How to Connect PostgreSQL to Databricks Within Minutes?

Looking to prep your PostgreSQL data for big analytics?

While PostgreSQL supports transactional workloads and moderate analytics, its row-based storage limits large-scale analytics and BI operations. 

However, the columnar storage and Lakehouse architecture of Databricks is built to handle such workloads, enabling faster ETL processing and highly efficient analytics.

This guide walks you through two ways to move data from PostgreSQL to Databricks, explaining both manual and automated approaches for seamless data integration.

Additional resources ahead!

How to Connect PostgreSQL to Databricks?

Data from PostgreSQL can be ingested into Databricks either through a modern ETL tool or a JDBC connection. Here, we discuss the two methods:

• Using Hevo’s no-code data pipelines
• Using the JDBC driver

Method 1: Replicating Data From PostgreSQL to Databricks Using Hevo

Hevo provides a fully managed pipeline for migrating PostgreSQL data to Databricks, offering native support for PostgreSQL data types and full compatibility with Databricks’ Delta Lake format.

Check out our 1-minute demo below to see the seamless integration in action!

Here’s a detailed breakdown of the steps:

Step 1: Configure PostgreSQL as the source

The workflow:

• Go to “+ CREATE PIPELINE” in your Hevo dashboard.
• From the list of sources, select “PostgreSQL.”

• Enter PostgreSQL credentials:
  • Pipeline Name: Unique name for the pipeline.
  • Host Name: Enter your PostgreSQL server host.
  • Port: Default 5432 (if not customized).
  • Database Name: Specify the database to replicate.
  • Username & Password: Enter valid credentials with read access.
  • SSL Mode: Choose based on your PostgreSQL setup.

• Click “TEST & CONTINUE” to verify connectivity.

• Finally, choose the tables or entire schema you want to replicate.

Step 2: Configure Databricks as the destination

The workflow:

• Go to “Destinations” and click “+ Add Destination.”
• Choose “Databricks” from the list of destination options.

• Provide the following details:
  • Destination Name: Unique name to identify the destination.
  • Server Hostname: The hostname of your Databricks cluster.
  • Database Port: Port used to connect to Databricks (default: 443).
  • HTTP Path: The path to your Databricks SQL warehouse.
  • PAT: Personal access token generated from Databricks.
  • Schema Name: Schema in Databricks for storing PostgreSQL tables.

• Click “TEST CONNECTION” to ensure Hevo can write to Databricks.
• Once tested, select “SAVE & CONTINUE.”

Step 3: Set up the data pipeline & advanced settings

The workflow:

• Set a prefix to be added to every table Hevo creates in Databricks.
• This avoids name collisions and makes lineage obvious in the Lakehouse.

Choose one of the following based on how you plan to query downstream:

A. Replicate JSON fields as JSON strings and array fields to strings.

• Hevo writes JSON objects and arrays as STRING columns.
• Best when you prefer schema-on-read in Databricks.

B. Replicate JSON structure as such while collapsing arrays into strings.

• Hevo keeps the object structure intact, while arrays are stored as a STRING.
• Best for quick, readable nested fields with minimal parsing.

Pro tips:

• For BI tools needing flat tables, use a table prefix with Option B for faster querying.
• If payloads change frequently, Option A maintains stability and allows parsing in Databricks when needed.

Method 2: Connecting PostgreSQL to Databricks Using JDBC Driver

Below is the step-by-step process to set up a PostgreSQL-to-Databricks migration manually:

Step 1: Install the JDBC driver on the Databricks cluster

The workflow:

• In your Databricks workspace, navigate to your cluster’s Libraries tab, then click “INSTALL NEW.”
• Choose Jar for the “Library Type” and “Upload” for the source.
• Upload the PostgreSQL JDBC driver JAR. For example, cdata.jdbc.postgresql.jar.

Depending on your environment (AWS, Azure, or GCP), Databricks supports library installation via DBFS or Maven coordinates, but uploading a JAR file works universally.

Note: On Azure, some setups use ADLS/DBFS for uploads, but direct library upload is still supported.

Step 2: Configure JDBC driver and connection URL

Once the driver is installed, you can configure the connection from a Databricks notebook.

• Specify the driver class and construct a JDBC URL with your PostgreSQL credentials.
• If using the CData driver, include the RTK property (unless beta versions don’t require it).

driver = "cdata.jdbc.postgresql.PostgreSQLDriver"<br>url =<br>"jdbc:postgresql:RTK=5246…;User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5432;"

• driver tells Databricks which JDBC driver to use (CData PostgreSQL).
• url builds the JDBC connection string with all details needed to connect.

Step 3: Load PostgreSQL data into a Spark DataFrame

The workflow:

• Once the connection string is ready, use it to connect to PostgreSQL from Databricks.
• Data in Spark DataFrames can be used for transformations, joins, and analytics.

The JDBC driver lets you read PostgreSQL tables or queries into Spark DataFrames:

remote_table = (<br>spark.read<br>        .format("jdbc")<br>        .option("driver", driver)<br>        .option("url", url)<br>        .option("dbtable", "Orders")  # Replace with your table name or a SQL query<br>        .load()<br>)<br>display(remote_table)

• spark.read.format(“jdbc”): Instructs Spark to read data via JDBC.
• .option(“driver”, driver): Specifies which JDBC driver to use. 
• .option(“url”, url): Sets the JDBC URL for PostgreSQL.
• .option(“dbtable”, “Orders”): Specifies the PostgreSQL table or SQL query.
• .load(): Executes the query and loads the data into a Spark DataFrame.
• display(remote_table): Displays the data in the Databricks notebook for verification.

