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+---
+title: "Prefect: Modern Workflow Orchestration Platform"
+library_name: prefect
+pypi_package: prefect
+category: workflow-orchestration
+python_compatibility: "3.9+"
+last_updated: "2025-11-02"
+official_docs: "https://docs.prefect.io"
+official_repository: "https://github.com/PrefectHQ/prefect"
+maintenance_status: "active"
+---
+
+# Prefect: Modern Workflow Orchestration
+
+## Core Purpose
+
+Prefect solves workflow orchestration with a Python-first approach that turns regular Python functions into production-ready data pipelines. Unlike legacy orchestrators that require DAG definitions and framework-specific operators, Prefect observes native Python code execution and provides orchestration through simple decorators@[1].
+
+**Problem Domain:** Coordinating multi-step data workflows, handling failures with retries, scheduling recurring jobs, monitoring pipeline execution, and managing dependencies between tasks without writing boilerplate orchestration code@[2].
+
+**When to Use:** Building data pipelines, ML workflows, ETL processes, or any multi-step automation that needs scheduling, retry logic, state tracking, and observability@[3].
+
+**What You Would Reinvent:** Manual retry logic, state management, dependency coordination, scheduling systems, execution monitoring, error handling, result caching, and workflow visibility dashboards@[4].
+
+## Official Information
+
+**Repository:** <https://github.com/PrefectHQ/prefect> **PyPI Package:** `prefect` (current: v3.4.24)@[5] **Documentation:** <https://docs.prefect.io> **License:** Apache-2.0@[6] **Maintenance:** Actively maintained by PrefectHQ with 1059 open issues, 20.6K stars, regular releases@[7] **Community:** 30K+ engineers, active Slack community@[8]
+
+## Python Compatibility
+
+**Minimum Version:** Python 3.9@[9] **Maximum Version:** Python 3.13 (3.14 not yet supported)@[9] **Async Support:** Full native async/await support throughout@[10] **Type Hints:** First-class support, type-safe structured outputs@[11]
+
+## Core Capabilities
+
+### 1. Pythonic Flow Definition
+
+Write workflows as regular Python functions with `@flow` and `@task` decorators:
+
+```python
+from prefect import flow, task
+import httpx
+
+@task(log_prints=True)
+def get_stars(repo: str):
+    url = f"https://api.github.com/repos/{repo}"
+    count = httpx.get(url).json()["stargazers_count"]
+    print(f"{repo} has {count} stars!")
+
+@flow(name="GitHub Stars")
+def github_stars(repos: list[str]):
+    for repo in repos:
+        get_stars(repo)
+
+# Run directly
+if __name__ == "__main__":
+    github_stars(["PrefectHQ/Prefect"])
+```
+
+@[12]
+
+### 2. Dynamic Runtime Workflows
+
+Create tasks dynamically based on data, not static DAG definitions:
+
+```python
+from prefect import task, flow
+
+@task
+def process_customer(customer_id: str) -> str:
+    return f"Processed {customer_id}"
+
+@flow
+def main() -> list[str]:
+    customer_ids = get_customer_ids()  # Runtime data
+    # Map tasks across dynamic data
+    results = process_customer.map(customer_ids)
+    return results
+```
+
+@[13]
+
+### 3. Flexible Scheduling
+
+Deploy workflows with cron, interval, or RRule schedules:
+
+```python
+# Serve with cron schedule
+if __name__ == "__main__":
+    github_stars.serve(
+        name="daily-stars",
+        cron="0 8 * * *",  # Daily at 8 AM
+        parameters={"repos": ["PrefectHQ/prefect"]}
+    )
+```
+
+@[14]
+
+```python
+# Or use interval-based scheduling
+my_flow.deploy(
+    name="my-deployment",
+    work_pool_name="my-work-pool",
+    interval=timedelta(minutes=10)
+)
+```
+
+@[15]
+
+### 4. Built-in Retries and State Management
+
+Automatic retry logic and state tracking:
+
+```python
+@task(retries=3, retry_delay_seconds=60)
+def fetch_data():
+    # Automatically retries on failure
+    return api_call()
+```
+
+@[16]
+
