Learning Path

Architectures for Large-Scale Data Processing

Distributed Systems Fundamentals → SQL-on-Hadoop Engines

12 Knowledge Nodes · 12 Learning Steps · v0 Active

A learning path for data engineers to understand the architectural principles and trade-offs of large-scale data processing systems. Starting with distributed systems fundamentals and data models, it progresses through MapReduce, the Hadoop ecosystem, Spark, and distributed SQL engines like Presto. The path emphasizes practical comparisons and design considerations for real-world data pipelines.

0 Likes

Knowledge Map

Explore the complete knowledge graph with this learning route highlighted, or switch to Route to focus on the route topology.

Explore Knowledge Structure

Explore all concepts and relationships across the complete graph.

12 nodes18 edges1 relationships

Drag to pan · Scroll to zoom · Click a node to inspect

Click a node to preview its details without leaving this path. Scroll to zoom, or open Fullscreen to explore the whole map.

Learning Route

12 learning steps · 3 phases. Click any step to inspect it and see it on the Knowledge Map.

Phase 1

Foundation

4 steps

Phase 2

Core Concepts

4 steps

Phase 3

Advanced Topics

4 steps