When should I use fan-out in a data pipeline?

Use fan-out when a workload can be split into independent units, such as files, tenants, tables, or date ranges. It improves throughput and narrows the blast radius of failures. Avoid it when tasks mutate shared state, depend on each other’s order, or overload the same downstream resource.

What is the difference between partitioning and dynamic mapping?

Partitioning defines the data boundary for a unit of work, such as a date, tenant, or source. Dynamic mapping is how the orchestrator creates tasks from inputs discovered at runtime. They often work together: discover the missing partitions, then run one task per partition.

Why write pipeline output to staging before publishing?

Staging keeps incomplete or unvalidated data away from production consumers. A pipeline can write intermediate results, run schema checks, count checks, deduplication, and business validation, then publish only after the data is acceptable. This makes final tables more stable and reduces visible partial updates.

Orchestration

07.05 · Concept

Pipeline Design Patterns

Apply fan-out, fan-in, dynamic mapping, partitioned execution, staging, and checkpointing patterns to a data pipeline.

Pipeline design patterns turn large data jobs into smaller, recoverable units of work. Fan-out, fan-in, dynamic mapping, partitioned execution, staging, and checkpointing help pipelines scale safely, retry only what failed, protect downstream consumers from bad intermediate output, and make operational failures easier to diagnose.

What this lesson answers

how to design fan-out fan-in data pipelines
when should data pipelines use partitioned execution
how checkpointing prevents duplicate pipeline outputs

Notes

Pipeline design patterns are reusable ways to structure work so a data pipeline is easier to scale, observe, and recover. Fan-out splits one logical workload into many independent tasks, such as processing each file, customer, table, or date range separately. Fan-in brings those parallel results back together for a downstream step, such as validation, aggregation, publishing, or notification.

Common questions

When should I use fan-out in a data pipeline?: Use fan-out when a workload can be split into independent units, such as files, tenants, tables, or date ranges. It improves throughput and narrows the blast radius of failures. Avoid it when tasks mutate shared state, depend on each other’s order, or overload the same downstream resource.
What is the difference between partitioning and dynamic mapping?: Partitioning defines the data boundary for a unit of work, such as a date, tenant, or source. Dynamic mapping is how the orchestrator creates tasks from inputs discovered at runtime. They often work together: discover the missing partitions, then run one task per partition.
Why write pipeline output to staging before publishing?: Staging keeps incomplete or unvalidated data away from production consumers. A pipeline can write intermediate results, run schema checks, count checks, deduplication, and business validation, then publish only after the data is acceptable. This makes final tables more stable and reduces visible partial updates.

Short definition: what is Pipeline Design Patterns?

07.04 · OrchestrationPrefect Flows and Deployments 07.06 · OrchestrationRetries, Idempotency, and Backfills