The Apache Airflow project aims to provide a platform to programmatically author, schedule, and monitor workflows. It is designed to manage complex computational workflows and data pipelines.
Business priorities and goals for adopting Apache Airflow include:
- Automating data pipelines and ETL processes to improve efficiency and reduce manual effort.
- Orchestrating machine learning workflows for model training, deployment, and monitoring.
- Enhancing data reliability and consistency by automating data processing tasks.
- Improving observability and monitoring of complex workflows to ensure timely execution and identify failures.
- Enabling self-service data engineering capabilities for data scientists and analysts.
Most important business risks that need to be addressed:
- Data Integrity Risks: Incorrect or incomplete data processing due to workflow failures or misconfigurations can lead to flawed business decisions.
- Data Availability Risks: Downtime or unavailability of Airflow can disrupt critical data pipelines, impacting business operations and reporting.
- Data Security Risks: Unauthorized access to sensitive data processed by workflows or to the Airflow platform itself can lead to data breaches and compliance violations.
- Operational Risks: Complexity of managing and maintaining Airflow infrastructure and workflows can lead to operational inefficiencies and errors.
- Compliance Risks: Failure to meet regulatory compliance requirements (e.g., GDPR, HIPAA) due to insecure data processing or lack of auditability.
Existing security controls:
- security control: Role-Based Access Control (RBAC) - Implemented within Airflow Web UI and API to manage user permissions and access to resources (DAGs, connections, pools, etc.). Described in Airflow documentation and code.
- security control: Authentication - Implemented via various backend options (e.g., password, LDAP, OAuth) for user login to the Airflow Web UI and API. Configurable in
airflow.cfgand documented. - security control: Connections Encryption - Supports encryption for connections to external systems (databases, cloud services) using connection strings and parameters. Configurable when defining connections in Airflow UI or via code.
- security control: Logging and Auditing - Airflow logs workflow execution details, user actions, and system events, providing audit trails. Logs are stored in configured logging locations (local files, cloud storage, etc.). Configurable in
airflow.cfgand documented.
Accepted risks:
- accepted risk: Vulnerabilities in third-party dependencies - Airflow relies on numerous Python packages, which may contain security vulnerabilities. Risk is partially mitigated by dependency scanning and updates.
- accepted risk: Misconfigurations - Incorrectly configured Airflow settings, permissions, or connections can lead to security weaknesses. Risk is mitigated by documentation and best practices.
- accepted risk: Insider threats - Malicious or negligent actions by authorized users can compromise the system. Risk is mitigated by RBAC, auditing, and security awareness training.
Recommended security controls:
- security control: Secrets Management - Implement a dedicated secrets management solution (e.g., HashiCorp Vault, AWS Secrets Manager) to securely store and manage sensitive credentials (API keys, database passwords) used in Airflow connections and DAGs.
- security control: Network Segmentation - Isolate Airflow components (webserver, scheduler, worker) and the underlying infrastructure within secure network segments to limit the impact of potential breaches.
- security control: Vulnerability Scanning - Regularly scan Airflow infrastructure and application dependencies for known vulnerabilities using automated tools.
- security control: Security Audits - Conduct periodic security audits and penetration testing to identify and address security weaknesses in Airflow deployments and configurations.
- security control: Incident Response Plan - Develop and maintain an incident response plan specifically for Airflow security incidents to ensure timely and effective handling of breaches or attacks.
Security requirements:
- Authentication:
- Requirement: Securely authenticate users accessing the Airflow Web UI and API.
- Requirement: Support multi-factor authentication (MFA) for enhanced user authentication.
- Requirement: Implement service account authentication for Airflow components interacting with external systems.
- Authorization:
- Requirement: Enforce fine-grained role-based access control (RBAC) to manage user permissions for DAGs, connections, and other Airflow resources.
- Requirement: Implement authorization policies to control access to sensitive data and operations within workflows.
- Requirement: Regularly review and update user roles and permissions to maintain least privilege access.
- Input Validation:
- Requirement: Validate inputs to DAGs and operators to prevent injection attacks and ensure data integrity.
