Amazon SQS
SOURCE

Amazon SQS

Easily bring messages from Amazon SQS queues into GlassFlow for real-time processing and transformation with the Amazon SQS Source Connector

Integration screenshot

Overview

Amazon SQS Source Connector

Amazon SQS is a fully managed message queuing system by AWS.
The Amazon SQS (Simple Queue Service) Source Connector allows you to stream messages from your SQS queues into GlassFlow for real-time data processing.
This connector helps integrate message-driven workflows, making it easy to capture and transform data as it arrives in your SQS queues.

Key Features

  • Real-Time Message Streaming: Capture and stream messages from SQS queues into GlassFlow as soon as they are added.
  • Flexible Message Processing: Process and transform data in real-time before sending it to downstream systems.
  • Scalable Integration: Seamlessly handle high-volume, distributed message queues with minimal overhead.
  • Customizable Message Filters: Filter and process specific types of messages based on your needs.
  • Secure Data Transfer: Ensures secure and encrypted message handling for reliable and safe data processing.

Use Cases

  • Event-Driven Architectures: Stream events from SQS queues into GlassFlow to trigger downstream processes or analytics.
  • Real-Time Data Processing: Process and transform incoming messages on the fly for faster insights.
  • Message-Queue Integration: Integrate SQS with other data systems and applications for streamlined workflows.

With the Amazon SQS Source Connector, GlassFlow makes it easy to capture and process messages in real time, transforming raw data into actionable insights instantly.

Connector Details

To configure the AWS SQS connector, you will need the following details:

AWS Access Key ID: Your AWS access key ID.
AWS Secret Access Key: Your AWS secret access key.
AWS Region: The AWS region where your SQS queue is located.
SQS Queue URL: The URL of the SQS queue where the events will be published.

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  • 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.
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