GCP Postgres is a fully managed database service that excels at managing relational data. Databricks, on the other hand, is a unified analytics service that offers effective tools for data engineering, data science, and machine learning. You can integrate data from GCP Postgres to Databricks to leverage the combined strengths of both platforms. 

GCP Postgres supports only basic SQL queries, while Databricks enables you to perform complex SQL queries for advanced data analytics. Also, Databricks’s machine learning capabilities offer automation and real-time data processing, saving you time and resources. Thus, integrating GCP Postgres to Databricks can help you streamline your data analytics workflows. 

This blog explains two methods to integrate GCP Postgres and Databricks to transfer your relational data for effective data analysis. 

Why Integrate GCP Postgres to Databricks? 

You should integrate GCP Postgres into Databricks for the following reasons:

  • Databricks offers a one-stop solution for data engineering, data science, and business analytics capabilities that GCP Postgres does not provide.
  • Databricks has flexible scalability and adjusts according to your requirements to process large volumes of data, which is not feasible with GCP Postgres. 
  • Databricks has built-in tools that support machine learning workflows. GCP Postgres do not provide this feature.
  • Databricks easily integrates with any other database services. While GCP Postgres integrates easily with other GCP services like BigQuery, connecting it with other non-Google database services can be a bit complex. 
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Overview of GCP Postgres

Google Cloud PostgreSQL is a database service managed fully by Cloud SQL for PostgreSQL. It enables you to administer your PostgreSQL relational databases on the Google Cloud Platform. 

Some of the key features of GCP Postgres are as follows:

  • Automation: GCP automates administrative database tasks such as storage management, backup or redundancy management, capacity management, or providing data access.
  • Less Maintenance Cost: GCP Postgres automates most administrative tasks related to maintenance and significantly reduces your team’s time and resources, leading to lower overall costs.
  • Security: GCP provides powerful security features, such as rest and transit encryption, identity and access management (IAM), and compliance certifications, to protect sensitive data. 

Overview of Databricks

Databricks is an open analytics platform for analyzing and maintaining data. It provides tools and services for analytics, from data ingestion to deploying machine learning models. Databricks combines the best data lakes and warehouses elements to perform effective data analysis. 

Some key features of Databricks are:

  • Scalability: Databricks provide high scalability with auto-scaling features, which allow the system to adjust automatically to accommodate the increased load.  
  • Optimized Performance: This platform is optimized for advanced querying, efficiently processing millions of records in seconds. This helps you get quick and accurate results for your data analysis.  
  • Real-time Data Processing: Databricks Runtime enables you to process real-time data from various sources using Apache Spark Streaming.  

Methods to Integrate GCP Postgres to Databricks

There are two methods to transfer GCP Postgres data to Databricks. They are:

Method 1: Using Hevo Data to Integrate GCP Postgres to Databricks

Hevo Data is a no-code ELT platform that provides real-time data integration and a cost-effective way to automate your data pipeline workflow. With over 150 source connectors, you can integrate your data into multiple platforms, conduct advanced analysis, and produce useful insights.

Here are some of the most important features provided by Hevo Data:

  • Data Transformation: Hevo Data provides you the ability to transform your data for analysis with a simple Python-based drag-and-drop data transformation technique.
  • Automated Schema Mapping: Hevo Data automatically arranges the destination schema to match the incoming data. It also lets you choose between Full and Incremental Mapping.
  • Incremental Data Load: It ensures proper utilization of bandwidth both on the source and the destination by allowing real-time data transfer of the modified data.

With a versatile set of features, Hevo Data is one of the best tools to export data from GCP Postgres to Databricks files. You can use the steps below to create a data pipeline: 

Step 1: Configuration of GCP Postgres as Source

Prerequisites:

  • Make sure that your PostgreSQL server’s IP address or hostname is available. Also, install PostgreSQL version 9.4 or higher.
  • Whitelist Hevo’s IP addresses. 
  • Grant SELECT, USAGE, and CONNECT privileges to the database user.
  • To create the pipeline, you are assigned the Team Administrator, Team Collaborator, or Pipeline Administrator role in Hevo.
  • If you are using Logical Replication as Pipeline mode, ensure:

After you fulfill all the prerequisites, follow these steps to configure GCP Postgres as the source:

  • Click PIPELINES in the Navigation Bar.
  • Click + CREATE in the Pipelines List View.
  • In the Select Source Type page, select the Google Cloud PostgreSQL.
  • On the Configure Google Cloud PostgreSQL Source page, enter all mandatory information.
GCP Postgres to Databricks: Configure Source Settings 
GCP Postgres to Databricks: Configure Source Settings 

For more information on the configuration of GCP Postgres as the source, refer to the Hevo documentation.

