Batch data processing is too slow for real-time AI: How open-source Apache Airflow 3.0 solves the challenge with event-driven data orchestration

Moving data from various sources to the appropriate place for AI applications is no small feat. This is where data orchestration tools like Apache Airflow come into play, making the process smoother and more efficient.

The Apache Airflow community has just released its most significant update in years with the launch of version 3.0. This marks the first major update in four years, following steady improvements in the 2.x series, including the 2.9 and 2.10 releases in 2024, which heavily focused on AI enhancements.

Apache Airflow has become the go-to tool for data engineers, cementing its place as the top open-source workflow orchestration platform. With over 3,000 contributors and widespread use among Fortune 500 companies, it's clear why it's so popular. There are also several commercial services built on top of it, such as Astronomer Astro, Google Cloud Composer, Amazon Managed Workflows for Apache Airflow (MWAA), and Microsoft Azure Data Factory Managed Airflow, to name a few.

As companies grapple with coordinating data workflows across different systems, clouds, and increasingly AI workloads, the need for robust solutions grows. Apache Airflow 3.0 steps up to meet these enterprise needs with an architectural overhaul that promises to enhance how organizations develop and deploy data applications.

"To me, Airflow 3 is a new beginning, a foundation for a much broader set of capabilities," Vikram Koka, an Apache Airflow PMC (project management committee) member and Chief Strategy Officer at Astronomer, shared in an exclusive interview with VentureBeat. "This is almost a complete refactor based on what enterprises told us they needed for the next level of mission-critical adoption."

Enterprise Data Complexity Has Changed Data Orchestration Needs

With businesses increasingly relying on data for decision-making, the complexity of data workflows has skyrocketed. Companies now juggle complex pipelines that span multiple cloud environments, diverse data sources, and increasingly sophisticated AI workloads.

Airflow 3.0 is tailored to address these evolving enterprise needs. Unlike its predecessors, this release moves away from a monolithic structure to a distributed client model, offering greater flexibility and security. This new architecture empowers enterprises to:

1. Execute tasks across multiple cloud environments.
2. Implement detailed security controls.
3. Support a variety of programming languages.
4. Enable true multi-cloud deployments.

The expanded language support in Airflow 3.0 is particularly noteworthy. While earlier versions were mainly Python-focused, the new release now natively supports multiple programming languages. Airflow 3.0 currently supports Python and Go, with plans to include Java, TypeScript, and Rust. This flexibility means data engineers can use their preferred programming language, making workflow development and integration smoother.

Event-Driven Capabilities Transform Data Workflows

Traditionally, Airflow has been great at scheduled batch processing, but enterprises are now demanding real-time data processing capabilities. Airflow 3.0 steps up to meet this demand.

"A key change in Airflow 3 is what we call event-driven scheduling," Koka explained.

Instead of running a data processing job on a set schedule, like every hour, Airflow can now trigger the job when a specific event occurs, such as when a data file is uploaded to an Amazon S3 bucket or a message appears in Apache Kafka. This event-driven scheduling bridges the gap between traditional ETL (Extract, Transform, and Load) tools and stream processing frameworks like Apache Flink or Apache Spark Structured Streaming, allowing organizations to manage both scheduled and event-triggered workflows with a single orchestration layer.

Airflow Will Accelerate Enterprise AI Inference Execution and Compound AI

The introduction of event-driven data orchestration will also boost Airflow's ability to support rapid AI inference execution.

Koka provided an example of using real-time inference for professional services like legal time tracking. In this scenario, Airflow helps gather raw data from sources like calendars, emails, and documents. A large language model (LLM) then transforms this unstructured data into structured information. Another pre-trained model can analyze this structured time tracking data, determine if the work is billable, and assign appropriate billing codes and rates.

Koka refers to this as a compound AI system – a workflow that combines different AI models to efficiently and intelligently complete a complex task. Airflow 3.0's event-driven architecture makes this type of real-time, multi-step inference process feasible across various enterprise use cases.

Compound AI, a concept first defined by the Berkeley Artificial Intelligence Research Center in 2024, differs from agentic AI. Koka explained that while agentic AI enables autonomous AI decision-making, compound AI follows predefined workflows that are more predictable and reliable for business applications.

Playing Ball with Airflow, How the Texas Rangers Look to Benefit

The Texas Rangers major league baseball team is among the many users of Airflow. Oliver Dykstra, a full-stack data engineer at the Texas Rangers Baseball Club, shared with VentureBeat that the team uses Airflow, hosted on Astronomer's Astro platform, as the 'nerve center' of their baseball data operations. All player development, contracts, analytics, and game data are orchestrated through Airflow.

"We're looking forward to upgrading to Airflow 3 and its enhancements to event-driven scheduling, observability, and data lineage," Dykstra said. "As we already rely on Airflow to manage our critical AI/ML pipelines, the added efficiency and reliability of Airflow 3 will help increase trust and resiliency of these data products within our entire organization."

What This Means for Enterprise AI Adoption

For technical decision-makers evaluating their data orchestration strategy, Airflow 3.0 offers tangible benefits that can be implemented gradually.

The first step is to assess current data workflows that could benefit from the new event-driven capabilities. Organizations can pinpoint data pipelines currently using scheduled jobs but would be more efficient with event-based triggers. This shift can significantly reduce processing latency and eliminate unnecessary polling operations.

Next, technology leaders should review their development environments to see if Airflow's expanded language support could help consolidate fragmented orchestration tools. Teams currently managing separate orchestration tools for different language environments can start planning a migration strategy to streamline their technology stack.

For enterprises at the forefront of AI implementation, Airflow 3.0 represents a crucial infrastructure component that addresses a key challenge in AI adoption: orchestrating complex, multi-stage AI workflows at an enterprise scale. The platform's ability to coordinate compound AI systems could help organizations move beyond proof-of-concept to enterprise-wide AI deployment, ensuring proper governance, security, and reliability.