Apache Kafka
Simplify Stream Processing with Drag-and-Drop Power
Empower teams to collaborate effortlessly with Nussknacker. Blend technical and domain expertise to visually design, deploy, and refine Kafka streaming processes using a low-code approach.
What is Apache Kafka?
Kafka is a powerful open-source event-streaming platform built to process and manage real-time data at scale. Designed for high performance, low-latency and fault tolerance data pipelines and applications. Kafka enables the seamless flow of data between systems, making it a cornerstone for event-driven architectures and real-time analytics. It excels at capturing, storing, and processing massive streams of events with unmatched reliability.
Read more about Apache Kafka
Should you code services or use SQL in your Apache Kafka applications?
Hidden Complexities of Coding
Coding streaming processing with Apache Kafka may seem manageable at first, but hidden challenges can quickly turn it into a complex and resource-intensive task. From costly expertise to the intricacies of event-driven systems, these challenges can delay projects and increase operational difficulties.
What makes streaming coding hard to manage?
- complex event-driven architecture: designing event-driven systems for real-time processing is complex and error-prone,
- integration challenges: connecting Kafka to external systems often requires custom connectors and complex logic, increasing effort and maintenance,
- lengthy development process: building new or making changes to existing streaming processes takes time, but businesses can't afford delays,
- schema evolution and compatibility: managing schema changes without breaking consumers is difficult in large, evolving pipelines,
- tooling limitations: Kafka’s built-in monitoring and management tools are often insufficient, requiring teams to build additional custom tools.
Is SQL Good Enough?
While SQL is a widely recognized and valuable tool for data processing, it often falls short when tackling the complexities of modern Kafka streaming processing applications.
When SQL reaches its limits in Kafka stream processing
- complex business logic: multi-step transformations can grow into thousands of lines, becoming hard to maintain,
- error handling and recovery: lacks of native support for retries, compensating actions, or dead-letter queues,
- stateful processing: managing state across events, such as sessionization or pattern detection, exceeds simple syntax,
- external integrations: connecting to APIs or external systems requires capabilities beyond standard SQL,
- performance tuning: optimizing resource-heavy operations in real-time requires fine-grained control,
- flexibility: adapting to evolving requirements in a fast-moving environment can be challenging with SQL's rigid structure.
Why not take the best of both and get ML integration without any extra effort?
Low-Code Apache Kafka
Nussknacker is a low-code platform for building, deploying, and managing real-time data processing workflows. It simplifies complex stream processing by offering an intuitive drag-and-drop Kafka interface, eliminating the need for extensive coding.
With native support for Apache Kafka & Flink, real-time events can be enriched using REST APIs, database lookups and ML inference. Nussknacker enables teams to quickly create and adapt business logic, ensuring scalability and efficiency in handling dynamic data streams.
Designed for Real-Time Streaming Data Processing
Nussknacker Features
flow diagrams for decision algorithms
less code with powerful expression language
autocompletion and validation
real-time monitoring and metrics
rapid testing tools
easy migration across environments
one-click process deployment
version history management
customisable and extensible
exposed REST API for automation and integration
running on Flink or K8s-based lightweight engine
real-time event stream processing
integration with Ververica Platform
Kafka® source and sink interfaces
integrates with Kafka-compatible platforms like Confluent® Cloud, Azure Event Hubs® and Aiven® for Apache Kafka®
REST (OpenAPI) and data base (JDBC) enrichments
ML models inferring enrichments → how to?
open source with enterprise extensions
on premises and cloud → play with it
Stream Processing Use Cases
Real-time marketing
Communications with customers in real-time, providing event-driven offers and actions
RTM System
Fraud management
Mitigating fraud by running detection algorithms on network or device signals
Fraud Platform
Recommendation systems
Assisting the Point Of Sale, displaying suggestions about what to offer and how to proceed with a customer
Next Best Action Asistance
ML model deployment & inference
Infer machine learning models in real time from complex decision algorithms
ML Inference
Internet of Things
Automating actionable data in
- predictive maintenance
- inventory management
- smart devices
IoT Example
Feature engineering pipelines
Streamline the creation and transformation of data features for machine learning models with Nussknacker.
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Next-Generation Real-Time Rating Systems
This blog post explains the rationale behind the shift from asynchronous architectures to stateful stream processing in real-time rating systems.
Your Visual Interface for Apache Kafka
Simplify stream processing with Nussknacker’s Low-Code Apache Kafka GUI. Empower users to handle real-time streams without deep technical knowledge and make Kafka accessible to everyone. Contact us to start building your visual streaming solutions today.
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