Apache Flink

Unlock the Power of Data Processing with a Less-Code Visual Interface

Apache Flink & Nussknacker integration for scalable event-driven automation and analytics.

Democratize Apache Flink data processing with Nussknacker’s low-code platform. Empower technical and business teams to collaborate effortlessly using less code.

What is Apache Flink?

Apache Flink is a powerful open-source distributed processing engine optimized for real-time, stateful computations. Designed for speed and reliability, Flink processes high-velocity data streams with millisecond latency, ensuring accurate and scalable event-driven workflows. Its fault-tolerant architecture makes it a go-to solution for organizations handling massive real-time data workloads.

Read more about Apache Flink

Should you code Flink jobs or use SQL in your Apache Flink applications?

Flink complexities of coding

Apache Flink is a powerful processing engine, but behind its capabilities lie hidden complexities that can turn development into a costly and resource-intensive endeavor. While Flink promises low-latency, high-throughput event processing, teams often struggle with steep learning curves, state management intricacies, and high operational costs.

Why is Apache Flink so challenging to manage?

  • skilled engineers: finding Flink engineers is costly and competitive due to its complex API. The talent pool is smaller, making hiring expensive and competitive,
  • development & maintenance: building new jobs or updating Flink jobs is costly, requiring expertise in parallel computing, state management, and fault tolerance,
  • complexity of stateful processing: Flink’s state management is powerful but complex, requiring careful handling of persistence, checkpointing, and consistency,
  • tooling limitations: Flink’s native monitoring tools are lacking, forcing teams to build custom solutions for observability, debugging, and performance tracking.,
  • schema evolution and compatibility: handling schema changes in Flink pipelines is complex, particularly when ensuring backward compatibility across versions.

Complex & complicated Apache Flink job code

Long SQL queries in stream processing

Is SQL good enough?

SQL is useful for data processing but lacks the flexibility needed to manage the complexities of Apache Flink applications.

When SQL reaches its limits in Apache Flink 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 leverage the power of ML without the extra overhead?

Simplify your Apache Flink with Nussknacker

Nussknacker is a less-code designer for Apache Flink, that makes data processing more accessible and efficient. It simplifies Flink job development by providing an intuitive drag-and-drop interface, reducing the need for complex coding while maintaining full flexibility and scalability.

Nussknacker integrates with Kafka, databases, data warehouses and files, allowing pipelines to be enriched with OpenAPI calls, database lookups and ML inference for data processing, enabling teams to quickly create and adapt business logic, ensuring scalability and efficiency in handling dynamic data streams.

Designed for Real-Time Data Processing

Diagram illustrating the Nussknacker (NU) structure and architecture for real-time stream processing.

Nussknacker features

  • Visual flow diagrams for building decision algorithms in Nussknacker’s real-time automation platform.flow diagrams for decision algorithms
  • Simplify development with Nussknacker’s powerful expression language for low-code stream processing.less code with powerful expression language
  • Enhance accuracy with Nussknacker’s intelligent autocompletion and real-time validation for stream processing.autocompletion and validation
  • Track performance with Nussknacker’s real-time monitoring and metrics for stream processing.real-time monitoring and metrics
  • Quickly validate and debug workflows with Nussknacker’s rapid testing tools for stream processing.rapid testing tools
  • Seamlessly transfer workflows between environments with Nussknacker’s easy migration feature.easy migration across environments
  • Deploy workflows instantly with Nussknacker’s one-click deployment feature.one-click process deployment
  • Track, review, and restore workflow versions with Nussknacker’s version history management.version history management
  • Extend and tailor Nussknacker to your needs with its customizable and extensible architecture.customisable and extensible
  • Seamless REST API integration for real-time data processing and automation.exposed REST API for automation and integration
  • Execute workflows efficiently with Nussknacker’s lightweight engine powered by Flink or Kubernetes.Run on-premises or on K8s, with support for both deployment modes
  • Real-time streaming data processing with Nussknacker for scalable event-driven workflows.unifies both streaming & batch processing
  • Seamlessly integrate Nussknacker with Ververica for enterprise-grade Apache Flink stream processing.integration with Ververica Platform
  • Seamless data streaming with Nussknacker’s Kafka source and sink interfaces.Kafka® source and sink interfaces, integration with platforms like Confluent® Cloud, Azure Event Hubs® and Aiven®
  • Seamlessly connect Nussknacker to data lakes for efficient data processing and storage.data lakes & warehouses support by Flink's connectors
  • Connect Nussknacker to databases for efficient real-time data processing and automation.databases source & sink interfaces
  • Enhance workflows with REST (OpenAPI) and database (JDBC) enrichments in Nussknacker for seamless data integration.REST (OpenAPI) and database (JDBC) enrichments
  • Integrate and enrich workflows with real-time ML model inference in Nussknacker.ML models inferring enrichments → how to?
  • Nussknacker combines open-source flexibility with powerful enterprise extensions for scalability and advanced features.open source with enterprise extensions
  • Experience Nussknacker on-premises or in the cloud with a free plan.on premises and cloud → play with it

Apache Flink use cases

Real-time marketing

Communications with customers in real-time, providing event-driven offers and actions
Read a customer story

Telco fraud management

Mitigating telco fraud by running detection algorithms on network or device signals
Read a customer story

 

Campaign Tool

Automates marketing communication by fetching data from warehouses and sending targeted messages to clients

ML model deployment & inference

Infer machine learning models in real time from complex decision algorithms
Read a blog post

Internet of Things

Automating actionable data in
- predictive maintenance
- inventory management
- smart devices
See demo

Feature engineering pipelines

Streamline the creation and transformation of data features for machine learning models with Nussknacker.

next steps