100 Data Engineering and AI Concepts — Day 3 of 7 — Spark and Distributed Systems

2026-04-26 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Cloud Computing & IT Infrastructure, Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

This installment, part of a 7-day series on data engineering and AI concepts, focuses on the internal mechanics of Spark and distributed systems. It explains how clusters of machines process large datasets efficiently and reliably. Key concepts include distributed systems, defined as independent computers appearing as a single coherent system, and parallelism, which involves the simultaneous execution of multiple tasks. Data parallelism specifically refers to splitting large datasets into partitions for concurrent processing across different CPU cores. The content also covers fault tolerance, detailing a system's ability to continue operating despite component failures, such as a Spark worker node dying, by rerunning lost tasks using DAG lineage. Finally, it introduces data locality, emphasizing the principle of moving computation to data rather than data to computation to optimize performance by reducing network transfers.

Key takeaway

For Data Engineers designing scalable data pipelines, understanding distributed system principles is crucial. Your architecture should prioritize data locality to minimize network overhead and implement fault tolerance mechanisms like DAG-based recovery to ensure system resilience against node failures. This approach will significantly improve processing efficiency and reliability for terabyte-scale data.

Key insights

Distributed systems enable scalable, fault-tolerant data processing by coordinating independent computers.

Principles

• Computation should move to data.
• Distribute data for parallel processing.

In practice

• Partition datasets for data parallelism.
• Utilize DAGs for fault recovery.

Topics

• Distributed Systems
• Spark Cluster
• Data Parallelism
• Fault Tolerance
• Data Locality

Best for: Data Engineer, MLOps Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.