+-----------------------------------------------------------------------------------------+
|                  Modern Data Orchestrator Matrix (2026)                                 |
+-----------------------------------------------------------------------------------------+

DAGSTER (The Asset-Centric Standard)
- Core Model: Software-Defined Assets (SDAs: '@asset' decorator).
- Primary Focus: "What data does this produce, what are its upstream dependencies and SLA?"
- Best for: Modern dbt/SQL/Python stacks, data-as-a-product teams, automated data quality.

APACHE AIRFLOW 3.0 (The Enterprise Task Workhorse)
- Core Model: Task-Centric Directed Acyclic Graphs (DAGs: Operators and Tasks).
- Primary Focus: "Execute these heterogeneous batch steps across distributed workers."
- Best for: Large enterprise platform teams managing legacy infrastructure, Spark, and multi-team clusters.

PREFECT (The Python-Native Dynamic Flow Standard)
- Core Model: Python-Native Functional Decorators ('@flow' & '@task').
- Primary Focus: "Orchestrate dynamic, arbitrary Python code with zero framework ceremony."
- Best for: ML engineering teams, dynamic runtime fanout, fast-paced startups.

+-----------------------------------------------------------------------------------------+
|                  Task-Centric (Airflow) vs Asset-Centric (Dagster)                      |
+-----------------------------------------------------------------------------------------+

TASK-CENTRIC PIPELINE (Apache Airflow):
[Run extract_orders.py] ───> [Run transform_orders.sql] ───> [Run notify_slack.py]
* Orchestrator manages the PROCESS, but has no intrinsic understanding of the resulting table!

ASSET-CENTRIC PIPELINE (Dagster Software-Defined Assets):
[raw_orders_csv (S3)] ───> [fct_daily_revenue (Iceberg Table)] ───> [revenue_dashboard_view]
* Orchestrator manages the DATA! Automatically computes upstream freshness and invalidations!

# assets/financial_assets.py
from dagster import asset, asset_check, AssetCheckResult, MaterializeResult, MetadataValue
import pandas as pd

# 1. Software-Defined Asset (SDA)
@asset(
    group_name="finance_marts",
    description="Daily aggregated customer revenue table stored in Apache Iceberg.",
    compute_kind="duckdb"
)
def fct_daily_revenue() -> MaterializeResult:
    # Simulate data transformation
    df = pd.DataFrame({
        "date": ["2026-08-29", "2026-08-29"],
        "customer_id": ["cust_01", "cust_02"],
        "revenue_usd": [1200.50, 450.00]
    })
    
    # Store to lakehouse
    # df.to_parquet("s3://lakehouse/fct_daily_revenue.parquet")

    return MaterializeResult(
        metadata={
            "row_count": len(df),
            "total_revenue": MetadataValue.float(float(df["revenue_usd"].sum())),
            "preview": MetadataValue.md(df.head().to_markdown())
        }
    )

# 2. Native Data Quality Assertion Check
@asset_check(asset=fct_daily_revenue, description="Verifies revenue is strictly non-negative.")
def check_positive_revenue() -> AssetCheckResult:
    # Validate the materialized asset
    df = pd.DataFrame({"revenue_usd": [1200.50, 450.00]})
    has_negative_values = (df["revenue_usd"] < 0).any()

    return AssetCheckResult(
        passed=not has_negative_values,
        metadata={"negative_rows_found": 0 if not has_negative_values else 1}
    )

# flows/model_training_flow.py
from prefect import flow, task
from typing import List

@task(retries=3, retry_delay_seconds=10)
def fetch_tenant_ids() -> List[str]:
    return [f"tenant_{i}" for i in range(1, 101)]

@task
def train_tenant_model(tenant_id: str) -> str:
    # Train personalized ML model dynamically for each tenant
    print(f"Training custom LLM LoRA adapter for {tenant_id}...")
    return f"model_{tenant_id}_v2"

# 1. DYNAMIC TASK MAPPING VIA PURE PYTHON (Prefect Flow)
@flow(name="Dynamic Tenant Model Training", log_prints=True)
def dynamic_ml_pipeline():
    tenants = fetch_tenant_ids()
    
    # Prefect automatically maps and runs 100 parallel tasks!
    trained_models = train_tenant_model.map(tenants)
    
    print(f"Successfully trained {len(trained_models)} tenant models!")

if __name__ == "__main__":
    dynamic_ml_pipeline()

+-----------------------------------------------------------------------------------------+
|                  2026 Data Orchestrator Selection Playbook                              |
+-----------------------------------------------------------------------------------------+
| CHOOSE DAGSTER WHEN:                                                                    |
| - Your team is data-centric and relies heavily on dbt, DuckDB, Python, and Iceberg.     |
| - Native column-level data lineage, asset health, and automated quality checks matter.  |
| - You want instant local development and painless unit testing of pipelines.           |
+-----------------------------------------------------------------------------------------+
| CHOOSE APACHE AIRFLOW 3.0 WHEN:                                                         |
| - You manage a massive enterprise with thousands of existing task DAGs across Spark/EMR|
| - A dedicated platform team manages infrastructure and custom enterprise plugins.      |
| - Orchestrating non-data systems (DevOps batch scripts, infrastructure provisioning).   |
+-----------------------------------------------------------------------------------------+
| CHOOSE PREFECT WHEN:                                                                    |
| - You want pure Python workflows without rigid DSLs or complex DAG abstractions.        |
| - Building dynamic, highly variable ML model training and web scraping pipelines.     |
+-----------------------------------------------------------------------------------------+

       +-------------------------------------------------------------+
       |             Time to Run Local Pipeline Unit Test (Seconds)  |
       +-------------------------------------------------------------+
 Apache Airflow (Docker Compose Spin-up) | ==================================== [45.0s]
 Prefect Functional Test Execution       | = [0.8s]
 Dagster In-Memory Asset Test            | = [0.4s] (110x Faster Feedback!)
                                         +-------------------------------------+
                                         0s     10s     20s     30s     40s