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From our blog

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

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.

Azure Networking Deep Dive: Peering, Private Link, and Secure Architectural Patterns

on July 5, 2026

Building robust and secure cloud infrastructure in Azure heavily relies on a deep understanding of its networking capabilities. While creating a Virtual Network (VNet) and subnet might seem straightforward, the true power and complexity lie in interconnecting these networks, enforcing granular security, and securely integrating Platform-as-a-Service (PaaS) offerings without exposing them to the public internet. This deep dive will go beyond the basics, exploring the “under-the-hood” mechanics of Azure VNet Peering, User-Defined Routes (UDRs), Network Security Groups (NSGs), and the transformative Azure Private Link service.

Demystifying Apache Spark: Under the Hood of its Distributed Architecture

on July 1, 2026

Apache Spark has cemented its position as a cornerstone in the big data ecosystem, lauded for its speed, ease of use, and versatility. While many developers are familiar with its high-level APIs like map, reduce, and filter, the true power and elegance of Spark lie in its sophisticated, deeply optimized execution engine. This deep-dive explores Spark’s internal architecture, its core abstractions, the magic of the Catalyst Optimizer and Tungsten Engine, and crucial performance considerations that transform a basic Spark job into a highly efficient distributed application.

Kafka's Unseen Engine: Deep Dive into Log Compaction and Idempotence

on June 28, 2026

Beyond the Basics: Unraveling Kafka’s Log Compaction and Idempotence Welcome back to the DataFibers Community! Today, we’re ditching the superficial “what is Kafka” and plunging into the intricate mechanics that make it a robust and reliable distributed streaming platform. We’ll explore two powerful, yet often misunderstood, features: Log Compaction and Idempotent Producers. These aren’t just buzzwords; they are critical for building fault-tolerant and efficient data pipelines. The Heart of the Matter: Kafka’s Log Structure Before we dive into compaction and idempotence, let’s refresh our understanding of Kafka’s fundamental data structure: the log.

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Know more about DataFibers?

From our blog

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

Azure Networking Deep Dive: Peering, Private Link, and Secure Architectural Patterns

Demystifying Apache Spark: Under the Hood of its Distributed Architecture

Kafka's Unseen Engine: Deep Dive into Log Compaction and Idempotence

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