In advance of the upcoming Streamr Marketplace for realtime data, we are publishing a mini-series of technical blog posts about data integration. This is the second post out of three:

To learn the basics, check out the first part. This post will go into more detail about different kinds of integration patterns, and show real-world examples of each.

The three patterns

We will cover three different patterns, each with their own pros and cons:

Pushing from the source (ideal) Bridging from a streaming source Bridging by polling a source

Before we start

In Streamr, each data point belongs to a Stream. Data points (also called events or messages) are timestamped pieces of information, such as measurements from a sensor, or messages in a chat. Depending on the use case, a single Stream could contain data from a single source, or it could be a “firehose” of data from multiple sources.

Streamr ingests data via an API. The easiest way to interact with the API from your own software is by using a Streamr client library. Currently one is available for JavaScript, and other programming languages will follow.

If a library isn’t available for your language, you can call the API endpoints using any HTTP library. For simple examples of this, check out the previous post in this series.

To authenticate, you will need your API key. You can find and manage your keys on your Profile page.

1. Pushing from the source