Step 4: Configure authentication and database settings

Ensure the connection uses the correct parameters:

• User & Password: The PostgreSQL account you want to authenticate with.
• Server & Port: Host address and port (default is 5432).
• Database: If left empty, PostgreSQL defaults to the user’s database.

Step 5: Use the data in Databricks

After loading PostgreSQL data into a Spark DataFrame, you can:

• Execute Spark SQL queries on the DataFrame for analysis.
• Apply DataFrame transformations such as filtering, aggregations, and joins.
• Save the data to Databricks tables or storage for downstream use.

Challenges Faced While Replicating Data

You can face some challenges while replicating your data from PostgreSQL to Databricks using JDBC Driver.

Setting up data pipelines across multiple environments is expensive. The configuration of a pipeline may change after it gets deployed into multiple settings.

This method requires technical expertise, and you will need to utilise your engineering bandwidth, which can be a complex task.

What is PostgreSQL?

PostgreSQL is an open-source, general-purpose, object-relational database management system, or ORDBMS. It is widely used and provides advanced features along with standard compliance. Some of these features include complex queries, foreign keys, triggers, and views—all supporting transactional integrity with full ACID compliance. Inherent in it is vast extensibility for custom data types, functions, and operators.

Use Cases

• It can power dynamic web applications with its robust performance and scalability.
• Through its PostGIS extension, PostgreSQL can deal with geospatial data in an exemplary manner. This makes it very well-suited for applications of spatial data analysis, including mapping, location-based services, and geographic information systems.
• PostgreSQL is used in the financial sector for its strong transactional support and data integrity features.

What is Databricks?

Databricks provides a unified platform for data analytics that empowers simplicity in big data processing and machine learning, tightly integrating with Apache Spark for the power of an open-source analytics engine. It provides a cloud-based environment that simplifies the data pipeline from ingesting data to analyzing it. Principally, Databricks provides collaborative notebooks, automated cluster management, and advanced analytics capabilities that enable data engineers and data scientists to work more collaboratively on big data projects.

Use Cases

Some key use cases of Databricks are listed below:

• Databricks excels in data engineering workloads like ETL processes, data cleaning, and data transformation.
• Databricks comes with out-of-the-box machine learning capabilities that are tightly integrated into popular libraries to help you in end-to-end machine learning workflows. 
• The integrated development environment, with collaboration notebooks, is available across the platform so that data scientists can work seamlessly.

Why Integrate PostgreSQL with Databricks?

Let’s start by understanding why there is a need to integrate PostgreSQL with Databricks. Time Complexity, Features, and data compression are the main factors that play an essential role in doing so.

1) Time Complexity 

It may take longer than expected to ensure a seamless PostgreSQL replication effort. Unexpected problems frequently necessitate further study, which might slow down the process of replicating data. Therefore, it is crucial to include time for Ad-hoc research to tackle unforeseen problems in the project timeline from the start.

Your teams can immediately query the data using a “simple-to-use” interface without requiring time-consuming operations. By separating storage from computing and offering limitless scalability, Databricks democratizes data access and improves time complexity.

2) Difference in features on the cloud vs. on-premises 

Working with PostgreSQL in the Cloud is different from working with it locally. The PostgreSQL Cloud databases lack many PostgreSQL extensions, and suppliers sometimes lock some default settings, which restricts PostgreSQL setups and functionality. To assure scalability, several businesses have been forced to go back to on-premises Postgres from Cloud ones.

Databricks is a fully managed solution that supports functionalities like Big data and machine learning. It uses the unified Spark engine to support machine learning, graph processing, and SQL queries. The libraries in Databricks increase developers’ productivity.

3) Data Compression

PostgreSQL stores tables as columns rather than rows. Additionally, as data from the same columns are more likely to be comparable, it aids with data compression — PostgreSQL lacks these functionalities.

Databricks supports easy and quick access to information. There are no issues with data compression. Databricks also provides additional features for building streaming apps using production-quality machine learning and increasing the use of data science to aid in decision-making. You might want to transfer data from PostgreSQL to Databricks to implement Databricks features and overcome limitations.

To overcome these issues, you can transfer data from PostgreSQL to Databricks.

Why Use Hevo to Connect PostgreSQL with Databricks?

Here’s how Hevo challenges the normal to beget the new ‘exceptional.’

• Reliability at Scale – With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. 
• Monitoring and Observability – Monitor pipeline health with intuitive dashboards that reveal every stat of the pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs 
• Stay in Total Control – When automation isn’t enough, Hevo offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.    
• Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps the source schema with the destination warehouse so that you don’t face the pain of schema errors.
• 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-feature free trial.
• Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in data flow. 

Additional Resources for PostgreSQL Integrations and Migrations

• How to load data from PostgreSQL to BigQuery
• Postgresql on Google Cloud Sql to Bigquery
• MySQL to Databricks
• Migrate Data from Postgres to MySQL
• How to migrate Data from PostgreSQL to SQL Server

Let’s Put It All Together

In this article, you get to learn about the need for PostgreSQL to Databricks data transfer, as well as got to know about the methods to carry out the process.

Don’t forget to express your experience in the comment section, employing a data pipeline from PostgreSQL to Databricks using Hevo.

Check out this video to learn how Hevo seamlessly replicates data from wide data sources. See how to migrate data from Azure PostgreSQL to Databricks for powerful data analytics and reporting.

Initiate your journey with Hevo today and enjoy fully automated, hassle-free data replication for 150+ sources. Hevo’s free trial gives you limitless free sources and models to pick from, support for up to 1 million events per month, and a spectacular live chat service supported by an incredible 24/7 support team to help you get started. Sign up for Hevo’s 14-day free trial and experience seamless data migration.

FAQs to replicate data from PostgreSQL to Databricks