- Requirement: Sanitize user-provided inputs in the Airflow Web UI to prevent cross-site scripting (XSS) vulnerabilities.
- Requirement: Implement input validation for API requests to prevent malicious or malformed data from being processed.
- Cryptography:
- Requirement: Encrypt sensitive data at rest, including database credentials, API keys, and other secrets stored within Airflow.
- Requirement: Encrypt data in transit between Airflow components and external systems using TLS/SSL.
- Requirement: Utilize strong cryptographic algorithms and key management practices for encryption and decryption operations.
flowchart LR
subgraph "Organization"
A["Data Engineer"]
B["Data Scientist"]
C["DevOps Engineer"]
end
D["Apache Airflow System"]:::highlight
E["Data Sources"]
F["Data Lake/Warehouse"]
G["Monitoring System"]
H["Notification System"]
A --> D
B --> D
C --> D
D --> E
D --> F
D --> G
D --> H
classDef highlight stroke:#f9f,stroke-width:2px
Context Diagram Elements:
-
Element:
- Name: Data Engineer
- Type: User
- Description: Professionals responsible for designing, building, and maintaining data pipelines using Airflow.
- Responsibilities: Creating and managing DAGs, defining workflows, monitoring pipeline execution, troubleshooting issues.
- Security controls: Authenticated access to Airflow Web UI and API via RBAC.
-
Element:
- Name: Data Scientist
- Type: User
- Description: Professionals who utilize data pipelines orchestrated by Airflow for data analysis, model training, and experimentation.
- Responsibilities: Triggering data pipelines, accessing processed data, monitoring workflow status relevant to their tasks.
- Security controls: Authenticated access to Airflow Web UI and API via RBAC, potentially with restricted permissions compared to Data Engineers.
-
Element:
- Name: DevOps Engineer
- Type: User
- Description: Professionals responsible for deploying, managing, and maintaining the Airflow infrastructure and ensuring its availability and performance.
- Responsibilities: Infrastructure provisioning, Airflow installation and configuration, monitoring system health, implementing security controls, managing deployments.
- Security controls: Access to infrastructure management tools, potentially privileged access to Airflow configuration and underlying systems, authenticated access to Airflow Web UI and API via RBAC.
-
Element:
- Name: Apache Airflow System
- Type: System
- Description: The workflow orchestration platform that programmatically authors, schedules, and monitors data pipelines.
- Responsibilities: DAG parsing and scheduling, task execution, workflow monitoring, logging, user interface, API access.
- Security controls: Authentication, Authorization (RBAC), Input Validation, Cryptography (secrets management, connection encryption), Logging and Auditing.
-
Element:
- Name: Data Sources
- Type: External System
- Description: External systems that provide data to be processed by Airflow workflows. Examples include databases, APIs, cloud storage services, message queues.
- Responsibilities: Providing data to Airflow pipelines, authenticating Airflow's access requests.
- Security controls: Authentication mechanisms (API keys, passwords, tokens), authorization policies, network access controls.
-
Element:
- Name: Data Lake/Warehouse
- Type: External System
- Description: Centralized repositories where processed data from Airflow workflows is stored. Examples include cloud storage (S3, GCS, Azure Blob Storage), data warehouses (Snowflake, BigQuery, Redshift).
- Responsibilities: Storing processed data, providing access to data for downstream applications and users, enforcing data access controls.
- Security controls: Access control lists (ACLs), Identity and Access Management (IAM), encryption at rest and in transit.
-
Element:
- Name: Monitoring System
- Type: External System
- Description: Systems used to monitor the health and performance of Airflow and its workflows. Examples include Prometheus, Grafana, CloudWatch, Datadog.
- Responsibilities: Collecting metrics and logs from Airflow, providing dashboards and alerts, enabling performance monitoring and troubleshooting.
- Security controls: Secure API access, authentication and authorization for accessing monitoring data.
-
Element:
- Name: Notification System
- Type: External System
- Description: Systems used to send notifications about workflow status, failures, or alerts. Examples include email servers, Slack, PagerDuty.
- Responsibilities: Sending notifications based on Airflow events, ensuring reliable delivery of notifications.
