---
title: "SparkPipe | Introduction"
slug: "sparkpipe-introduction"
description: "SparkPipe is a cloud-native service that utilizes the MQTT Sparkplug specification to securely connect to any MQTT 3.1 compatible broker, capturing all the events published by any Sparkplug-enabled edge node, and routing this data to cloud-based services and applications such as Apache Kafka, etc."
updated: 2025-12-19T15:11:15Z
published: 2025-12-19T15:11:15Z
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.n3uron.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

## What is SparkPipe

[**SparkPipe**](https://aws.amazon.com/marketplace/pp/prodview-xiiaocgyyc3z2)****is a cutting-edge connectivity solution designed for **IIoT and DataOps** that seamlessly bridges operational data at the edge with advanced data processing in the cloud. This service integrates operational data into various data-driven applications, such as stream processing, advanced analytics, machine learning, and predictive maintenance. By leveraging open protocols and standardized data models, **SparkPipe** ensures the efficient and effective use of data from industrial assets and systems.

**SparkPipe** is a **cloud-native service** that utilizes the [MQTT Sparkplug v3 specification](https://sparkplug.eclipse.org/) to securely connect to any **MQTT 3.1** compatible broker, capturing all the events published by any Sparkplug-enabled edge node, and routing this data to cloud-based services and applications.

![](https://cdn.document360.io/54093ab5-6b22-4542-a265-04377931f11a/Images/Documentation/sparkpipe-home-architecture.png)

Designed to be highly reliable, secure by default, easy to configure, and simple to operate, **SparkPipe** is a no-code solution that integrates natively with **AWS** deployment systems, security features, and access mechanisms, making it accessible to a broad range of users.

**SparkPipe** transforms raw OT data into actionable insights, driving operational efficiency and unlocking the full potential of your Industrial IoT and DataOps initiatives.

## Connectors

### **Apache Kafka**

The [Apache Kafka](https://kafka.apache.org/) connector simplifies the use of Kafka for IIoT by integrating **MQTT Sparkplug** data into the Kafka messaging flow. It publishes all events to Kafka clusters, such as [Amazon MSK](https://aws.amazon.com/es/msk/) or [Confluent Cloud](https://www.confluent.io/), using a JSON representation of the Sparkplug payload. It supports various authentication methods including SASL/PLAIN, SASL/SCRAM, mTLS, and AWS IAM. Additionally, it supports data compression to reduce bandwidth and disk usage, and topic partitioning to scale throughput.

### **MongoDB Time Series**

The SparkPipe connector for [MongoDB](https://www.mongodb.com/) leverages the [MongoDB Time Series engine](https://www.mongodb.com/products/capabilities/time-series) to ingest every event in your Sparkplug network in a time series collection along with it’s context and metadata.

### **AWS IoT SiteWise**

Provides a no-code integration of Sparkplug OT data with the [AWS IoT SiteWise](https://aws.amazon.com/iot-sitewise/)service, automatically creating Asset models and Assets to match your edge data model while continuously ingesting real-time data into the IoT SiteWise time-series engine.

### **Snowflake**

The [Snowflake](https://www.snowflake.com/) connector streams Sparkplug events directly into Snowflake tables, enabling no-code, zero-ETL, and cost-effective ingestion of Sparkplug OT data into the Snowflake Data Cloud. Each event is accessible in a ready-to-use SQL table with its context and metadata for seamless analytics and integration.

### **Stdout**

The Stdout connector is provided for testing purposes; it logs the Sparkplug metrics to the console’s standard output using an easy-to-read JSON format that represents the Sparkplug payload.