ETL PostgreSQL data to Databricks

Renta ELT is a simple way to create data pipelines in minutes. Not months.

Trusted by teams at

PostgreSQL is a powerful open-source database trusted by businesses worldwide.
Renta Marketing ELT seamlessly connects PostgreSQL to Databricks, enabling fast, secure, and automated data transfers for advanced analytics.
Empower your team with reliable, up-to-date insights and streamline your marketing and business intelligence workflows.

Set up your ELT pipeline in 3 simple steps

Select PostgreSQL as your data source and securely authenticate your account to initiate the data extraction process.

Selecting PostgreSQL as the data source in Renta.

1Connect your source

Select PostgreSQL as your data source and securely authenticate your account to initiate the data extraction process.

2Identify the destination

3Configure and synchronize

The data structure of the PostgreSQL API

Renta supports 7 methods of data export using the PostgreSQL API.

Tables

Represents collections of structured data organized in rows and columns. Each table defines a specific entity, such as users or transactions, and stores records relevant to that entity.

Views

Virtual tables generated by SQL queries, allowing users to present and query data from one or more tables without modifying the underlying data.

Schemas

Logical containers that group related database objects, such as tables, views, and functions, to organize and manage data access within a PostgreSQL database.

Indexes

Structures that improve the speed of data retrieval operations on tables by providing efficient access paths based on specified columns.

Functions

Reusable SQL or procedural code blocks that perform operations or calculations, enabling automation and encapsulation of business logic within the database.

Sequences

Database objects that generate unique numeric values, commonly used for auto-incrementing primary keys in tables.

Triggers

Procedures that are automatically executed in response to specific events on a table or view, such as insert, update, or delete operations.

Use cases

Unlock actionable PostgreSQL insights

Sync customer purchase data from PostgreSQL to Databricks for churn prediction analysis
Combine web analytics from PostgreSQL with Databricks to optimize ad spend by channel
Aggregate product usage logs in Databricks for real-time feature adoption insights
Enrich sales pipeline data in PostgreSQL with Databricks ML models for lead scoring
Automate daily revenue reporting by merging PostgreSQL and Databricks datasets
Detect anomalies in transaction data by running Databricks analytics on PostgreSQL exports