+### 5. Concurrent Task Execution
+
+Run tasks in parallel with `.submit()`:
+
+```python
+@flow
+def my_workflow():
+    future = cool_task.submit()  # Non-blocking
+    print(what_did_cool_task_say(future))
+```
+
+@[17]
+
+### 6. Event-Driven Automations
+
+React to events, not just schedules:
+
+```python
+# Trigger flows on external events
+my_flow.deploy(
+    triggers=[
+        DeploymentEventTrigger(
+            expect=["s3.file.uploaded"]
+        )
+    ]
+)
+```
+
+@[18]
+
+## Real-World Integration Patterns
+
+### Integration with dbt
+
+Orchestrate dbt transformations within Prefect flows:
+
+```python
+from prefect_dbt import DbtCoreOperation
+
+@flow
+def dbt_flow():
+    result = DbtCoreOperation(
+        commands=["dbt run", "dbt test"],
+        project_dir="/path/to/dbt/project"
+    ).run()
+    return result
+```
+
+@[19]
+
+**Example Repository:** <https://github.com/anna-geller/prefect-dataplatform> (106 stars) - Shows Prefect + dbt + Snowflake data platform@[20]
+
+### AWS Deployment Pattern
+
+Deploy to AWS ECS Fargate:
+
+```python
+# prefect.yaml configuration
+work_pool:
+  name: aws-ecs-pool
+  type: ecs
+
+deployments:
+  - name: production
+    work_pool_name: aws-ecs-pool
+    schedules:
+      - cron: "0 */4 * * *"
+```
+
+@[21]
+
+**Example Repository:** <https://github.com/anna-geller/dataflow-ops> (116 stars) - Automated deployments to AWS ECS@[22]
+
+### Docker Compose Self-Hosted
+
+Run Prefect server with Docker Compose:
+
+```yaml
+version: "3.8"
+services:
+  prefect-server:
+    image: prefecthq/prefect:latest
+    command: prefect server start
+    ports:
+      - "4200:4200"
+    environment:
+      - PREFECT_API_DATABASE_CONNECTION_URL=postgresql+asyncpg://postgres:password@postgres:5432/prefect
+```
+
+@[23]
+
+**Example Repositories:**
+
+- <https://github.com/rpeden/prefect-docker-compose> (142 stars)@[24]
+- <https://github.com/flavienbwk/prefect-docker-compose> (161 stars)@[25]
+
+## Common Usage Patterns
+
+### Pattern 1: ETL Pipeline with Retries
+
+```python
+from prefect import flow, task
+from prefect.tasks import exponential_backoff
+
+@task(retries=3, retry_delay_seconds=exponential_backoff(backoff_factor=2))
+def extract_data(source: str):
+    # Fetch from API with automatic retries
+    return fetch_api_data(source)
+
+@task
+def transform_data(raw_data):
+    return clean_and_transform(raw_data)
+
+@task
+def load_data(data, destination: str):
+    write_to_database(data, destination)
+
+@flow(log_prints=True)
+def etl_pipeline():
+    raw = extract_data("https://api.example.com/data")
+    transformed = transform_data(raw)
+    load_data(transformed, "postgresql://db")
+```
+
+@[26]
+
+### Pattern 2: Scheduled Data Sync
+
+```python
+@flow
+def sync_customer_data():
+    customers = fetch_customers()
+    for customer in customers:
+        sync_to_warehouse(customer)
+
+# Schedule to run every hour
+if __name__ == "__main__":
+    sync_customer_data.serve(
+        name="hourly-sync",
+        interval=3600,  # Every hour
+        tags=["production", "sync"]
+    )
+```
+
+@[27]
+
+### Pattern 3: ML Pipeline with Caching
+
+```python
+@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(hours=1))
+def load_training_data():
+    # Expensive data loading - cached for 1 hour
+    return load_large_dataset()
+
+@task
+def train_model(data):
+    return train_ml_model(data)
+
+@flow
+def ml_pipeline():
+    data = load_training_data()  # Reuses cached result
+    model = train_model(data)
+    return model
+```
+
+@[28]
+
+## Integration Ecosystem
+
+### Data Transformation
+
+- **dbt:** Native integration via `prefect-dbt` package (archived, use dbt Cloud API)@[29]
+- **dbt Cloud:** Official integration for triggering dbt Cloud jobs@[30]
+
+### Data Warehouses
+
+- **Snowflake:** `prefect-snowflake` for query execution@[31]
+- **BigQuery:** `prefect-gcp` for BigQuery operations@[32]
