This content originally appeared on DEV Community and was authored by Milcah03
Introduction
In data engineering, scaling complexity often feels like juggling flaming chainsaws without losing a finger. Thankfully, Apache Airflow and Kafka bring balance to the chaos. One orchestrates workflows; the other powers real-time streaming. Here’s how they shine, and why you should care.
Why It Matters
Consider Airflow’s meteoric rise: as of November 2024, it recorded 31 million monthly downloads (up from just 888 K in 2020). Its contributor base nearly tripled, and it’s now adopted by 77,000+ organisations, compared to 25,000 in 2020. More than 90 % of users say Airflow is business-critical, with over 85 % expecting it to drive external or revenue-generating solutions in the coming year.
On the streaming side, Apache Kafka is used by over 80 % of Fortune 100 companies, serving as the backbone for real-time pipelines in sectors from retail to IoT.
Apache Airflow: Your Orchestration Maestro
Why data engineers rely on Airflow:
Workflows-as-code: Define DAGs (Directed Acyclic Graphs) in Python, making pipelines reproducible, modular, and versionable.
Rich features and growth: Since Airflow 3.0 launched in April 2025, it has added DAG versioning, a React-based UI, event-driven scheduling, and an SDK-driven task execution interface.
Real-world usage: In a 2024 community survey, Airflow was used daily by 79 % of respondents, with 85 % expressing satisfaction and loyalty.
Apache Kafka: The Real-Time Data Highway
Kafka’s strengths make it indispensable for modern systems:
Unmatched scalability & reliability: Built to deliver high-throughput, persistent, and low-latency streaming.
Widespread adoption: From Goldman Sachs detecting fraud in real time, to Walmart managing inventory, Kafka is now mission-critical
Battle-tested at scale: For example, Cloudflare’s Kafka architecture spans 14 clusters across data centres and has processed over one trillion messages during its production run.
Why You Need Both
Think of Airflow and Kafka as complementary leadership in your data stack:
- Airflow is best for Workflow orchestration, scheduling, monitoring, Batch ETL, ML/AI pipelines, and DAG-driven jobs.
- Kafka is best for Real-time streaming, high-scale messaging, Event ingestion, decoupled microservices, and real-time analytics
Hybrid example:
- Kafka ingests streaming events (clickstream, sensor data, etc.).
- Consumers write raw events to a data lake.
- Airflow triggers daily DAGs to process and aggregate this data for dashboards.
- This architecture balances real-time freshness with reliable, maintainable workflows.
Conclusion
Airflow and Kafka are cornerstones of modern data platforms. Airflow brings structure and observability; Kafka brings speed and resilience. Together, they empower hybrid architectures that flow from batch to real-time seamlessly.
This content originally appeared on DEV Community and was authored by Milcah03