Turning Data at REST into Data in Motion with Kafka StreamsTurning Data at REST into Data in Motion with Kafka Streams
From Confluent Blog
Another great achievement for our Team: we are now on Confluent Official Blog with one of our R&D projects based on Event Stream Processing.
Event stream processing continues to grow among business cases that have been reliant primarily on batch data processing. In recent years, it has proven especially prominent when the decision-making process must take place within milliseconds (for ex. in cybersecurity and artificial intelligence), when the business value is generated by computations on event-based data sources (for ex. in industry 4.0 and home automation applications), and – last but not least – when the transformation, aggregation or transfer of data residing in heterogeneous sources involves serious limitations (for ex. in legacy systems and supply chain integration).
Our R&D decided to start an internal POC based on Kafka Streams and Confluent Platform (primarily Confluent Schema Registry and Kafka Connect) to demonstrate the effectiveness of these components in four specific areas:
1. Data refinement: filtering the raw data in order to serve it to targeted consumers, scaling the applications through I/O savings
2. System resiliency: using the Apache Kafka® ecosystem, including monitoring and streaming libraries, in order to deliver a resilient system
3. Data update: getting the most up-to-date data from sources using Kafka
4. Optimize machine resources: decoupling data processing pipelines and exploiting parallel data processing and non-blocking IO in order to maximize hardware capacity These four areas can impact data ingestion and system efficiency by improving system performance and limiting operational risks as much as possible, which increases profit margin opportunities by providing more flexible and resilient systems.
At Bitrock, we tackle software complexity through domain-driven design, borrowing the concept of bounded contexts and ensuring a modular architecture through loose coupling. Whenever necessary, we commit to a microservice architecture.
Due to their immutable nature, events are a great fit as our unique source of truth. They are self-contained units of business facts and also represent a perfect implementation of a contract amongst components. The Team chose the Confluent Platform for its ability to implement an asynchronous microservice architecture that can evolve over time, backed by a persistent log of immutable events ready to be independently consumed by clients.
This inspired our Team to create a dashboard that uses the practices above to clearly present processed data to an end user—specifically, air traffic, which provides an open, near-real-time stream of ever-updating data.
If you want to read the full article and discover all project details, architecture, findings and roadmap, click here: https://bit.ly/3c3hQfP.