Data Lakes
Storing raw, unstructured, and semi-structured datasets at Petabyte scales cheaply.
What you'll learn
- Architectural Abstraction
- Fault Containment Bounds
- Stateless Service Workers
TL;DR
Storing raw, unstructured, and semi-structured datasets at Petabyte scales cheaply.
Visual System Topology
Data Lakes Execution Topology
Concept Overview
Data Lakes is a key architectural blueprint and system pattern designed to solve structural distributed system challenges. Storing raw, unstructured, and semi-structured datasets at Petabyte scales cheaply.
Architecting scalable, resilient systems is the primary objective of system design. Software architects must select correct design patterns to decouple compute tiers, establish reliable datastores, implement low-latency caches, and coordinate state updates safely. Understanding the exact mechanical behaviors of Data Lakes allows you to make informed decisions that ensure your production platform scales reliably to handle massive traffic.
Key Architectural Pillars
Architectural Abstraction
Decoupling implementation interfaces to ensure Data Lakes can evolve independently without breaking clients.
Fault Containment Bounds
Isolating failures within decoupled service borders to stop cascading crashes during database overloads.
Stateless Service Workers
Designing app instances that do not save active session states locally, enabling perfect horizontal scale.