- Security controls: Secure API access, authentication for sending notifications, potentially encryption of notification content.
flowchart LR
subgraph "Apache Airflow System"
A["Webserver"]:::container
B["Scheduler"]:::container
C["Worker"]:::container
D["Database"]:::container
E["Message Queue"]:::container
F["Flower"]:::container
end
G["Data Sources"]:::external_system
H["Data Lake/Warehouse"]:::external_system
I["Monitoring System"]:::external_system
A -- "HTTP/HTTPS" --> G
A -- "HTTP/HTTPS" --> H
A -- "Database Connection" --> D
A -- "Message Queue" --> E
A -- "HTTP/HTTPS (Metrics)" --> I
B -- "Database Connection" --> D
B -- "Message Queue" --> E
C -- "Database Connection" --> D
C -- "Message Queue" --> E
C -- "Execute Tasks" --> G
C -- "Execute Tasks" --> H
F -- "Message Queue" --> E
F -- "Database Connection" --> D
classDef container stroke:#f66,stroke-width:2px
classDef external_system stroke:#66f,stroke-width:2px
Container Diagram Elements:
-
Element:
- Name: Webserver
- Type: Container
- Description: Provides the user interface (UI) and API for interacting with Airflow. Handles user authentication, authorization, DAG management, workflow monitoring, and API requests.
- Responsibilities: Serving the Airflow UI, handling API requests, managing user sessions, providing access control.
- Security controls: Authentication (user login), Authorization (RBAC), Input Validation (UI inputs, API requests), HTTPS for UI and API access, potentially rate limiting to prevent abuse.
-
Element:
- Name: Scheduler
- Type: Container
- Description: Responsible for parsing DAG files, scheduling tasks based on defined schedules and dependencies, and triggering task execution.
- Responsibilities: DAG parsing, task scheduling, workflow orchestration, managing task states.
- Security controls: Access control to DAG files and configurations, secure communication with the database and message queue, input validation of DAG definitions.
-
Element:
- Name: Worker
- Type: Container
- Description: Executes the actual tasks defined in DAGs. Workers pull tasks from the message queue and execute them using configured executors.
- Responsibilities: Task execution, interacting with external systems (data sources, data warehouses), logging task execution details.
- Security controls: Secure communication with the message queue and database, isolation of task execution environments (e.g., using Docker), secure handling of credentials for external system access.
-
Element:
- Name: Database
- Type: Container
- Description: Stores Airflow metadata, including DAG definitions, task states, user information, connections, and logs. PostgreSQL or MySQL are commonly used.
- Responsibilities: Persistent storage of Airflow metadata, providing data access to other Airflow components.
- Security controls: Database access control (user permissions), encryption at rest, encryption in transit (TLS/SSL for database connections), regular backups.
-
Element:
- Name: Message Queue
- Type: Container
- Description: Used for communication between the Scheduler and Workers. The Scheduler enqueues tasks, and Workers dequeue and execute them. Celery or Redis are commonly used.
- Responsibilities: Task queuing and distribution, asynchronous communication between Scheduler and Workers.
- Security controls: Access control to the message queue, encryption in transit (if supported by the message queue), secure configuration of message queue brokers.
-
Element:
- Name: Flower
- Type: Container
- Description: An optional real-time monitor and web administrator for Celery. Provides insights into worker status, task queues, and task execution.
- Responsibilities: Monitoring Celery workers and tasks, providing a UI for Celery monitoring.
- Security controls: Authentication and authorization for accessing Flower UI, secure communication with the message queue and database.
