Real-Time Data Orchestration with Apache Airflow and Streaming Tools

Overview

Apache Airflow has long been a tool for managing batch data pipelines. However, with the rise of real-time analytics, companies need to process data instantly. Traditional batch methods fall short of fraud detection, real-time monitoring, personalized recommendations, and log analytics. Although Airflow is not a streaming engine, it can coordinate real-time workflows by integrating with tools like Apache Kafka, Apache Flink, AWS Kinesis, and Google Pub/Sub. This article explains how Airflow works with these services to process, transform, and deliver data for analysis, discussing architecture, strategies, challenges, and best practices.

Real-Time Data Processing in Airflow

Real-time data processing works differently from batch processing. It handles data continuously instead of processing it in scheduled batches. This is crucial for finance, e-commerce, healthcare, and cybersecurity industries, where quick insights drive decisions. Apache Airflow isn’t a real-time processor; it’s an orchestrator that schedules tasks using DAGs (Directed Acyclic Graphs). However, by integrating with streaming tools, Airflow can help manage and monitor real-time workflows, ensuring data is ingested, processed, and stored efficiently as it arrives.

In a real-time architecture, Airflow is typically used to:

• Trigger streaming jobs that process incoming data from Kafka, Kinesis, or Pub/Sub.
• Monitor and manage long-running streaming applications, ensuring they are healthy and restart if they fail.
• Orchestrate ETL pipelines where real-time data needs to be enriched, aggregated, or stored in a data warehouse.
• Trigger alerts based on anomalies detected in real-time streams.

Unlike batch workflows, where Airflow triggers a DAG at regular intervals in real-time processing, Airflow listens for events, reacts dynamically, and integrates with external services that handle continuous data ingestion.

Integrating Apache Airflow with Streaming Services

To enable real-time workflows, Apache Airflow must interact with message queues, stream processing frameworks, and cloud-based streaming services. The most used technologies for streaming data include:

• Apache Kafka – A distributed event streaming platform that enables high-throughput message passing.
• Apache Flink – A stream-processing framework that provides stateful computation over real-time data.
• Apache Spark Structured Streaming – A real-time data processing framework built on Spark.
• AWS Kinesis – A managed service for collecting and processing real-time data streams.
• Google Pub/Sub – A messaging service for event-driven architectures.

Each technology handles continuous data processing, while Airflow orchestrates and monitors its workflows. The integration typically follows a three-step architecture:

• Ingest Data: Streaming sources like Kafka or Kinesis continuously push data events into a message queue.
• Process Data: A stream processing engine (Flink, Spark Streaming, or Kinesis Analytics) consumes and transforms the data.
• Store and Analyze: Processed data is stored in real-time databases like Apache Druid, ClickHouse, or Snowflake, which can be queried for insights.

Using Apache Airflow to Trigger and Manage Streaming Jobs

Airflow can trigger streaming jobs through its operators. For example, if a company wants to process logs from Kafka using Apache Flink, Airflow can submit a Flink job using the FlinkOperator.

In this scenario, Airflow does not process the data directly but ensures that the Flink job is deployed and monitored. The parameter ‘wait_for_completion=False’ allows Airflow to trigger the Flink job and immediately continue with other tasks, ensuring real-time execution without blocking the DAG.

Similarly, for Kafka-based ingestion pipelines, Airflow can use the KafkaSensor to detect when new messages are available in a topic and trigger processing tasks dynamically.

When new data arrives in the real-time topic, the Airflow DAG is triggered, ensuring that the pipeline processes the data as it comes in rather than waiting for scheduled runs.

Handling Long-Running Streaming Applications with Airflow

The lengthy duration of streaming operations, instead of batch jobs with a set start and end time, presents a barrier to real-time data processing. Streaming jobs need to run continuously, while traditional Airflow tasks cease execution when their command is finished.

Airflow can manage long-running streaming jobs by:

• Using Airflow’s TriggerDagRunOperator to restart jobs if they fail.
• Monitoring job health with sensors periodically, checking whether the stream processing service is still active.
• Using ExternalTaskSensor to ensure downstream processes wait until streaming jobs are stable.

For instance, in a Kafka-to-Snowflake pipeline, a Kafka consumer continuously ingests real-time data, and an Airflow DAG monitors the pipeline’s health. If the streaming job crashes, Airflow automatically restarts it using a retry mechanism.

With this approach, Airflow does not run the streaming job itself but ensures that it remains active and recovers quickly in case of failures.

Optimizing Performance for Real-Time Data Pipelines

Performance tuning is essential for avoiding bottlenecks when connecting Airflow with real-time streaming providers. Among the most important optimization techniques are:

• Minimizing Airflow DAG Parsing Time –Avoid superfluous dependencies and optimize DAG imports to make DAGs react rapidly to streaming events.
• Scaling Airflow Workers – Streaming workflows require high availability, so CeleryExecutor or KubernetesExecutor ensures that Airflow worker nodes are scaled dynamically.
• Leveraging Airflow’s Smart Sensors – Instead of triggering new DAGs every second, use event-based triggering to ensure Airflow reacts only when necessary.

Conclusion

Although Airflow was built for batch workflows, it can effectively manage real-time data pipelines by integrating with streaming tools like Kafka, Flink, and Kinesis.

The right integrations help companies build scalable, low-latency data pipelines that adapt to evolving business needs.

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