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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Unpacking the Databricks Lakehouse: A Deep Dive into Delta, Photon, and Unity Catalog

on July 19, 2026

Databricks has rapidly evolved from a managed Spark platform to the cornerstone of many modern data architectures, often termed the ‘Lakehouse’. While the high-level benefits—simplicity, scale, and collaboration—are well-known, the true power lies in its meticulously engineered components working in concert. This deep dive aims to peel back the layers, exploring the “under-the-hood” mechanisms of key Databricks technologies: Delta Lake, Photon, and Unity Catalog, alongside practical implementation considerations for DataFibers engineers.

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Advanced RAG Architecture: From Naive Pipelines to Production-Grade Retrieval and Re-ranking Engines

on July 15, 2026

Productionizing Retrieval-Augmented Generation (RAG) is far more complex than setting up a basic LangChain pipeline with a default vector database. While “Naive RAG” (embed-retrieve-generate) works well for simple demos, it consistently fails in production environments under complex queries, scale, and noisy data. This deep-dive architectural guide explores the engineering patterns required to transition from naive prototypes to high-performance, production-grade RAG systems. We will analyze advanced chunking strategies, multi-stage retrieval, query translation, and hybrid search integration.

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Unpacking Apache Spark: A Deep Dive into its Architectural Core

on July 12, 2026

Apache Spark has revolutionized big data processing, becoming an indispensable tool for data engineers and scientists alike. While many are familiar with its high-level APIs like DataFrames and Spark SQL, understanding the intricate mechanisms “under the hood” is crucial for building robust, performant, and scalable applications. This deep dive will pull back the curtain, exploring Spark’s architectural patterns, its sophisticated optimization engine, and the practical challenges of distributed execution.

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Hermes Agent: Under the Hood of a Resilient Distributed Event Ingestion System

on July 8, 2026

The modern data landscape is a torrent of events, flowing from countless sources to various analytical and operational sinks. Ensuring every single event is captured, processed, and delivered reliably, without loss or performance degradation, is a monumental challenge. Enter Hermes Agent: a robust, distributed event ingestion system designed to act as a resilient intermediary, buffering, processing, and delivering high volumes of events even in the face of network glitches, downstream backpressure, and system failures.

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