The key to working with SwiftRiver is to understand the workflow and the terminology we've created around that workflow and the data itself.

To help us visualize this workflow, you'll notice as we proceed that just about all these terms are around the "river" metaphor to help describe trying to consume information straight from Twitter or other high velocity, high volume online services.  Let's briefly review what those terms are now; don't worry, we'll go into more depth on each in the Working with Data section of this guide.

What we're ultimately working with in SwiftRiver are Drops.  A drop is a basic unit of content inside of SwiftRiver.  Currently, this could be an individual tweet from Twitter, a single blog post or article delivered via RSS, or an SMS message.

As drops appear in SwiftRiver, they enter via Channels.  A channel is the vehicle for receiving drops, which could be RSS, SMS, Twitter, JSON, or XML.  As these drops appear, they come into the River.  The river is all of the data you've received, the complete torrent of drops that come from the predefined channels.  The river is there to help you get the big picture and see what's coming in.

Finally, we have filters, streams, and buckets.  A filter is the mechanism for reducing a River from a torrent of drops to a more manageable set.  Those drops, whose contents are defined by a filter or a combination of filters, are the streams, and those more manageable sets are the buckets they land in.  These buckets are likely the areas you'll be doing the most work once the information is collected and you're ready to process it.

Let's Recap all that

Channels are setup to receive drops of information, which form the river of data that we then make manageable but creating filtered streams to fill the smaller buckets setup to help us start to make sense of everything coming in.  Buckets are where you'll likely be working, processing data from within SwiftRiver or using our API to send that data into additional, external systems.

Other definitions

• Identity: The originator of a drop from a specific channel i.e. a Twitter account, a Facebook account, a phone number, an email address. Identities are automatically extracted when a drop is “siphoned” from a channel.
• Source: Comprises one or more identities, and could be a person or organization. Unlike identities that are automatically extracted, sources are subjective and are put together by users in the system.