Data Storage

Introduction

Every modern application—from streaming platforms and chat apps to ML pipelines and financial systems—relies on a robust data storage foundation. The ability to store, retrieve, and manage massive amounts of data with low latency and high durability is central to backend architecture. Whether you're designing immutable object stores like S3, hierarchical file systems like HDFS, or metadata-heavy stores for real-time access, mastering storage design is a critical step in building scalable infrastructure.

What Is a Data Storage System?

At its core, a storage system allows users or services to write and read data—often at petabyte scale, with billions of keys or files. These systems go far beyond traditional databases. Object stores like S3 treat files as immutable blobs, optimized for massive parallelism and durability. File systems like HDFS organize data into hierarchical paths and support mutable directories. Under the hood, these systems are composed of metadata databases, chunk managers, storage nodes, replication strategies, and APIs that abstract the complexity.

Storage is not just about putting bytes on disk—it’s about doing so safely, efficiently, and at scale, while supporting real-world access patterns and SLAs.

Why Do We Need Them?

Storing a few files is easy. Storing trillions of them across data centers with low latency, zero data loss, and multi-tenant isolation is a different game. Teams building internal platforms, data lakes, ML pipelines, or analytics engines need storage systems that can:

• Handle massive concurrency
• Scale horizontally
• Ensure durability even in the face of node or region failures
• Serve varied access patterns—from putFile() to listDirectory()

Without thoughtful design, storage layers can become the bottleneck—slowing down analytics jobs, breaking user uploads, or silently dropping writes.

Comparing Object Stores to File Systems

Object stores, like Amazon S3, are built around immutable files and flat namespaces. They shine in scalability and simplicity, making them ideal for data lakes, ML workloads, and logs. File systems, like HDFS or Databricks File System, support hierarchies, rename operations, and directory structures—essential for use cases where users expect filesystem-like semantics.

Both have trade-offs. Object stores simplify concurrency and consistency models but can make metadata operations like list or rename more expensive. File systems offer more flexibility but require careful handling of consistency and directory-level locking.

As a system designer, you’ll be expected to navigate these trade-offs, depending on the functional and performance requirements at hand.

Common Design Scenarios

You’ll run into large-scale data storage challenges across many systems. Some common examples include:

• Cloud object storage systems like S3, GCS, and Azure Blob that store logs, media, and models at massive scale
• Analytics platforms using Parquet files on distributed file systems
• Data ingestion pipelines that need durable intermediate file storage before processing
• Video or image upload systems that need to chunk, version, and replicate user files
• Machine learning platforms that manage model checkpoints and training data
• Backup and archival systems that optimize for cost-effective, long-term retention
• File browsers or developer IDEs built on hierarchical file APIs
• System design interview questions that ask for “Dropbox-like” or “S3-like” storage

In each case, storage is more than an implementation detail—it’s a first-class concern that impacts performance, cost, reliability, and developer experience.

What You’ll Learn in This Module

This module will walk you through how to design, scale, and optimize modern data storage systems. Specifically, you’ll learn:

• How to build an S3-like object store that supports billions of immutable files with low latency
• How to design a hierarchical file system that supports mutability, renaming, and consistent directory views
• How to shard metadata to scale list and stat operations
• How to use chunking and replication strategies to ensure durability and parallelism
• How to expose APIs like getFile(), putFile(), and listDirectory() with robust backend support
• How to evaluate consistency trade-offs, such as eventual vs strong consistency for file metadata
• How to design for performance bottlenecks at each layer—from metadata DB to chunk manager to storage nodes
• How to approach system design questions that ask about file systems, data lakes, or storage engines

By the end of this module, you’ll be able to confidently reason about the core components of large-scale storage systems, recognize common bottlenecks, and articulate your trade-offs clearly in interviews and real-world design discussions.