An Open Source Big Data Bus

The goal of DataFibers is
to simplify big data processing
and be your Data Heros

Simple, but Powerful

  • Various data connects and sinks
  • Stream and Batch trasnforms
  • Topics, Schema and Meta-data
  • Data enginering and science together

Complete Guideline

  • The Insignt of Big data processing
  • The value of Kappa architecture
  • Considerations of design
  • Best practice with DataFibers
  • Rest API Document

Services

  • Community Support
  • Professional Support
  • Consulting Service
  • Technical trainings
  • Meetup and Conference

Simple

Simply build, plug, and immediately subscribe your data anywhere at anytime.

FLEXIBLE

Batch, Stream, Real-Time, or Hybrid data processing are right at hand.

Powerful

Data landing, discovery, transfer, transform, cache, mining are all in one place.

Consulting

Explore the oppotunities from DataFibers and Big Data to business success

Support

We actively support development/deployment requests on DataFibers and queries on big data use cases.

Training

We have provided on-line and off-line big data professional trainings across world.

Know more about DataFibers?

Check out <<DataFibers Complete Guideline>>

Read Our EBook

From our blog

Here, we are sharing our experience and best practice of using DataFibers as well as other big data technology.

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kimi-model: Deep Dive into its Architecture and Implementation Patterns

on August 23, 2026

kimi-model: Beyond the Surface - A Deep Dive into Architecture and Implementation In the rapidly evolving landscape of data processing and AI, new models and frameworks emerge with remarkable frequency. Among these, kimi-model has garnered attention for its unique approach. This post aims to move beyond a high-level overview and delve into the architectural underpinnings, practical implementation challenges, and design patterns that make kimi-model tick. We’ll explore its internal workings, discuss common pitfalls, and provide actionable insights for leveraging its full potential.

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Deep Dive into Kimi's 200K Context Window: Architecture, Challenges, and Optimizations

on August 19, 2026

The landscape of Large Language Models (LLMs) is rapidly evolving, with a constant push towards greater capabilities. One of the most significant recent advancements has been the dramatic expansion of context windows. Moonshot AI’s Kimi Chat has emerged as a frontrunner, boasting an impressive 200,000-token context window. This isn’t just a marginal improvement; it fundamentally changes how developers can interact with and leverage LLMs for complex, long-form tasks. But how do models like Kimi achieve such unprecedented context lengths without succumbing to the quadratic scaling nightmares of traditional Transformers?

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Under the Hood: Deconstructing Robotics with ROS 2 Architectures

on August 16, 2026

Under the Hood: Deconstructing Robotics with ROS 2 Architectures Robotics is no longer a futuristic fantasy; it’s a rapidly evolving field pushing the boundaries of automation, AI, and embedded systems. From industrial manipulators in factories to autonomous delivery drones and sophisticated surgical assistants, robots are becoming indispensable. But what truly makes these complex machines tick? How do their diverse components – sensors, actuators, and decision-making algorithms – communicate and cooperate seamlessly?

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Under the Hood: Deconstructing Godot's Engine Architecture

on August 12, 2026

The Godot Engine has rapidly grown in popularity, admired for its open-source nature, intuitive editor, and efficient workflow. While many appreciate its user-friendly interface, the true power and flexibility of Godot lie in its elegant, component-based architecture and robust extensibility mechanisms. This deep dive aims to pull back the curtain, exploring the core engine design, GDScript’s internals, and how Godot enables high-performance extensions. The Recursive Heartbeat: Godot’s Scene Tree and Nodes At the very foundation of every Godot project is the Scene Tree.

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