Quick Start Guide

This guide helps you get from a fresh installation to a first working result in StreamPipes.

The guide begins with installation and then walks through one complete first-run flow in the product.

Before you start

The easiest way to install StreamPipes is the Docker-based installation.

Prerequisites

You need:

• Docker
• Docker Compose
• a recent version of Chrome, Firefox, or Edge

The Docker-based installation is supported on:

• Linux
• macOS
• Windows 10 and newer

Install Docker

Go to docs.docker.com and follow the instructions for your operating system.

Make sure Docker can be started as a non-root user where required and verify the installation with a simple test such as docker run hello-world.

Install StreamPipes

Use the installation package below to start a local or test instance.

Latest Release

Download Apache StreamPipes 0.98.0

Download ZIP

Download the latest Apache StreamPipes source release and extract the archive to a directory of your choice. The package includes the installer and the reference deployment presets.

File	Version	Release Date	Verification
apache-streampipes-0.98.0-source-release.zip	0.98.0	2025-12-15	SHA512 · PGP

Verify the release using the published signatures and the project release KEYS. See the official ASF verification instructions for the recommended procedure.

Review the current release notes before upgrading existing environments.

Installation Flow

Start StreamPipes in a few steps

1

Start the installer

Open the folder installer/compose/ and run docker compose up -d. If your environment still uses the legacy Compose binary, docker-compose up -d works as well.

For additional deployment models and broker-specific setups, continue with the deployment documentation.

2

Open the web interface

Open your browser and navigate to http://localhost:80, or use the host name of the server where StreamPipes is running, and complete the setup flow in the UI.

The default credentials are admin@streampipes.apache.org and admin.

For production-oriented setups, also review the Kubernetes-based deployment options that are part of the installation kit or check the Deployment section.

After starting StreamPipes and opening the provided URL in your browser, StreamPipes performs its initial installation flow.

You should see the following login screen:

Login Screen

After login, you are ready to work in the web interface.

If installation components do not start

If the installation does not complete successfully, the most common reason is an underpowered local environment.

Check that:

• Docker has enough memory assigned
• all required containers are running
• you restarted the stack cleanly if the first startup failed

If necessary, stop the deployment, remove the containers, and start again with a clean state.

First steps

After installation, this quick start takes you through one simple end-to-end workflow:

• create a sample adapter using the machine data simulator
• persist the events so StreamPipes creates a dataset
• create a chart from that dataset
• add the chart to a dashboard

The goal is not to explain every feature. The goal is to get one complete flow running so you understand how StreamPipes works as an industrial data platform.

1

Connect a data source

Create one adapter in Connect so StreamPipes can start receiving events.

2

Create a reusable data stream

Publish the source into the platform as a managed stream other features can use.

3

Persist the data as a dataset

Enable persistence so live events become durable operational data.

4

Create a chart

Query the dataset and confirm that the data can be visualized correctly.

5

Place the chart on a dashboard

Turn the first chart into a reusable operational view for monitoring.

If you complete these steps once, the rest of the platform becomes much easier to understand.

Step 1: Connect a first data source

Open Connect and create a new adapter.

For a first test, the Machine Data Simulator is usually the best choice because it lets you validate the platform flow without depending on an external machine or broker.

Use Case

Recommended first source

Start with the Machine Data Simulator for your first adapter. It removes protocol and network uncertainty from the first run, so you can focus on learning the StreamPipes workflow itself.

To create the adapter:

1. Open Connect.
2. Click New Adapter.
3. Choose Machine Data Simulator.
4. Configure the Machine Data Simulator, e.g., by choosing the Flow Rate sensor and a frequency of 1000ms.
5. Click Next to see a preview of the data. We don't apply any transformation script for now, so it's enough to click Next.
6. In the Configure schema section, choose the timestamp row and set the scope from Measurement to Timestamp.
7. Click Next to open the adapter settings view.

If your preferred adapter is not visible, first install the required extension in Configuration > Extensions.

Step 2: Start the adapter and persist the events

On the final adapter step, give the adapter a clear name and enable persistence.

To do that:

1. Enter a meaningful adapter name.
2. Enable Persist events.
3. Start the adapter.

If your installation offers additional startup options such as asset linking or event-rate reduction, you can leave them at their defaults for the first run unless you already know you need them.

Why persistence matters in the quick start

Persistence creates a dataset from the incoming events. That dataset becomes the basis for charts and dashboards.

Without persistence, you may still have a live stream, but you will not get the full chart and dashboard workflow that best demonstrates the current StreamPipes product model.

What success looks like

After the adapter starts successfully:

• the stream is available in StreamPipes
• events begin to flow in the live preview dialog

If no data appears later in the quick start, come back to this step first and verify that the adapter is actually running and persistence was enabled.

Step 3: Check the dataset

Next, confirm that the persisted data is available as a dataset.

To do that:

1. Open Datasets.
2. Find the dataset created from your adapter.
3. Check the event counters.
4. Open the preview.

You should see incoming records and a schema that matches the data you connected.

This is the first important checkpoint in the quick start. Once data reaches a dataset, the rest of the workflow usually becomes straightforward.

Dataset Overview

Step 4: Create a chart

Now create a visualization from the dataset.

To create your first chart:

1. Open Charts.
2. Create a new chart.
3. Select the dataset from the dropdown.
4. Keep Raw as a query type.
5. In the fields selection, choose Select all to query all fields from the dataset.
6. Use the preview to confirm the query result.
7. Switch to the Visualization tab and choose Time Series Chart as visualization type.
8. Save the chart.

Step 5: Add the chart to a dashboard

Dashboards in the current StreamPipes version are built from saved charts.

To create a dashboard:

1. Open Dashboards.
2. Create a new dashboard.
3. Open the dashboard if it does not directly open.
4. Add the chart you saved in the previous step.
5. Arrange the chart on the grid.
6. Save the dashboard.

At this point, you have completed a full first-use flow: from data onboarding to a reusable dashboard view.

Optional next step: Build a pipeline

Once the quick start flow above works, the next logical step is to create a pipeline that transforms or enriches your live stream before persisting or visualizing it.

Open Pipelines when you want to:

• filter noisy data
• calculate KPIs
• combine processing steps
• send results to sinks
• create curated downstream datasets

For many users, pipelines make more sense after the basic source-to-visualization flow is already working.

If something is missing

If you cannot complete one of the steps, the most common reasons are:

• the required adapter or sink is not installed
• the adapter is not running
• Persist events was not enabled
• the dataset is empty, so the chart query has nothing to show

Check these in order. Most first-run issues are resolved by going one step upstream.

Where to go next

After this quick start, continue with the feature guides for the areas you used:

• Connect
• Datasets
• Charts
• Dashboards
• Pipelines

If you want a better mental model before going deeper, read the concepts pages next:

• Introduction
• Terms
• Architecture