Apache Airflow

The Go SDK is a Go implementation of the Airflow Task SDK that allows you to write task functions in Go instead of Python. It leverages the Task Execution Interface (TEI/Task API) introduced in Airflow 3.0.0, which decouples the task language from the Airflow core by communicating over an HTTP Execution API.

Note: This is an experimental feature. APIs, wire protocols, and tooling may change without notice.

The apache-airflow-providers-akeyless package is a provider for the Akeyless Vault Platform. It integrates Akeyless into Apache Airflow by providing:

• Hooks: For interacting with the Akeyless API.
• Connection types: To manage Akeyless credentials within Airflow.
• Secrets Backend: To allow Airflow to retrieve static secrets, dynamic secrets, and rotated secrets directly from Akeyless.

Airflow provides several mechanisms to ensure observability of data pipelines, which are critical for diagnosing issues in unsupervised environments. The observability suite includes:

• Logging Mechanisms: Support for multiple logging backends to capture task execution details.
• Metrics: A built-in mechanism to emit metrics for collection, processing, and visualization in downstream systems (e.g., Prometheus, StatsD).
• Health Checks: Built-in capabilities to detect errors in the operation of the Airflow infrastructure itself.
• Error Notifications: Real-time error reporting via integration with Sentry to catch runtime exceptions and system errors.

Apache Airflow is a platform designed to programmatically author, schedule, and monitor workflows. It is built on the principle that workflows defined as code are more maintainable, versionable, testable, and collaborative.

Core Workflow Components

• DAGs (Directed Acyclic Graphs): The primary unit of work where you author workflows to orchestrate tasks.
• Scheduler: Executes tasks on a set of workers while respecting specified dependencies.
• CLI Utilities: Provides rich command-line tools for performing complex operations on DAGs.
• User Interface: A rich UI for visualizing pipelines in production, monitoring progress, and troubleshooting.

The Airflow Java SDK is a JVM-based SDK that allows you to write workflow bundles using any JVM-compatible language (such as Java or Kotlin) for consumption by Apache Airflow.

Key details:

• Implementation: The SDK and its execution-time logic are implemented in Kotlin.
• Runtime Requirement: Requires Java 11 or later at runtime.
• Usage: You can author workflows in JVM languages and have the Airflow scheduler and workers execute them via the SDK's coordination logic.

The apache-airflow-core package contains the essential functional components of Apache Airflow. It provides the underlying logic and services required to run an Airflow environment, specifically managing the following components:

• Scheduler: Orchestrates task execution and manages DAG runs.
• API Server: Provides the interface for interacting with Airflow.
• DAG File Processor: Responsible for discovering and parsing DAG files.
• Triggerer: Handles asynchronous task execution via triggers.

Apache Airflow is an open-source platform for developing, scheduling, and monitoring workflows. It is designed for batch-oriented data pipelines, machine learning, model training, and agentic or LLM-based workloads.

Key characteristics include:

• Workflows as Code: Workflows are defined entirely in Python, allowing for dynamic generation, parameterization, and version control.
• Extensibility: A wide range of built-in operators and a growing set of providers for orchestrating various technologies (including AI tools).
• Flexibility: Uses the Jinja templating engine for rich customizations.
• Scalability: Can run as a single process on a laptop or as a distributed system for massive workloads.
• Observability: A web-based UI for visualizing, managing, and debugging workflows.

The airflowctl tool is organized into several command groups. You can view the full list of commands by running airflowctl --help.

Available command groups include:

• Assets
• Auth
• Backfill
• Config
• Connections
• Dags
• Dag Runs
• Jobs
• Pools
• Providers
• Task Instances
• Tasks
• Variables
• Version