Postgres CDC
SOURCE

Postgres CDC

A PostgreSQL CDC (Change Data Capture) source streams real-time database changes, such as inserts, updates, and deletes, to downstream systems for integration or analysis.

Integration screenshot

Overview

Postgres CDC Source Connector

The Postgres CDC (Change Data Capture) Source Connector enables you to bring real-time data changes from your PostgreSQL database into GlassFlow with ease.
By capturing inserts, updates, and deletes, this connector ensures that your data pipelines always operate with the latest information.

Key Features

  • Real-Time Change Capture: Stream inserts, updates, and deletes as they occur in your PostgreSQL database.
  • Optimized for Performance: Leverages PostgreSQL’s native replication slots to minimize the impact on database performance.
  • Scalable and Reliable: Seamlessly handles large volumes of transactions, ensuring consistent data capture even at scale.
  • Customizable Data Selection: Filter specific tables, columns, or schemas to align with your data pipeline needs.
  • Secure Connectivity: Uses encrypted connections to ensure secure and reliable data transfer.

Use Cases

  • Real-Time Analytics: Keep your data warehouse or BI tools updated with real-time changes for actionable insights.
  • Event-Driven Applications: Trigger downstream processes or alerts based on database activity.
  • Data Synchronization: Maintain data consistency across multiple systems with live database updates.

With the Postgres CDC Source Connector, GlassFlow helps you unlock the power of real-time data, eliminating the need for time-consuming batch processing.

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Supported by our users

Do not listen to us. Listen to them.

  • GlassFlow team managed to turn my scattered thoughts regarding the data streaming into a Python masterpiece.
    ⁠Anurag Roy@Doorlabs
  • Creating a data pipeline with GlassFlow is incredibly straightforward
    ⁠Swapnil Gupta@Deloitte
  • I am fed up with overcomplicated event-based pipelines. Finally a team that will make my life easier.
    Philippe Brule@aboutphilippe
  • Love the way they team tackles the complexity of building and sharing streaming pipelines.
    Rakesh Kaswan@KaswanRakesh
  • As a Python dev starting with data streaming, you should absolutely give Glassflow a try.
    ⁠Dinesh Marimuthu@Bilthouse
  • Something is cooking here! Super excited to test your solution.
    Joash Xu@joashxu
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