Deployment Architecture: Kubernetes
flowchart LR
subgraph "Kubernetes Cluster"
subgraph "Namespace: airflow"
A["Pod: airflow-webserver"]:::pod
B["Pod: airflow-scheduler"]:::pod
C["Pod: airflow-worker-1"]:::pod
D["Pod: airflow-worker-2"]:::pod
E["Service: airflow-webserver-svc"]:::service
F["Service: airflow-flower-svc"]:::service
G["Deployment: airflow-webserver-deploy"]:::deployment
H["Deployment: airflow-scheduler-deploy"]:::deployment
I["Deployment: airflow-worker-deploy"]:::deployment
J["Deployment: airflow-flower-deploy"]:::deployment
K["PersistentVolumeClaim: airflow-db-pvc"]:::pvc
L["PersistentVolumeClaim: airflow-logs-pvc"]:::pvc
M["Pod: airflow-flower"]:::pod
N["Database: PostgreSQL"]:::database
O["Message Queue: Redis"]:::database
end
P["Ingress Controller"]:::ingress
end
Q["External User"]
Q -- "HTTPS" --> P
P -- "Route" --> E
E -- "HTTP" --> A
A -- "Database Connection" --> N
B -- "Database Connection" --> N
C -- "Database Connection" --> N
D -- "Database Connection" --> N
B -- "Message Queue Connection" --> O
C -- "Message Queue Connection" --> O
D -- "Message Queue Connection" --> O
A -- "Message Queue Connection" --> O
A -- "Logs" --> L
B -- "Logs" --> L
C -- "Logs" --> L
D -- "Logs" --> L
A -- "Persistent Storage" --> K
B -- "Persistent Storage" --> K
C -- "Persistent Storage" --> K
D -- "Persistent Storage" --> K
P -- "Route" --> F
F -- "HTTP" --> M
M -- "Message Queue Connection" --> O
M -- "Database Connection" --> N
classDef pod stroke:#bbb,stroke-width:2px,fill:#eee,stroke-dasharray: 5 5
classDef service stroke:#bbf,stroke-width:2px,fill:#eef
classDef deployment stroke:#fbb,stroke-width:2px,fill:#fee
classDef pvc stroke:#bfb,stroke-width:2px,fill:#efe
classDef ingress stroke:#ccf,stroke-width:2px,fill:#eef
classDef database stroke:#afa,stroke-width:2px,fill:#efe
Deployment Diagram Elements (Kubernetes):
-
Element:
- Name: Kubernetes Cluster
- Type: Infrastructure
- Description: The container orchestration platform where Airflow is deployed. Provides scalability, resilience, and management for Airflow components.
- Responsibilities: Container orchestration, resource management, service discovery, load balancing.
- Security controls: Network policies, RBAC for Kubernetes API access, pod security policies/admission controllers, security monitoring.
-
Element:
- Name: Namespace: airflow
- Type: Kubernetes Namespace
- Description: A logical isolation boundary within the Kubernetes cluster dedicated to Airflow resources.
- Responsibilities: Resource isolation, access control within the Airflow deployment.
- Security controls: Kubernetes RBAC for namespace access, network policies to isolate namespace traffic.
-
Element:
- Name: Pod: airflow-webserver
- Type: Kubernetes Pod
- Description: Kubernetes Pod running the Airflow Webserver container.
- Responsibilities: Serving the Airflow UI and API.
- Security controls: Container security context, resource limits, network policies.
-
Element:
- Name: Pod: airflow-scheduler
- Type: Kubernetes Pod
- Description: Kubernetes Pod running the Airflow Scheduler container.
- Responsibilities: DAG scheduling and task orchestration.
- Security controls: Container security context, resource limits, network policies.
-
Element:
- Name: Pod: airflow-worker-1, airflow-worker-2
- Type: Kubernetes Pod
- Description: Kubernetes Pods running Airflow Worker containers. Scaled horizontally for task execution capacity.
- Responsibilities: Executing tasks from the message queue.
- Security controls: Container security context, resource limits, network policies, worker isolation.
-
Element:
- Name: Service: airflow-webserver-svc
- Type: Kubernetes Service
- Description: Kubernetes Service exposing the Airflow Webserver Pods within the cluster.
- Responsibilities: Load balancing and service discovery for Webserver Pods.
- Security controls: Network policies, service account access control.
-
Element:
- Name: Service: airflow-flower-svc
- Type: Kubernetes Service
- Description: Kubernetes Service exposing the Flower Pods within the cluster.
- Responsibilities: Load balancing and service discovery for Flower Pods.
- Security controls: Network policies, service account access control.
