➡️Flow

NEW

A directed sequence of steps representing movement of data or control through a pipeline or workflow.

DESCRIPTION

Flows model how work progresses through discrete steps, often encoded as tasks, transformations, or service calls. In orchestration tools, a flow defines dependencies, triggers, and error handling for reliable execution. Flows can be DAGs or stateful processes, supporting retries, scheduling, and parallelism. They provide clarity, reusability, and observability for complex pipelines across data and application domains. Good flow design reduces coupling, isolates failures, and improves performance and maintainability.

ALTERNATIVE DEFINITIONS

Prefect

A Flow is the container for tasks and the edges between them.

Defines flow as the structural unit of orchestration linking tasks and dependencies.

Source

Apache NiFi

A dataflow describes how data moves through a system of processors, connections, and queues.

Emphasizes movement of data through processors and queues in a flow.

Source

Microsoft Learn

Logic Apps orchestrate workflows that integrate apps, data, services, and systems with automated, scalable flows.

Connects flows to integration workflows at cloud scale.

Source

RELATED TERMS

Workflow Orchestration Apache Airflow Prefect Azure Logic Apps Dataflow Control Flow Event-Driven Architecture

COMPETING TERMS

Pipelines Directed Acyclic Graph

RELATIONS

• Narrower: Control Flow Normalizing Flow Stock and Flow

KNOWLEDGE AREAS

Data Engineering Platform & Tooling Collaboration Software Modeling

TARGET AUDIENCE

Data Engineer MLOps Engineer DevOps Engineer Software Developer Solution Architect IT Project Manager

EXTERNAL SOURCES

• Wikipedia - Workflow
• Prefect - Concept docs defining flows
• Apache Airflow - DAGs as flow orchestration
• Apache NiFi - NiFi dataflow documentation

TOP QUESTIONS

What is a Flow in data engineering?

A Flow models the steps and dependencies that move data through processing. It defines execution order, error handling, and scheduling.

Category: Basics

How do Flows differ from DAGs?

DAGs are acyclic graphs; flows can be modeled as DAGs but may include stateful or looped processes depending on the platform.

Category: Comparison

What are common mistakes in Flow design?

Overly coupled tasks, missing retries, and poor failure isolation. Insufficient observability leads to troubleshooting challenges.

Category: Mistakes

How do you observe and monitor a Flow?

Use logging, metrics, traces, and lineage. Platforms provide run histories, alerts, and dashboards.

Category: Observability

What risks exist when building Flows?

Deadlocks, missed triggers, data loss on failures, and inefficient resource usage. Proper design and testing mitigate risks.

Category: Risks

What is a hot topic around Flows?

Declarative orchestration, event-driven pipelines, and unified observability across data and ML flows.

Category: Topic

How to design resilient Flows?

Add retries, idempotency, circuit breakers, and compensating steps. Isolate failures and checkpoint progress.

Category: BestPractice

Where to start learning Flow orchestration?

Explore Apache Airflow, Prefect, or NiFi docs and tutorials. Build small pipelines and iterate.

Category: Learning

How do Flows support ML pipelines?

They coordinate data prep, training, evaluation, and deployment, integrating feature stores and registries for reproducibility.

Category: MLOps