Application Data Streaming with Apache Kafka and Swim

Streaming Audio: Apache Kafka® & Real-Time Data

How do you set data applications in motion by running stateful business logic on streaming data? Capturing key stream processing events and cumulative statistics that necessitate real-time data assessment, migration, and visualization remains as a gap—for event-driven systems and stream processing frameworks according to Fred Patton (Developer Evangelist, Swim Inc.) In this episode, Fred explains streaming applications and how it contrasts with stream processing applications. Fred and Kris also discuss how you can use Apache Kafka® and Swim for a real-time UI for streaming data.
Swim's technology facilitates relationships between streaming data from distributed sources and complex UIs, managing backpressure cumulatively, so that front ends don't get overwhelmed. They are focused on real-time, actionable insights, as opposed to those derived from historical data. Fred compares Swim's functionality to the speed layer in the Lambda architecture model, which is specifically concerned with serving real-time views. For this reason, when sending your data to Swim, it is common to also send a copy to a data warehouse that you control.
Web agent—a data entity in the Swim ecosystem, can be as small as a single cellphone or as large as a whole cellular network. Web agents communicate with one another as well as with their subscribers, and each one is a URI that can be called by a browser or the command line. Swim has been designed to instantaneously accommodate requests at widely varying levels of granularity, each of which demands a completely different volume of data. Thus, as you drill down, for example, from a city view on a map into a neighborhood view, the Swim system figures out which web agent is responsible for the view you are requesting, as well as the other web agents needed to show it.
Fred also shares an example where they work with a telephony company that requires real-time statuses for a network infrastructure with thousands of cell towers servicing millions of devices. Along with a use case for a transportation company needing to transform raw edge data into actionable insights for its connected vehicle customers.
Future plans for Swim include porting more functionality to the cloud, which will enable additional automation, so that, for example, a customer just has to provide database and Kafka cluster connections, and Swim can automatically build out infrastructure.

EPISODE LINKS

• Swim Cellular Network Simulator
• Continuous Intelligence - Streaming Apps That Are Always in Sync
• Using Swim with Apache Kafka
• Swim Developer
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlist
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Intro to Event-Driven Microservices with Confluent
• Use PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

챕터

1. Intro (00:00:00)

2. Distributed actor system - real-time streaming (00:02:01)

3. Stateful microservices (00:07:36)

4. Streaming joints (00:15:03)

5. Real-time streaming data UI (00:19:28)

6. Web agents—a data entity in the Swim ecosystem (00:27:36)

7. Integration with Apache Kafka (00:29:49)

8. It's a wrap (00:36:29)