-
Element:
- Name: Deployment: airflow-webserver-deploy, airflow-scheduler-deploy, airflow-worker-deploy, airflow-flower-deploy
- Type: Kubernetes Deployment
- Description: Kubernetes Deployments managing the lifecycle and scaling of Airflow component Pods.
- Responsibilities: Declarative management of Pod replicas, rolling updates, rollbacks.
- Security controls: Deployment strategies, update policies, resource management.
-
Element:
- Name: PersistentVolumeClaim: airflow-db-pvc, airflow-logs-pvc
- Type: Kubernetes PersistentVolumeClaim
- Description: Requests for persistent storage for the Airflow Database and Logs.
- Responsibilities: Persistent storage for Airflow metadata and logs across Pod restarts.
- Security controls: Access control to persistent volumes, encryption at rest for persistent volumes.
-
Element:
- Name: Pod: airflow-flower
- Type: Kubernetes Pod
- Description: Kubernetes Pod running the Flower container.
- Responsibilities: Monitoring Celery workers and tasks.
- Security controls: Container security context, resource limits, network policies.
-
Element:
- Name: Database: PostgreSQL
- Type: Database (Kubernetes managed or external)
- Description: PostgreSQL database used for Airflow metadata storage. Can be deployed within Kubernetes or externally managed.
- Responsibilities: Persistent storage of Airflow metadata.
- Security controls: Database access control, encryption at rest and in transit, regular backups, database hardening.
-
Element:
- Name: Message Queue: Redis
- Type: Message Queue (Kubernetes managed or external)
- Description: Redis message queue used for task queuing and communication between Scheduler and Workers. Can be deployed within Kubernetes or externally managed.
- Responsibilities: Task queuing and distribution.
- Security controls: Access control to Redis, encryption in transit (if configured), secure Redis configuration.
-
Element:
- Name: Ingress Controller
- Type: Kubernetes Ingress Controller
- Description: Manages external access to Kubernetes services, routing external HTTPS traffic to the Airflow Webserver Service.
- Responsibilities: External access management, TLS termination, routing.
- Security controls: TLS configuration, ingress controller security hardening, rate limiting, Web Application Firewall (WAF).
-
Element:
- Name: External User
- Type: User
- Description: Users accessing the Airflow Web UI and API from outside the Kubernetes cluster.
- Responsibilities: Interacting with Airflow for workflow management and monitoring.
- Security controls: Authentication via Airflow Web UI, HTTPS for secure communication.
flowchart LR
A["Developer"] --> B{Code Changes};
B -- "Git Push" --> C["GitHub Repository"];
C --> D["GitHub Actions CI"];
D -- "Build & Test" --> E["Build Artifacts"];
E -- "Containerize" --> F["Container Image"];
F -- "Push Image" --> G["Container Registry"];
G --> H["Deployment System (Kubernetes)"];
subgraph "GitHub Actions CI"
D --> D1["Source Code Checkout"];
D --> D2["Dependency Resolution"];
D --> D3["Unit & Integration Tests"];
D --> D4["SAST Scanning"];
D --> D5["Build & Package"];
D --> D6["Container Image Build"];
D --> D7["Image Scanning"];
end
style D fill:#eee,stroke:#bbb,stroke-width:2px,stroke-dasharray: 5 5
Build Process Description:
- Developer: Developers write and modify Airflow code, including DAGs, operators, and core Airflow components.
- Code Changes: Code changes are committed and pushed to the GitHub repository.
- GitHub Repository: The central source code repository for the Airflow project.
- GitHub Actions CI: GitHub Actions workflows are triggered on code changes (e.g., pull requests, pushes to main branch).
- GitHub Actions CI - Source Code Checkout: CI pipeline starts by checking out the latest code changes from the repository.
- GitHub Actions CI - Dependency Resolution: CI pipeline resolves project dependencies using package managers (e.g., pip for Python).
- GitHub Actions CI - Unit & Integration Tests: Automated unit and integration tests are executed to verify code functionality and prevent regressions.
- GitHub Actions CI - SAST Scanning: Static Application Security Testing (SAST) tools are used to scan the codebase for potential security vulnerabilities.
