A fully managed messaging service that allows for event-driven systems and real-time analytics on Google Cloud Platform. Key features include:

• Scalability: Automatically scales to handle high-throughput workloads.
• Durability: Ensures message delivery with at-least-once delivery guarantees.
• Flexibility: Supports both push and pull delivery models.
• Integration: Easily integrates with other Google Cloud services.

• GO: pubsub, to wrap and simplify pubsub. Example is at go-pubsub-sample
• nodejs: google-pubsub, to wrap and simplify @google-cloud/pubsub. Example is at pubsub-sample

• The libraries to implement this flow are:
  • mq for GOLANG. Example is at go-pubsub-sample
  • mq-one for nodejs. Example is at pubsub-sample

Common use cases include event-driven architectures, log collection, and streaming analytics.

• Scenario: Building applications where different components communicate via events (e.g., microservices)
• Benefit: Decouples components, allowing independent scaling and development

• Scenario: Aggregating logs from multiple applications and systems.
• Benefit: Centralized logging and monitoring, improving visibility and debugging capabilities.

• Scenario: Collecting and analyzing data streams from various sources like IoT devices, social media, or user activity.
• Benefit: Enables real-time data processing and analytics, providing timely insights and actions.

• Type: Managed real-time messaging service.
• Use Case: Event-driven architectures, real-time analytics.
• Scalability: Automatically scales.
• Delivery Guarantees: At-least-once delivery.
• Integration: Tight with Google Cloud services.
• Delivery Models: Push and pull.

• Type: Managed message queuing service.
• Use Case: Decoupling and scaling microservices, asynchronous tasks.
• Scalability: Automatically scales.
• Delivery Guarantees: At-least-once, FIFO (exactly-once).
• Integration: Deep integration with AWS services.
• Delivery Models: Primarily pull, with long polling.

• Type: Open-source event streaming platform.
• Use Case: High-throughput messaging, event sourcing, log aggregation.
• Scalability: High with partitioned topics.
• Delivery Guarantees: Configurable (at-least-once, exactly-once).
• Integration: Broad ecosystem with various connectors.
• Delivery Models: Pull-based consumer groups.

• Management: Pub/Sub and SQS are managed services, while Kafka is typically self-managed or via managed services like Confluent.
• Use Case Focus: Pub/Sub and Kafka are ideal for real-time processing, whereas SQS is great for decoupling microservices and handling asynchronous tasks.
• Delivery Models: Pub/Sub supports push and pull, SQS supports pull with long polling, and Kafka primarily uses pull with consumer groups.
• Scalability: All three are highly scalable, but Kafka offers the most control over performance tuning.
• Integration: Pub/Sub integrates well with Google Cloud, SQS with AWS, and Kafka has a broad integration ecosystem.

• Google Pub/Sub: If you're using Google Cloud and need a managed, real-time messaging solution.
• Amazon SQS: For reliable, scalable message queuing in AWS environments.
• Apache Kafka: For complex event streaming and log aggregation, with a need for fine-tuned control and a broad integration ecosystem.

Please make sure to initialize a Go module before installing core-go/pubsub:

go get -u github.com/core-go/pubsub

Import:

import "github.com/core-go/pubsub"