+- **Redshift, PostgreSQL:** Standard database connectors@[33]
+
+### Cloud Platforms
+
+- **AWS:** `prefect-aws` (S3, ECS, Lambda, Batch)@[34]
+- **GCP:** `prefect-gcp` (GCS, BigQuery, Cloud Run)@[35]
+- **Azure:** `prefect-azure` (Blob Storage, Container Instances)@[36]
+
+### Container Orchestration
+
+- **Docker:** Native Docker build and push support@[37]
+- **Kubernetes:** `prefect-kubernetes` for K8s deployments@[38]
+- **ECS Fargate:** Built-in ECS work pools@[39]
+
+### Data Quality
+
+- **Great Expectations:** `prefect-great-expectations` for validation@[40]
+- **Monte Carlo:** Circuit breaker integrations@[41]
+
+### ML/AI
+
+- **LangChain:** `langchain-prefect` for LLM workflows (archived)@[42]
+- **MLflow:** Track experiments within Prefect flows@[43]
+
+## Deployment Options
+
+### 1. Prefect Cloud (Managed)
+
+Fully managed orchestration platform with:
+
+- Hosted API and UI
+- Team collaboration features
+- RBAC and access controls
+- Enterprise SLAs
+- Automations and event triggers@[44]
+
+**Pricing:** Free tier + usage-based pricing@[45]
+
+### 2. Self-Hosted Prefect Server
+
+Open-source server you deploy:
+
+```bash
+# Start local server
+prefect server start
+
+# Or deploy via Docker
+docker run -p 4200:4200 prefecthq/prefect:latest prefect server start
+```
+
+@[46]
+
+**Requirements:** PostgreSQL database, Redis (optional for caching)@[47]
+
+### 3. Hybrid Execution Model
+
+Orchestration in cloud, execution anywhere:
+
+- Control plane in Prefect Cloud
+- Workers run in your infrastructure
+- Code never leaves your environment@[48]
+
+## When to Use Prefect
+
+### Use Prefect When
+
+1. **Building data pipelines** that need scheduling, retries, and monitoring@[49]
+2. **Orchestrating ML workflows** with dynamic dependencies@[50]
+3. **Coordinating microservices** or distributed tasks@[51]
+4. **Migrating from cron jobs** to a modern orchestrator@[52]
+5. **Need Python-native workflows** without DSL overhead@[53]
+6. **Want local development** with production parity@[54]
+7. **Require event-driven automation** beyond scheduling@[55]
+8. **Need visibility** into workflow execution and failures@[56]
+
+### Use Simple Scripts/Cron When
+
+1. **Single-step tasks** with no dependencies@[57]
+2. **One-off scripts** that rarely run@[58]
+3. **No retry logic** needed@[59]
+4. **No failure visibility** required@[60]
+5. **Under 5 lines of code** total@[61]
+
+## Prefect vs. Alternatives
+
+### Prefect vs. Airflow
+
+| Dimension | Prefect | Airflow |
+| --- | --- | --- |
+| **Development Model** | Pure Python functions with decorators | DAG definitions with operators |
+| **Dynamic Workflows** | Runtime task creation based on data | Static DAG structure at parse time |
+| **Local Development** | Run locally without infrastructure | Requires full Airflow setup |
+| **Learning Curve** | Minimal - just Python | Steep - framework concepts required |
+| **Infrastructure** | Runs anywhere Python runs | Multi-component (scheduler, webserver, DB) |
+| **Cost** | 60-70% lower (per customer reports)@[62] | Higher due to always-on infrastructure@[63] |
+| **Best For** | ML/AI, modern data teams, dynamic pipelines | Traditional ETL, platform teams invested in ecosystem |
+
+**Migration Path:** Prefect provides 73.78% cost reduction over Astronomer (managed Airflow)@[64]
+
+### Prefect vs. Dagster
+
+| Dimension        | Prefect                     | Dagster                           |
+| ---------------- | --------------------------- | --------------------------------- |
+| **Philosophy**   | Workflow orchestration      | Data asset orchestration          |
+| **Abstractions** | Flows and tasks             | Software-defined assets           |
+| **Use Case**     | General workflow automation | Data asset lineage and cataloging |
+| **Complexity**   | Lower barrier to entry      | Higher conceptual overhead        |
+
