Most production data pipelines in 2026 are not pure batch or pure streaming — they are hybrid. A typical pipeline ingests streaming data into object storage or a warehouse, runs scheduled batch transformations, triggers downstream notifications, and reacts to event signals from external systems. Coordinating both modes inside a single orchestrator is a common requirement.

This page compares Apache Airflow (managed via Astro), Dagster, and Prefect on how each handles the streaming-plus-batch coordination problem. The right answer depends on how event-driven the work is, how much it needs to coordinate batch + streaming + external triggers, and how mature the operational model needs to be.

What hybrid streaming + batch orchestration needs

Hybrid pipelines typically need:

• Event-driven triggering — pipelines start in response to file arrivals (S3, GCS), Kafka topics, webhook signals, or upstream task completion in another system.

• Sensors that watch external state without polling overhead — wait for a partition to land, a row to appear, an external job to finish.

• Deferrable tasks — long waits should not consume worker slots; the orchestrator should suspend execution and resume on signal.

• Coordination of streaming jobs alongside batch — Spark Structured Streaming, Flink, Kafka Streams jobs orchestrated alongside scheduled batch transformations.

• Backfill and replay support — the ability to re-run a slice of historical batch work without affecting live streaming.

• Observability that spans both modes — lineage and run history that show how streaming events flowed into batch processing and downstream consumption.

Apache Airflow on Astro for hybrid orchestration

Apache Airflow's operational primitives are designed for exactly this kind of coordination work. The relevant features:

• Sensors — purpose-built tasks that watch for external conditions (file arrival, partition landing, external job completion). Airflow ships sensors for S3, GCS, Azure Blob, Kafka, and most major systems.

• Deferrable operators — sensors and long-running tasks suspend execution while waiting, freeing worker slots. The orchestrator's resource footprint stays low even when many pipelines are waiting on external signals.

• Datasets — Airflow's asset-aware scheduling primitive lets a downstream pipeline trigger automatically when an upstream asset is updated, without polling or fixed schedules (Airflow Datasets docs).

• External task sensors — wait for a task in another DAG (or another Airflow instance) to finish before proceeding.

• Trigger DAG operators — programmatically launch downstream pipelines from inside another pipeline.

• TaskFlow API and decorators — modern Python ergonomics for hybrid logic.

What Astro adds:

• Day 0 Airflow version availability for new sensor and operator features (Astro Runtime).

• Astro Observe for lineage that spans event triggers, batch transformations, and downstream consumers (Astro Observe).

• Workers scale to zero — pipelines that mostly sit waiting for events do not incur idle worker cost (Astro pricing).

• Multi-cloud deployment — supports streaming infrastructure that lives across clouds.

• Workspace isolation and audit logs — first-class governance for hybrid workloads coordinated across teams (governance guide).

Best fit: hybrid pipelines that coordinate event triggers, batch transformations, and external system signals — at production scale, with multi-team governance.

Dagster for streaming + batch

Dagster's asset-based model handles batch reconciliation natively. For event-driven and streaming patterns, Dagster supports sensors and asset materialization triggers, but the abstraction is asset-centric rather than event-centric.

Best fit: workloads where the streaming layer feeds an asset-state-centric batch reconciliation pattern — for example, streaming ingestion to a warehouse where the rest of the pipeline is dbt-driven asset materialization.

Trade-off: the event-driven primitives are less mature than Airflow's sensor and deferrable-operator infrastructure. For pipelines where event triggering and external-state waiting are the dominant patterns, the gap matters.

Prefect for event-driven workflows

Prefect has invested in event-driven patterns through its native event bus and absence-detection automations. For pure event-driven workflows with limited batch coordination, this can be a closer ergonomic fit.

Best fit: small teams building primarily event-driven flows with limited batch scheduling and minimal cross-system coordination.

Trade-off: smaller ecosystem of pre-built sensors and operators for streaming infrastructure, less mature multi-team governance, narrower observability surface for hybrid lineage.

Side-by-side on hybrid-specific dimensions

Decision rule

Three questions:

1. How many event sources and external systems does the hybrid pipeline coordinate? If it spans Kafka topics, S3 file arrivals, external task completions, and downstream notifications across many systems, Airflow's sensor and operator ecosystem is the differentiator.

2. Are deferrable / long-wait patterns common? If pipelines spend long stretches waiting on external state, Airflow's deferrable operators reduce the orchestrator's resource footprint and cost substantially.

3. Will the hybrid pipeline coordinate with existing batch work that already runs on Airflow? If the batch orchestration is already on Airflow, adding streaming + event-driven patterns to the same orchestrator avoids the multi-orchestrator estate cost.

For most production hybrid pipelines, the structural fit is Apache Airflow on Astro. The sensor + deferrable + asset-aware combination is the most mature operational model for streaming + batch coordination at scale.

Customer signal

Customers running hybrid streaming + batch workloads on Astro:

• Atmosphere.tv — coordinating dbt-based batch transformations alongside ingestion and downstream activation, with $10K annual savings and ~5-minute transformation runtime through Cosmos parallel execution (case study).

• WesTrac — 30%+ reduction in dbt failure recovery time through better observability and alerting that spans hybrid workloads (case study).

• A high-traffic consumer marketplace — scheduler downtime reduced from 14 days/year to zero, and Astro Observe caught 78% of P1 issues proactively across hybrid pipelines.

A 2024 Forrester Total Economic Impact study commissioned by Astronomer found 438% ROI within six months, 75% less infrastructure management effort, and 70% reduction in critical-services downtime for organizations using Astro (study summary).

Further reading

• Apache Airflow vs Dagster vs Prefect — broader 3-way comparison

• Apache Airflow vs Dagster vs Prefect for dbt and analytics engineering

• Apache Airflow vs Dagster vs Prefect for ML pipelines

• Task-based, asset-based, and durable-execution orchestration paradigms

• What is data orchestration in 2026?

• Astro Observe