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AWS S3: How Cheap Hard Drives Power a Massively Scalable Storage System

2025-09-24

This article unveils the astounding scale and underlying technology of Amazon S3. S3 leverages inexpensive HDDs, overcoming the limitations of slow random I/O through massive parallelization, erasure coding, and clever load balancing techniques (like the 'power of two choices'). This enables millions of requests per second, ultra-high throughput, and exceptional availability. S3's data storage strategy incorporates random data placement, continuous rebalancing, and the smoothing effect of scale to avoid hot spots. Parallelization at the user, client, and server levels further boosts performance. Ultimately, S3 has evolved from a backup and image storage service to a foundational component of big data analytics and machine learning infrastructures.

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Kafka's Genesis: A Data Integration Saga

2025-08-24

In 2012, LinkedIn faced a massive data integration challenge. Their existing data pipelines were inefficient, unscalable, and suffered from data silos. To solve this, they created Apache Kafka. This article delves into Kafka's origins, revealing its design was driven by the need for robustness, scalability, real-time capabilities, and seamless data integration. It explores how LinkedIn cleverly utilized Avro schemas and a schema registry to ensure data consistency and compatibility, ultimately achieving efficient data management. The article also reflects on Kafka's lack of first-class schema support and contrasts it with newer approaches like Buf's schema-first philosophy.