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 Kafka's Internals: A Deep Dive into Its Core Mechanics

on June 17, 2026

Unpacking Kafka’s Internals: A Deep Dive into Its Core Mechanics Introduction Kafka isn’t just a message queue; it’s a distributed streaming platform designed for high-throughput, low-latency, and fault-tolerant data ingestion. While many understand its basic publish-subscribe model, its true power lies in its meticulously engineered “under-the-hood” mechanisms. This post will peel back the layers, exploring the core architectural components, data distribution, replication, and the guarantees it provides. The Foundation: Brokers, Topics, and Partitions At its heart, a Kafka cluster consists of one or more brokers (servers).

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Demystifying Azure Networking: Beyond the Basics with VNet Peering and Private Endpoints

on May 31, 2026

When diving deep into Azure, the networking layer is often where the real magic (and sometimes the biggest headaches) happens. While the basic Virtual Network (VNet) concept is straightforward, understanding how to securely and efficiently connect resources across VNets and to on-premises environments requires a solid grasp of advanced concepts like VNet Peering and Private Endpoints. This post goes beyond the surface-level “drag and drop” of resources and explores the “under-the-hood” mechanics, architectural patterns, and practical implementation challenges you’ll face when architecting robust Azure network solutions.

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Beyond the API: A Deep Dive into Spark's Execution Engine and Performance Puzzles

on May 24, 2026

Apache Spark has become the de-facto standard for large-scale data processing, thanks to its versatility and speed. But merely knowing its DataFrame API isn’t enough to harness its full potential. True mastery comes from understanding what happens under the hood: how Spark orchestrates computations, manages memory, and optimizes queries. This deep dive will pull back the curtain on Spark’s execution engine, exploring its architecture, common bottlenecks, and advanced tuning techniques.

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Demystifying Databricks: An Under-the-Hood Look at Clusters, Photon, and Delta Live Tables

on May 20, 2026

Databricks has revolutionized how organizations approach data and AI, providing a unified platform built on Apache Spark. While its user-friendly notebooks and managed services are widely celebrated, true mastery—and the ability to troubleshoot, optimize, and build robust solutions—comes from understanding what’s happening beneath the surface. This deep dive into Databricks’ core components will pull back the curtain, exploring its architecture, internal mechanisms, and advanced features, complete with practical code and configuration examples for the DataFibers Community.

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