Step 2: Configuration of Databricks as Destination 

Prerequisites:

  • Ensure you can access an active AWS, Azure, or GCP account.
  • Create a Databricks workspace in your cloud service account (AWS, Azure, or GCP). The workspace enables connections from Hevo IP addresses of your region only if the IP access lists feature is enabled in your respective cloud provider. Make sure that you have Admin access before creating an IP access list. Also, get the URL of your Databricks workspace.
  • Additionally, ensure that the following requirements are fulfilled if you want to connect to the workspace using your Databricks credentials:

You can use the Databricks Partner Connect method to establish a connection with Hevo. You can then configure Databricks as a destination by following these steps:

  • Click DESTINATIONS in the Navigation Bar.
  • Click + CREATE in the Destinations List View.
  • In the Add Destination page, select Databricks as the Destination type.
  • In the Configure your Databricks Destination page, you must specify the following details:
GCP Postgres to Databricks: Configure Source Settings 
GCP Postgres to Databricks: Configure Source Settings 

For more information on the configuration of Databricks as a destination in Hevo, refer to the Hevo documentation.

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Method 2: Using CSV file to Integrate Data from GCP Postgres to Databricks

You can use CSV files to transfer data from GCP Postgres to Databricks using the following steps: 

Step 1: Export Data from GCP Postgres to a CSV File Using Google Console

To export data from GCP Postgres in a Cloud Storage bucket to a CSV file, you can follow the below steps:

  • Go to the Cloud SQL Instances page in the Google Cloud Console
  • Click the instance name to open the Overview page of any instance. 
  • Then, click Export. Select Offload export, as it allows other operations to occur simultaneously while the export is ongoing.
  • You need to add the name of the bucket, folder, and file that you want to export in the Cloud Storage export location section. You can also click Browse to search or create a bucket, folder, or file. 
  • Click CSV in the Format section. 
  • Click on the database name in the drop-down list from the Database for Export section
  • You can use the following SQL query to specify the table from which you want to export data:
SELECT * FROM database_name.table_name;

Your query must mention a table in a specific database. Also, you cannot export an entire database in CSV format here.

  • Click on Export to start exporting data. The Export database box provides a message about the time needed to complete the export process.

Step 2: Load Data from the CSV file to Data bricks Using Add Data UI

You can use the Add Data UI in Databricks to import data from a CSV file to Databricks. Follow the steps below for this: 

  • Login to your Databricks account and go to the Navigation Pane
GCP Postgres to Databricks: Databricks  Functions Tab
GCP Postgres to Databricks: Databricks  Functions Tab
  • Click on Data>Add Data
GCP Postgres to Databricks: CSV Databricks Export
GCP Postgres to Databricks: CSV Databricks Export
  • Then, find or drag and drop your CSV files directly into the drop zone.
  • You can then either click on Create Table in UI or Create Table in Notebook.
GCP Postgres to Databricks: CSV Databricks Export
GCP Postgres to Databricks: CSV Databricks Export
  • Run the Notebook to view the exported CSV data in Databricks. 
GCP Postgres to Databricks: CSV Databricks Export
GCP Postgres to Databricks: CSV Databricks Export

You can also transfer GCP Postgres data to Databricks using the JDBC method. 

Limitations of Using CSV file to Integrate Data from GCP Postgres to Databricks

There are several limitations of using CSV files to convert GCP Postgres to Databricks, such as:

  • Low Scalability: CSV files cannot handle large volumes of data, so this method does not enable the processing of large-scale data. 
  • Limited Data Support: CSV files do not support many complex data types, so you cannot use them for advanced data analytics. 
  • Security: CSV files lack built-in security features like encryption or access control, which can potentially threaten your data. 

These limitations can create hurdles in seamless data integration from GCP Postgres to Databricks. To avoid this, you can use platforms like Hevo for efficient data integration. 

Use Cases

You can import GCP Postgres to Databricks for many important applications, such as:

  • Data Engineering: The migration allows your team of data engineers and analysts to deploy and manage data workflows. Your organization can use Databricks capabilities and features like Delta Live Tables to simplify data import and incremental change propagation. 
  • Cybersecurity: You can utilize machine learning and real-time analytics capabilities to improve your organization’s cybersecurity. These capabilities enable monitoring network traffic and identifying patterns of suspicious activities, which helps you take action against any potential data breaches. 
  • To Create Integrated Workspace: Migrating data from GCP Postgres to Databricks allows you to create an integrated workspace for your team. The multi-user environment fosters collaboration and allows your team to design new machine learning and streaming applications with Apache Spark. It also enables you to create dashboards and interactive reports to visualize results in real-time, simplifying your workflow. 

Conclusion

This blog provides comprehensive information on how to integrate GCP Postgres to Databricks by showcasing two methods of data integration. To save time and resources, you can use Hevo Data to migrate from GCP Postgres to Databricks. The zero-code data pipelines, a wide range of connectors, and an easy-to-use interface make Hevo an ideal tool for effective data integration.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also checkout our unbeatable pricing to choose the best plan for your organization.

Share your experience of GCP Postgres to Databricks integration in the comments section below!

FAQs

  1. Is there a limit to the number of PostgreSQL databases you can create on GCP Cloud SQL?
    Google Cloud SQL allows up to 40 PostgreSQL database instances per project. You can increase this limit by contacting support.
  2. What is a Databricks notebook?
    A Databricks notebook is an interactive document for writing and executing code and visualizing data. It supports several computational languages like Python, R, and SQL. 
Shuchi Chitrakar
Technical Content Writer

Shuchi is a Physicist turned journalist with passion for data story telling. She enjoys writing articles on latest technologies specifically AI and Data Science.

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