Project Metamorphosis: Unveiling the next-gen event streaming platformLearn More

Streams and Tables: Two Sides of the Same Coin

We are happy to announce that our paper Streams and Tables: Two Sides of the Same Coin is published and available for free download. The paper was presented at the Twelfth International Workshop on Real-Time Business Intelligence and Analytics (BIRTE) held in conjunction with the 44th International Conference on Very Large Data Bases (VLDB) in Rio de Janeiro, Brazil, in August of this year.

The BIRTE workshop attracted many participants and hosted a keynote, research, industry and demo session as well as a panel discussion about data stream processing.

Paper summary

The paper is a joint work between Confluent and Humboldt-Universität zu Berlin that describes the Dual Streaming Model, which is the foundation of Kafka Streams’ and KSQL’s stream processing semantics:

In this paper, we introduce the Dual Streaming Model to reason about physical and logical order in data stream processing. This model presents the result of an operator as a stream of successive updates, which induces a duality of results and streams. As such, it provides a natural way to cope with inconsistencies between the physical and logical order of streaming data in a continuous manner, without explicit buffering and reordering. We further discuss the trade-offs and challenges faced when implementing this model in terms of correctness, latency, and processing cost. A case study based on Apache Kafka illustrates the effectiveness of our model in the light of real-world requirements.
Original Source

The Dual Streaming Model builds on the so-called stream-table duality, which allows you to unify data streams and relational tables into a holistic data processing model. Thus, data streams and continuously updating tables are the two core abstractions in the model. Additionally, the Dual Streaming Model decouples the handling of data that arrives later (i.e., out-of-order) from latency concerns and opens up a design space between processing cost, accepted latency and result completeness for the user that no other model offers.

Figure 1. Design Space

Figure 1. Design space

The wide adoption and growth of Kafka Streams and KSQL among enterprises shows that the Dual Streaming Model solves real-world problems across all types of industries. As a result, we are elated to share our paper for free so you can become the stream processing expert in your company and take the business to the next level.

Happy reading! 🙂

Next steps

Did you like this blog post? Share it now

Subscribe to the Confluent blog

More Articles Like This

Building a Machine Learning Logging Pipeline with Kafka Streams at Twitter

Twitter, one of the most popular social media platforms today, is well known for its ever-changing environment—user behaviors evolve quickly; trends are dynamic and versatile; and special and emergent events

An Overview of Confluent Cloud Security Controls

Whether you are a developer working on a cool new real-time application or an architect formulating the plan to reap the benefits of event streaming for the organisation, the subject

Driving Cost Savings in a Rapidly Changing Economy with Event Streaming

As businesses reassess their strategies mid pandemic, some are struggling to adjust to the new normal. The digital divide is growing wider. Leading companies have recognized the massive opportunity in