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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Demystifying Databricks: An Architectural Deep-Dive into Compute, Delta, and Photon

on May 17, 2026

Demystifying Databricks: An Architectural Deep-Dive into Compute, Delta, and Photon The modern data landscape demands agility, scalability, and unified governance. While many platforms promise these, Databricks stands out with its Lakehouse architecture, built upon Apache Spark and Delta Lake. But what truly makes it tick? Beyond the notebooks and pretty dashboards lies a sophisticated orchestration of compute, storage, and metadata management. This deep-dive will pull back the curtain, exploring the “under-the-hood” mechanisms that empower Databricks to deliver on its promise.

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Hermes-Agent Under the Hood: Dissecting Its Architecture for Robust Data Ingestion

on May 13, 2026

The landscape of modern distributed systems demands sophisticated solutions for collecting, processing, and routing operational data. Logs, metrics, and traces—often generated at immense scale across heterogeneous environments—are critical for observability. While many tools exist, the hermes-agent distinguishes itself by offering a highly configurable, resilient, and performant agent designed for these exact challenges. This isn’t a generic overview. We’re diving deep into the hermes-agent’s internal workings, exploring its architectural patterns, data flow mechanisms, and how it tackles the practical complexities of distributed data ingestion.

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Demystifying Open-CLAW: Under the Hood of Cloud Native Application Lifecycle Management

on May 10, 2026

The cloud-native landscape is a dizzying array of tools and abstractions. While Kubernetes orchestrates our containers, managing the full lifecycle of complex applications – from development to deployment, scaling, and upgrades – presents its own set of challenges. This is where Open-CLAW, a project aiming to standardize and simplify Cloud Application Lifecycle Automation, steps into the spotlight. Forget generic overviews; today, we’re diving deep into the architectural patterns and practical implementation hurdles of Open-CLAW.

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Unveiling Spark's Core: A Deep Dive into its Execution and Optimization Engine

on May 6, 2026

Apache Spark has become the de-facto standard for large-scale data processing, analytics, and machine learning. While many interact with its intuitive APIs, a true mastery of Spark, and the ability to diagnose and optimize complex workloads, hinges on understanding its “under-the-hood” mechanics. This deep dive will pull back the curtain, exploring Spark’s architectural patterns, its sophisticated optimization engine, and critical aspects like shuffle management and fault tolerance. The Anatomy of a Spark Application Every Spark application runs as a set of independent processes on a cluster, coordinated by the SparkContext in the driver program.

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