Description

What this gets into:
Data pipeline architecture decisions made without a clear understanding of the processing trade-offs they embed create systems that work correctly at initial scale and become operationally problematic as volume, latency requirements, and downstream system complexity increase. This module develops the architectural reasoning behind pipeline design choices rather than the implementation of specific processing frameworks.

Technical territory covered:
– Batch versus stream trade-offs in practice: the specific latency, throughput, complexity, and operational characteristics of batch and stream processing, how to identify which processing model a given workload actually requires, and how to evaluate the operational cost of choosing the more complex model when the simpler one would have sufficed
– Lambda and Kappa architecture patterns: how each pattern addresses the batch/stream trade-off, what operational complexity each introduces, when the additional complexity of Lambda architecture is justified by the requirements it addresses, and how to evaluate whether a simpler architecture can meet the same requirements with lower operational overhead
– Pipeline resilience and backpressure: how to design pipelines that handle upstream volume spikes without cascading failure downstream, how backpressure mechanisms work in stream processing systems, and how to build the monitoring and alerting that surfaces pipeline health problems before they become data loss or latency incidents

Estimated hours: +/- 5

Engineering outcome:
An architectural reasoning capability for data pipeline design that produces systems matched to their actual processing requirements — avoiding the operational complexity of over-engineered pipelines while building in the resilience that production data processing demands.