Pipeline Architecture Trade Offs and Alternatives

When to Choose Pipeline Architecture: Pipeline architecture is not always the right choice. The decision depends on team structure, data volume, latency requirements, and how frequently logic changes. Choose pipelines when you have multiple teams contributing logic. Each team can own specific stages with clear contracts, enabling independent development and deployment. Choose pipelines when different transformations have vastly different resource needs. Your join stage might need 10 times more memory than your validation stage. With pipelines, you scale them independently. Choose pipelines when you need reusability. The same cleaning or enrichment stages are used by multiple downstream consumers, reducing duplicated code. Avoid pipelines for small scale or single team projects where simplicity matters more than modularity. A monolithic batch job that runs in 1 minute is easier to understand, deploy, and debug than 5 pipeline stages that take 2 to 3 minutes end to end due to serialization and queue overhead. Alternative: Monolithic Batch Job For batch workloads under a few terabytes with a single team, a monolithic job is often better. You write one program that reads input, performs all transformations, and writes output. Deployment is simpler: one artifact, one scheduler entry. Debugging is easier: stack traces show the full context. Performance can be better: no network hops, no serialization between stages, and the query optimizer can optimize across the entire workflow. The downside is scaling. If your aggregation logic is the bottleneck, you must scale the entire job, overprovisioning for the other steps. As data grows from 2 TB to 20 TB, the monolith becomes unwieldy. Alternative: Interactive Query Engine If your primary use case is ad hoc analytics, not continuous processing, an interactive query engine (like Presto, BigQuery, or Athena) is better than pipelines. Analysts write SQL queries against your data lake and get results in seconds to minutes. There is no pipeline to build or maintain. But query engines are not suitable for complex multi step transformations with intermediate state, or for low latency requirements. Query engines typically target latencies of 5 to 30 seconds, not sub second. Alternative: Serverless Workflow Orchestrators Serverless platforms (like AWS Step Functions or Google Cloud Workflows) can model pipelines as state machines. Each stage is a serverless function invocation. This simplifies operations: no servers to manage. You pay per execution, not for idle capacity. The trade offs: cold starts add 100 to 500 milliseconds latency, making this unsuitable for low latency real time pipelines. Execution time limits (often 15 minutes per function) constrain long running transformations. Cost per invocation becomes expensive at high throughput: 10 million function invocations per day at $0.20 per million is $2 per day, but 1 billion invocations is $200 per day, where dedicated pipeline infrastructure might cost less. Decision Criteria: Use this framework. If your data is under 5 TB daily and one team owns all logic, start with a monolithic job. If you have 2 or more teams contributing transformations or stages have different scaling needs (one needs 10x more CPU than another), use pipelines. If primary use is ad hoc queries, use an interactive engine. If you want zero operational overhead and can tolerate cold start latency, try serverless workflows. Also consider change frequency. If logic changes weekly, the overhead of managing pipeline stages is worth it for testability and isolated deployments. If logic is stable for months, a simpler monolith reduces complexity.