+### Prefect vs. Metaflow
+
+| Dimension      | Prefect                   | Metaflow              |
+| -------------- | ------------------------- | --------------------- |
+| **Origin**     | General orchestration     | Netflix ML workflows  |
+| **Scope**      | Broad workflow automation | ML-specific pipelines |
+| **Deployment** | Any infrastructure        | AWS, K8s focus        |
+| **Community**  | Larger ecosystem          | ML-focused community  |
+
+## Decision Matrix
+
+```text
+Use Prefect when:
+- You write Python workflows
+- You need dynamic task generation
+- You want local development + production parity
+- You need retry/caching/scheduling out of box
+- You're building ML, data, or automation pipelines
+- You want low operational overhead
+- Cost efficiency matters (vs. Airflow)
+
+Use Airflow when:
+- You're heavily invested in Airflow ecosystem
+- Your team already knows Airflow
+- You need specific Airflow operators not in Prefect
+- You have dedicated platform engineering for Airflow
+
+Use Dagster when:
+- Data asset lineage is primary concern
+- You're building a data platform with asset catalog
+- You need software-defined assets
+
+Use simple cron/scripts when:
+- Single independent tasks
+- No retry logic needed
+- No monitoring required
+- Runs once per day or less
+```
+
+@[65]
+
+## Anti-Patterns and Gotchas
+
+### Don't Use Prefect For
+
+1. **Simple one-off scripts** - adds unnecessary overhead@[66]
+2. **Real-time streaming** - designed for batch/scheduled workflows@[67]
+3. **Sub-second latency requirements** - orchestration adds overhead@[68]
+4. **Pure event processing** - use Kafka/RabbitMQ instead@[69]
+
+### Common Pitfalls
+
+1. **Over-decomposition:** Breaking every line into a task creates overhead@[70]
+2. **Ignoring task inputs:** Tasks should be pure functions for caching@[71]
+3. **Not using .submit():** Blocking task calls prevent parallelism@[72]
+4. **Skipping local testing:** Run flows locally before deploying@[73]
+
+## Learning Resources
+
+**Official Quickstart:** <https://docs.prefect.io/v3/get-started/quickstart@[74>] **Examples Repository:** <https://github.com/PrefectHQ/examples@[75>] **Community Recipes:** <https://github.com/PrefectHQ/prefect-recipes> (254 stars, archived)@[76] **Slack Community:** <https://prefect.io/slack@[77>] **YouTube Channel:** <https://www.youtube.com/c/PrefectIO/@[78>]
+
+## Installation
+
+```bash
+# Using pip
+pip install -U prefect
+
+# Using uv (recommended)
+uv add prefect
+
+# With specific integrations
+pip install prefect-aws prefect-gcp prefect-dbt
+```
+
+@[79]
+
+## Verification Checklist
+
+- [x] Official repository confirmed: <https://github.com/PrefectHQ/prefect>
+- [x] PyPI package verified: prefect v3.4.24
+- [x] Python compatibility: 3.9-3.13
+- [x] License confirmed: Apache-2.0
+- [x] Real-world examples: 5+ GitHub repositories with 100+ stars
+- [x] Integration patterns documented: dbt, Snowflake, AWS, Docker
+- [x] Decision matrix provided: vs Airflow, Dagster, Metaflow, cron
+- [x] Anti-patterns identified: streaming, sub-second latency
+- [x] Code examples: 6+ verified from official docs and Context7
+- [x] Maintenance status: Active (1059 open issues, recent commits)
+
+## References
+
+Sources cited with @ notation throughout document:
+
+[1-79] Information gathered from:
+
+- Context7 Library ID: /prefecthq/prefect (Trust Score: 8.2, 6247 code snippets)
+- Official documentation: <https://docs.prefect.io>
+- GitHub repository: <https://github.com/PrefectHQ/prefect>
+- PyPI package page: <https://pypi.org/project/prefect/>
+- Prefect vs Airflow comparison: <https://www.prefect.io/compare/airflow>
+- Example repositories: anna-geller/prefect-dataplatform, rpeden/prefect-docker-compose, flavienbwk/prefect-docker-compose, anna-geller/dataflow-ops
+- Exa code context search results
+- Ref documentation search results
+
+Last verified: 2025-10-21