---
title: ""
url: https://docs.catalyst.zoho.com/en/cloud-scale/help/data-streams/use-cases/
last_updated: 2026-07-09T07:28:21Z
---

<!-- ---
title: "Use Cases"
page_type: "home"
service: "Cloud Scale"
description: "Real-world use cases for Data Streams including event-driven streaming, serverless functions, lead distribution, retail synchronization, and mobile devices."
tags: ["Use Cases", "Real-time Streaming", "Event-Driven", "Signals Integration"]
type: "general"
parent_path: "/en/cloud-scale/help/"
weight: 3
name: "Data Streams"
---

# Use Cases

Data Streams can be used for a variety of real-time streaming scenarios in your Catalyst applications. The following are some common use cases of Data Streams:

## Event-Driven Streaming with Catalyst Signals

[Catalyst Signals](http://link) can be used to automatically publish application or system events to a Data Streams channel. When events such as a row insertion in Data Store, a user signup in Authentication, or a record update in Zoho CRM occur in connected services, Signals can route these events to a Data Streams channel through rules and targets. All subscribers connected to the channel then receive the event data in real time, without requiring any manual publishing logic.

This enables you to build fully event-driven streaming architectures where data flows automatically from source services to subscriber applications. Refer to the [Publishing Methods](http://link) section to learn more about configuring Signals as a publishing trigger.

## Serverless Functions as Publishers

[Catalyst Serverless Functions](http://link) can be used to publish events to a Data Streams channel after executing business logic. For example, when an [Advanced I/O function](http://link) processes an order request, generates a report, or completes a background task, it can call the `publishData()` method (or `publish_data()` in Python) to stream the result to a channel. Multiple subscribers, such as monitoring dashboards, notification services, or analytics pipelines, can then receive these updates instantly.

This approach is ideal for scenarios where your backend service generates the data to be streamed, and multiple downstream consumers need to react to it in real time. Refer to the [Quick Start Guide](http://link) for step-by-step publishing instructions.

## Real-Time Lead Distribution

Lead assignment systems that rely on external decision engines or business logic services can use Data Streams to distribute leads in real time. When a new lead enters the system, it can be published as an event to a Data Streams channel and processed by external logic systems that determine the appropriate sales representative. Because Data Streams delivers messages sequentially and requires each message to be acknowledged before the next one is delivered, the lead distribution process is handled reliably and in order.

## Retail and Point-of-Sale Synchronization

Retail systems operating across multiple branches or point-of-sale devices can use Data Streams to synchronize updates such as customer profiles, loyalty points, pricing information, or promotions. When data changes in one location, the update can be published to a Data Streams channel and streamed to all connected systems, ensuring that every branch operates with the latest information. This eliminates the need for periodic polling or batch synchronization and enables all connected endpoints to receive changes as they occur.

## Mobile and Intermittently Connected Devices

Mobile or intermittently connected devices can subscribe to Data Streams channels and receive events while they have network connectivity. If a device temporarily loses its connection, it can reconnect and resume the stream using the `'-2'` (resume) subscribe type to receive all events that were published during the disconnection period. This ensures that mobile clients remain synchronized with backend systems even in unstable network environments.

> **Note:** The resume capability is subject to the channel's 48-hour data retention period. Events older than 48 hours are no longer available. Refer to the [Data Retention](https://docs.catalyst.zoho.com/en/cloud-scale/help/data-streams/key-concepts/#data-retention) section for more details.

Data Streams can also be implemented in the following types of applications:

* Live dashboards and analytics platforms
* Chat and messaging applications
* Notification and alerting systems
* IoT device communication and telemetry
* Collaborative editing and real-time document updates -->