- GitHub Actions CI - Build & Package: The project is built and packaged into distributable artifacts (e.g., Python packages, wheels).
- GitHub Actions CI - Container Image Build: Dockerfiles are used to build container images for Airflow components (webserver, scheduler, worker).
- GitHub Actions CI - Image Scanning: Container images are scanned for known vulnerabilities using container image scanning tools.
- Build Artifacts: Build artifacts, including Python packages and container images, are produced by the CI pipeline.
- Container Image: Container images for Airflow components are created.
- Container Registry: Container images are pushed to a container registry (e.g., Docker Hub, GitHub Container Registry).
- Deployment System (Kubernetes): The deployment system (e.g., Kubernetes) pulls the latest container images from the container registry to deploy or update Airflow components.
Build Process Security Controls:
- security control: Automated Build Process: Use of GitHub Actions for automated CI/CD pipelines ensures consistent and repeatable builds, reducing manual errors and improving security.
- security control: Source Code Management: Using GitHub for version control and code review provides traceability and helps prevent unauthorized code changes.
- security control: Unit and Integration Tests: Automated tests ensure code quality and reduce the risk of introducing vulnerabilities through code changes.
- security control: SAST Scanning: Static Application Security Testing (SAST) tools identify potential security vulnerabilities in the codebase early in the development lifecycle.
- security control: Dependency Scanning: Dependency scanning tools analyze project dependencies for known vulnerabilities, ensuring that vulnerable libraries are not included in the build.
- security control: Container Image Scanning: Container image scanning tools identify vulnerabilities in container images before deployment, reducing the risk of deploying vulnerable containers.
- security control: Secure Container Registry: Using a secure container registry with access controls and vulnerability scanning ensures the integrity and security of container images.
- security control: Code Signing/Artifact Signing: Signing build artifacts and container images can provide assurance of their authenticity and integrity.
Critical business processes we are trying to protect:
- Data Pipeline Execution: Ensuring reliable and timely execution of data pipelines is critical for data availability, reporting, and downstream applications.
- Workflow Orchestration: Maintaining the integrity and availability of workflow orchestration is essential for automated data processing and business operations.
- Data Ingestion and Processing: Protecting the integrity and confidentiality of data ingested and processed by Airflow workflows is crucial for data quality and compliance.
- Monitoring and Alerting: Reliable monitoring and alerting are necessary for timely detection and response to workflow failures and system issues.
Data we are trying to protect and their sensitivity:
- Workflow Definitions (DAGs): DAGs may contain sensitive information such as connection details, API keys, and business logic. Sensitivity: Medium to High (depending on content).
- Connection Credentials: Database passwords, API keys, cloud service credentials stored in Airflow connections. Sensitivity: High.
- Logs: Airflow logs may contain sensitive data processed by workflows, system events, and user actions. Sensitivity: Medium to High (depending on content).
- Processed Data: Data processed by Airflow workflows can range from low to high sensitivity depending on the business context (e.g., PII, financial data, health records). Sensitivity: Variable, potentially High.
- Airflow Metadata: Database containing Airflow configurations, user information, and workflow states. Sensitivity: Medium.
Questions:
- What is the specific deployment environment for Airflow (cloud, on-premises, hybrid)?
- What are the specific data sources and data destinations that Airflow will interact with?
- What is the sensitivity level of the data being processed by Airflow workflows?
- Are there any specific compliance requirements (e.g., GDPR, HIPAA, PCI DSS) that need to be considered?
- What is the organization's risk appetite regarding security vulnerabilities and operational disruptions?
- What secrets management solution is currently in use or planned for adoption?
- What monitoring and alerting systems are currently in place?
Assumptions:
- Assumption: Airflow is deployed in a Kubernetes environment for scalability and resilience.
- Assumption: Security is a high priority for the Airflow deployment due to the sensitivity of data and business processes involved.
- Assumption: A secrets management solution will be implemented to securely manage sensitive credentials.
- Assumption: Standard security best practices will be followed for infrastructure and application security.
- Assumption: The organization has a DevOps team responsible for managing and maintaining the Airflow infrastructure.