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Read moreEmpower AI with streaming using Apache Kafka and Flink.
This blog post is for customer architects who want to ensure their data infrastructure is ready—not just for today’s needs, but for the demands of tomorrow. We’ll explore how event-driven architecture and data streaming, powered by technologies like Apache Kafka and Apache Flink, are transforming the data landscape—and how Vivicta can help you lead the way.
Traditionally, data integration relied on point-to-point APIs or batch jobs that transferred data between systems at set times. However, in a world where decisions must be made in milliseconds, this model introduces friction, latency and risk.
Enter event-driven architecture (EDA). In this model, systems publish and subscribe to events — changes in state or new data — without needing to know who will consume them or when. This decoupling allows for greater flexibility, scalability and reuse.
At the heart of this approach are real-time data products: curated, governed and reusable data assets built on top of event streams. These products combine raw data from multiple sources, enriching it in real time and making it available for operational and analytical use case
AI thrives on data, but not just any data. To generate insights, make predictions and automate decisions, it needs high-quality, timely and context-rich information.
This is where data streaming comes in. Unlike traditional data pipelines, data streaming enables a continuous flow of data. When an event occurs — for example, when a customer places an order or a sensor detects a fault — the data is immediately published to a streaming platform such as Apache Kafka. From there, the data can be processed, enriched and consumed by downstream systems in real time.
Apache Flink adds another layer of intelligence by enabling complex event processing, filtering, and transformation in real time. Together, Kafka and Flink form a robust basis for AI-ready data ecosystems.
Consider these best practices when building a resilient and scalable data integration strategy:
Move away from tightly coupled APIs and embrace asynchronous, event-based communication. This allows systems to evolve independently and scale more effectively.
Kafka provides a robust backbone for real-time data flow, ensuring high throughput and low latency. It supports a wide range of use cases, including fraud detection and supply chain optimization.
Flink provides powerful stream processing capabilities, including filtering, joining and windowing. This enables you to derive insights and trigger actions as data flows through your systems.
Rather than exposing raw data, create well-defined data products with clear ownership, metadata and access controls. This improves the quality, reusability and compliance of the data.
Use AsyncAPI specifications to document event contracts and ensure consistency between producers and consumers. Tools such as Confluent's Schema Registry help to enforce data formats and prevent integration issues.
In practice, these principles are already delivering value across industries. Below are some examples.
Retail: Real-time product catalogues and order tracking systems enable faster fulfilment and better customer experiences.
Insurance: Streaming data enables instant risk assessment and automated decision-making in claims processing.
Banking: Event-driven integrations support fraud detection and personalised customer engagement.
In each case, the ability to process and act on data in real time is transformative.
We offer a comprehensive data streaming service, developed using Confluent's managed platform for Apache Kafka and Flink. We help our customers to design, deploy and scale real-time data products, focusing on:
Whether you are just starting out or looking to optimise an existing platform, our proven expertise and best practices will help you succeed.
Anton is a TOGAF-certified integration architect with over 25 years' experience. He has worked with over 50 customers in multiple industries. He has experience with various integration platforms, including Azure Logic Apps, API Management, Functions, Cloud Pak for Integration, and Confluent Kafka. He has also worked with cloud-based and hybrid integration platforms for several years.