contract with the skills needed to set up, administer, protect, and upgrade different database systems if you use just one database management system.
• Data Availability & Resiliency: Privately supported versions of PostgreSQL provide extra high availability, resilience, and security capabilities for mission-critical production settings, such as government agencies, financial services corporations, and healthcare organizations.
• Geographic data: Because Postgres has some outstanding features for managing spatial data, businesses frequently rely on it for applications that utilize it. Postgres, for example, offers particular data types for geometrical objects, and PostGIS makes creating geographical databases simple and quick. Postgres has been particularly popular with transportation and logistics organizations as a result of this.

Introduction to Google BigQuery 

Google BigQuery is a serverless, cost-effective, and massively scalable Data Warehousing platform that includes built-in Machine Learning capabilities. Its processes are carried out using the Business Intelligence Engine. It combines fast SQL queries with the processing power of Google’s infrastructure to manage business transactions, data from several databases, and access control limits for people seeing and querying data.

UPS, Twitter, and Dow Jones are some companies that extensively utilize BigQuery. For instance, UPS uses BigQuery to forecast the precise amount of shipments it will receive for its various services. And, Twitter uses BigQuery to assist with ad changes and the aggregation of millions of data points per second.

To know more about Google BigQuery, visit this link.

Benefits of Google BigQuery

• Distributed Architecture: Google’s distributed design automatically distributes BigQuery’s computation among computing resources, removing the need to maintain compute clusters. Competing products sometimes involve unique scaling (and price) of certain computing clusters, which can be difficult to manage over time.
• Flexible Pricing Choices: Because Google allocates resources dynamically, prices are also dynamic. Google provides a pay-as-you-go option in which you pay for the data put into BigQuery as well as per-query charges. They provide a reporting tool as part of this method to provide more visibility into usage and expense patterns. For bigger customers, fixed pricing is also an option.
• Fully Managed: Google handles the backend configuration and optimization for BigQuery because it is a fully managed service. This is far more straightforward than competing systems, which require you to select the number and kind of clusters to construct and maintain over time.
• High Availability: To provide high availability, BigQuery automatically duplicates data between zones. It also loads balances automatically for maximum performance and to reduce the effect of any hardware problems. This differs from rival technologies, which usually concentrate on a sing.

Methods to Set up BigQuery to PostgreSQL Integration

Method 1: Manual ETL Process to Set Up BigQuery to PostgreSQL Integration

Note: Enable your PostgreSQL database to accept connections from Cloud Data Fusion before you begin. We recommend using a private Cloud Data Fusion instance to perform this safely.

Step 1: Open your Cloud Data Fusion instance.

In your Cloud Data Fusion instance, enter your PostgreSQL password as a secure key to encrypt. See Cloud KMS for additional information on keys.

• Go to the Cloud Data Fusion Instances page in the Google Cloud console.
• To open your instance in the Cloud Data Fusion UI, click View instance.

Step 2: Save your PostgreSQL password as a protected key.

• Click System admin > Configuration in the Cloud Data Fusion UI.
• Make HTTP Calls by clicking the Make HTTP Calls button.

• Select PUT from the dropdown list.
• Enter namespaces/default/securekeys/pg_password in the path field.
• Enter "data":"POSTGRESQL_PASSWORD" in the Body field. POSTGRESQL_PASSWORD should be replaced with your PostgreSQL password.
• Hit Send.

Step 3: Connect to Cloud SQL For PostgreSQL.

• Click the menu in the Cloud Data Fusion UI and go to the Wrangler page.
• Click the Add connection button.
• To connect, select Database as the source type.

• Click Upload under Google Cloud SQL for PostgreSQL.

• A JAR file containing your PostgreSQL driver should be uploaded. The format of your JAR file must be NAME-VERSION.jar. Rename your JAR file if it doesn’t meet this format before uploading.
• Click the Next button.
• Fill in the fields with the driver’s name, class, and version.
• Click the Finish button.
• Click Google Cloud SQL for PostgreSQL in the Add connection box that appears. Under Google Cloud SQL for PostgreSQL, your JAR name should display.

• Fill in the connection fields that are necessary. Select the secure key you stored previously saved in the Password field. This guarantees that Cloud KMS retrieves your password.

• In the Connection string field, enter your connection string.
  

• Replace the following:
  • DATABASE_NAME: the Cloud SQL database name as listed in the Databases tab of the instance details page.
  • INSTANCE_CONNECTION_NAME: the Cloud SQL instance connection name as displayed in the Overview tab of the instance details page.

Example:

• Allow the Cloud SQL Admin API to be used.
• Give the service-PROJECT NUMBER@gcp-sa-datafusion.iam.gserviceaccount.com account the following IAM roles:
  1. Cloud SQL Admin (roles/cloudsql.admin)
  2. Cloud Data Fusion Admin (roles/datafusion.admin)
  3. Cloud Data Fusion API Service Agent (roles/datafusion.serviceAgent)
     

See Manage access for additional information on granting roles.

• To check that the database connection can be made, click Test connection.
• Click the Add connection button.

Let’s get into the next method to export BigQuery to PostgreSQL.

Method 2: Using Hevo Data to Set Up BigQuery to PostgreSQL Integration

Hevo Data, a No-code Data Pipeline, automates the direct transfer of data from 150+ data sources to PostgreSQL Databases and other Data Warehouses, BI tools, or any other destination of your preference. Hevo fully automates the process of not only importing data from your selected source, but also enriching and converting it into an analysis-ready format, all without writing a single line of code. Its fault-tolerant architecture guarantees that data is handled securely and consistently, with no data loss.

Hevo Data handles all of your data preprocessing needs, allowing you to focus on core business operations and gain a better understanding of how to create more leads, maintain customer retention lifecycle, and grow your firm to new heights of profitability. It provides a consistent and dependable solution for managing data in real-time, ensuring analysis-ready data is always available in your designated destination.

The following are the steps to load data from BigQuery to PostgreSQL using Hevo Data:

• Step 1: Link your Google Cloud Platform account to Hevo’s platform. Hevo includes a built-in BigQuery integration that allows you to connect to your account in minutes.
• Step 2: Choose PostgreSQL as your destination and begin transferring data.

When I saw Hevo, I was amazed by the smoothness with which it worked so many different sources with zero data loss.

– Swati Singhi, Lead Engineer, Curefit

That’s it about BigQuery connect to PostgreSQL. Now, it’s your turn to decide which method suits your requirement.

Conclusion

This article offers an overview of PostgreSQL and BigQuery, as well as a description of their features. Furthermore, it described the two approaches for transferring data from BigQuery to PostgreSQL. Although successful, the manual approach will take a lot of time and resources. Data migration from BigQuery to PostgreSQL is a time-consuming and tedious operation, but with the help of a data integration solution like Hevo, it can be done with little work and in no time.

Visit our Website to Explore Hevo

Businesses can use automated platforms like Hevo Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience.

Moreover, Hevo offers a fully-managed solution to set up data integration from 150+ other data sources(including 30+ free data sources) and will let you directly load data to the destination of your choice. It will automate your data flow in minutes without writing any line of code.

Not sure about purchasing a plan? Sign Up for a 14-day full feature access trial and simplify your Data Ingestion & Integration process. You can also check out our unbeatable pricing and decide the best plan for your needs. 

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