Best Microservices Tools for Salesforce Data Cloud

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Compare the Top Microservices Tools that integrate with Salesforce Data Cloud as of October 2025

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This a list of Microservices tools that integrate with Salesforce Data Cloud. Use the filters on the left to add additional filters for products that have integrations with Salesforce Data Cloud. View the products that work with Salesforce Data Cloud in the table below.

What are Microservices Tools for Salesforce Data Cloud?

Microservices tools and frameworks are comprehensive platforms and libraries that assist in the development and management of microservices-based applications. These tools and frameworks offer essential features such as service discovery, fault tolerance, load balancing, and API management to streamline the design of microservices architectures. They support developers in creating services that are decoupled, independently deployable, and scalable. Additionally, these frameworks often come with built-in support for integrating with container orchestration systems like Kubernetes and Docker. By using these tools and frameworks, teams can enhance the resilience, scalability, and maintainability of their applications. Compare and read user reviews of the best Microservices tools for Salesforce Data Cloud currently available using the table below. This list is updated regularly.

  • 1
    Amazon Simple Queue Service (SQS)
    Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating message oriented middleware, and empowers developers to focus on differentiating work. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available. Get started with SQS in minutes using the AWS console, Command Line Interface or SDK of your choice, and three simple commands. Use Amazon SQS to transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be available. SQS lets you decouple application components so that they run and fail independently, increasing the overall fault tolerance of the system.
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  • 2
    Google Cloud Pub/Sub
    Google Cloud Pub/Sub. Scalable, in-order message delivery with pull and push modes. Auto-scaling and auto-provisioning with support from zero to hundreds of GB/second. Independent quota and billing for publishers and subscribers. Global message routing to simplify multi-region systems. High availability made simple. Synchronous, cross-zone message replication and per-message receipt tracking ensure reliable delivery at any scale. No planning, auto-everything. Auto-scaling and auto-provisioning with no partitions eliminate planning and ensures workloads are production-ready from day one. Advanced features, built in. Filtering, dead-letter delivery, and exponential backoff without sacrificing scale help simplify your applications. A fast, reliable way to land small records at any volume, an entry point for real-time and batch pipelines feeding BigQuery, data lakes and operational databases. Use it with ETL/ELT pipelines in Dataflow.
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  • 3
    AWS Lambda
    Run code without thinking about servers. Pay only for the compute time you consume. AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume. With Lambda, you can run code for virtually any type of application or backend service - all with zero administration. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call it directly from any web or mobile app. AWS Lambda automatically runs your code without requiring you to provision or manage servers. Just write the code and upload it to Lambda. AWS Lambda automatically scales your application by running code in response to each trigger. Your code runs in parallel and processes each trigger individually, scaling precisely with the size of the workload.
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