Apache Flink
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.
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
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
Run on-premises or on K8s, with support for both deployment modes
unifies both streaming & batch processing
integration with Ververica Platform
Kafka® source and sink interfaces, integration with platforms like Confluent® Cloud, Azure Event Hubs® and Aiven®
data lakes & warehouses support by Flink's connectors
databases source & sink interfaces
REST (OpenAPI) and database (JDBC) enrichments
ML models inferring enrichments → how to?
open source with enterprise extensions
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.
Telecom's credit scoring system with ML inference
Using Nussknacker with MLflow, enabled data scientists to deploy ML models directly while letting business analysts manage credit rules. This resulted in faster updates, reduced developer dependency, and more efficient credit risk assessment for their 13 million customers.
Streaming SQL alternative
Many streaming applications require significant domain knowledge and continuous updates, however SQL is neither up to the task nor user-friendly for domain experts
Real-Time Recommendations: Using Machine Learning in Clickstream Processing Pipeline
Nussknacker simplifies the integration of machine learning models into streaming data processes. Software teams can now build intelligent recommendation systems using Nussknacker, Snowplow, and MLflow.
next steps
see the demo in action
try it yourself
have any questions?