5 posts tagged with "Storage"

Every modern big data platform owes a debt to one 2003 research paper. When Google published The Google File System, it described how to store petabytes of data reliably on top of cheap, failure-prone commodity machines. That paper directly inspired the Hadoop Distributed File System (HDFS), the storage layer that launched the open-source big data movement. Understanding GFS vs HDFS is the fastest way to understand why distributed storage looks the way it does today.

Apache Hadoop and Snowflake both store and process large datasets at scale — but they sit at opposite ends of the modern data architecture spectrum. Hadoop is a self-managed open-source stack where storage and compute live on the same cluster. Snowflake is a fully managed cloud data warehouse that separates storage from compute and bills per second of query time.

In 2026, the question rarely is "which one is better?". It is "which workload belongs on which platform, and what does each cost over five years?". Many enterprises run both: Hadoop (or its successor S3-based lakehouse) for cheap raw storage and large-scale ETL, Snowflake for governed analytics and BI on top.

This guide compares Hadoop vs Snowflake across architecture, query performance, total cost of ownership (TCO), and use cases — with a decision matrix and FAQ at the end.

As organizations move workloads to the cloud, one of the most common questions is: should I use HDFS or Amazon S3 as my Hadoop storage layer? Both are valid choices, but they have very different performance profiles and operational characteristics.

The s3a:// filesystem connector in Hadoop lets you use Amazon S3 as a drop-in replacement for HDFS storage. It's the foundation for cost-effective data lake architectures where compute and storage are decoupled. This guide covers configuration, performance tuning, and production best practices.

Three SQL engines dominate the Hadoop data lake landscape: Apache Hive, Presto, and Trino (Presto's open-source fork). Each evolved to solve different problems. Picking the wrong one leads to either unbearably slow interactive queries or over-engineered infrastructure for simple batch ETL. Here's how they compare.