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Flinkflow

Flinkflow is a declarative, low-code data streaming platform built on top of Apache Flink. Inspired by Apache Camel K, it democratizes stateful stream processing by abstracting the complexities of the Flink API into a simple, Kubernetes-native YAML DSL.


🚀 The Philosophy: Democratizing Data Engineering

Traditionally, building real-time data pipelines is a specialized engineering endeavor, requiring deep Java/Scala expertise and resulting in siloed data teams. Flinkflow breaks down this "Flink Complexity Gap" by shifting the focus from infrastructure plumbing to data logic.

Our mission is to be the "Glue Layer" for real-time event-driven architectures—empowering Data Analysts, DevOps, and Backend Developers to build, deploy, and scale enterprise-grade streaming workloads without ever touching a Maven assembly.

Why Flinkflow?

FeatureNative Java FlinkFlinkflow
AuthoringHeavy Java/Maven BoilerplateDeclarative YAML DSL
Development CycleCompile → Package → Deploy JARInstant Hot-Reload (YAML/Java/Python Snippets)
Logic Changes~10 minute CI/CD cyclesSeconds (Apply K8s CRD or YAML)
Target PersonaSpecialized Flink EngineersData Scientists, Analysts, DevOps, Backend Devs
Component ModelCustom Code / ClassesReusable, Parameterized Flowlets

✨ Features

  • Declarative YAML DSL: Define entire pipeline structures—Sources, Sinks, and Operations—in clean YAML.
  • Polyglot Logic Snippets: Inject custom logic directly into your YAML—support for Java (Janino), Python (GraalVM), and Apache Camel (YAML DSL/Simple) for transformations, filters, and flatmaps without recompiling.
  • Enterprise Integration Patterns (EIP): Build complex routing, throttling, and field extractions using the high-performance Apache Camel engine.
  • Kubernetes-Native (GitOps): Manage pipelines as Pipeline and Flowlet Custom Resources. Fully compatible with Helm, ArgoCD, and the Flink Kubernetes Operator.
  • Reusable Flowlet Catalog: Drag-and-drop capability for complex connectors (Kafka, Confluent, S3, JDBC) using parameterized components.
  • Advanced Data Mapping: Support for XSLT 3.0 via Saxon-HE for structural JSON/XML transformations (Kaoto integration).
  • Observability Built-in: Real-time monitoring of job health and throughput via a dedicated dashboard.
  • Extensible Connectors: Unified support for Kafka, S3, JDBC, HTTP Sinks, and more.
  • Enterprise Security: Native support for Kubernetes Secrets (secret:name/key) to secure credentials without hardcoding.
  • Schema Management: First-class integration with Confluent/Apicurio Schema Registry for Avro-encoded streams with automatic schema fetching.

🏢 Flinkflow Enterprise Edition & Services

Flinkflow is maintained by Talweg. While the core engine is free and open-source, we offer professional services and an enterprise-grade distribution for organization-wide streaming platforms.

FeatureFlinkflow Community EditionFlinkflow Enterprise Edition
Core DSL & Polyglot Runtimes
Agentic Bridge (LLM Integration)
Kafka, S3, JDBC Connectors
In-stream ML & AI Validation Assist
Live Hot-Reloading
Enterprise RBAC & SSO
Data Security (Format-Preserved Encryption)
Air-Gapped Container Gallery
Advanced Observability (SLA/Audit)
Proprietary High-Perf Connectors
Day 1 Setup Support (Kubernetes)
Professional Services

🛠️ Professional Services

Talweg provides expert consulting and managed services to accelerate your streaming journey:

  • Pipeline Architecture Reviews: Expert validation of your DAGs, stateful logic, and secret management.
  • End-to-End Pipeline Development: Full-lifecycle implementation support, from data source ingestion to final sink delivery.
  • Custom Flowlet Development: Building proprietary, secure connectors for your internal systems (SAP, AS/400, etc.).
  • Agentic AI & ML Strategy: Real-time inference optimization and agentic workflow design.
  • Managed Hosting: Fully managed, auto-scaling container based Flinkflow clusters on AWS, Azure, GCP, or On-Premise.

Need help or looking for enterprise features? Contact our team or visit talweg.ai.


🗺️ Visual Overview

Flinkflow bridges the gap between declarative configuration and high-performance execution.