The Differences Between Kestra and Azure Data Factory
How to Choose the Right Orchestration Platform
About Kestra & Azure Data Factory
Kestra is an open-source orchestration platform that enables teams to automate complex workflows across cloud, on-prem, and hybrid environments. It follows an everything-as-code approach, allowing users to define workflows in a straightforward YAML format. With its API-first architecture, Kestra ensures workflows are fully declarative, extensible, and free from proprietary limitations.
Azure Data Factory (ADF) is a cloud-based ETL and data integration service developed by Microsoft. It provides a no-code interface for orchestrating data pipelines but is tightly integrated with the Azure ecosystem. While ADF simplifies development with a drag-and-drop UI, it lacks the flexibility of an open-source and multi-cloud orchestration platform.
Installation and Setup
Azure Data Factory is a fully managed cloud service with no installation required. However, it is restricted to the Azure ecosystem, requiring users to configure security settings, data sources, and integration runtimes before running workflows.
Kestra offers a more flexible deployment model, supporting Docker, Kubernetes, and Terraform. This allows organizations to retain full control over execution, security, and data residency, making it adaptable to different infrastructure needs.
Workflow Definition
Kestra enables users to define workflows using YAML, ensuring clarity, maintainability, and version control. Inline scripting in Python, JavaScript, SQL, and Shell is supported for added flexibility.
Kestra also features a no-code editor that visually constructs workflows while ensuring they are fully represented as code. This ensures workflows remain declarative, portable, and version-controlled.
Azure Data Factory provides a no-code designer with a drag-and-drop UI for building pipelines. However, it does not automatically translate workflows into code, making version control and advanced automation more challenging.
Scalability and Performance
Kestra dynamically scales to handle large workloads efficiently, optimizing for both event-driven and scheduled workflows.
Azure Data Factory scales within the Azure infrastructure but follows a pricing model based on data movement, pipeline execution, and integration runtimes, which can lead to unpredictable costs.
Integration Capabilities
Kestra is designed to work with any cloud provider, on-premises infrastructure, and SaaS applications. Its extensive plugin ecosystem supports integrations with BigQuery, Snowflake, Kafka, Airbyte, dbt, and more.
Azure Data Factory is deeply integrated with Microsoft services like Azure Synapse, Data Lake, and Power BI but has limited out-of-the-box support for non-Microsoft technologies, which may be restrictive for multi-cloud strategies.
Monitoring and Observability
Kestra provides built-in workflow monitoring, logs, and real-time execution tracking, additionally Kestra offers a custom dashboard feature to display the most important metrics.
Azure Data Factory relies on Azure Monitor and Log Analytics for observability, requiring additional configuration and incurring extra costs.
Open-Source vs. Proprietary
Kestra is fully open-source, supported by an active community, and offers enterprise options for advanced needs.
Azure Data Factory is a proprietary Microsoft service, locking workflows within the Azure ecosystem and tying users to Azure infrastructure and pricing.
Advanced Features
Azure Data Factory:
- Data Compression: Compress data during copy activities to optimize bandwidth.
- Extensive Connectivity: Connects to a wide range of data sources.
- Custom Event Triggers: Automate processing based on custom events.
- Data Preview & Validation: Ensure correctness during data transfers.
- Customizable Data Flows: Add custom actions or processing steps.
- Integrated Security: Entra ID integration and role-based access control.
Kestra:
- Hybrid orchestration: Supports cloud, on-prem, and multi-cloud workflows.
- Full-code, low-code, and no-code capabilities in one platform.
- Prebuilt plugin ecosystem with extensive integrations.
- Version-controlled workflows with built-in governance.
- Lightweight YAML-based configuration, easy to modify and maintain.
| Feature | Kestra | Azure Data Factory |
|---|---|---|
| Core Focus | Orchestrating workflows, tasks, operations, and data pipelines | ETL and data pipeline orchestration within Azure |
| Language Support | Language-agnostic (YAML for orchestration) | No native scripting, limited to Azure-native transformations |
| Ease of Setup | Quick setup, scheduled workflow in minutes | Requires Azure account, complex permission setup |
| Developer Experience | Built-in code editor, live-updating DAG view, documentation, and blueprints | Drag-and-drop UI, limited flexibility for developers |
| Workflow Definitions | YAML-based, with inline scripting support (Python, JavaScript, SQL, Bash) | No-code visual editor, no built-in scripting support |
| Integration/Extensibility | Plugin ecosystem, REST API, webhooks, and event triggers | Primarily integrates with Azure services, limited external connectors |
| Cross-role Collaboration | Designed for all engineers and business users | Business-friendly but restrictive for engineers |
| Data Lineage & Debugging | Task-level metadata through metrics and outputs | Built-in monitoring via Azure services but lacks transparency |
| Business Logic Support | Supports SQL, Python, R, Rust, Bash, and more | No direct code support, relies on Azure functions for logic execution |
| No-Code / Low-code Interface | Low-code UI that fully replicates workflows as YAML, ensuring version control and flexibility | Drag-and-drop editor, but workflows are not automatically translated into code |
| Scalability | Runs on any cloud, allowing unlimited scaling across cloud and on-prem | Scales within Azure but can become expensive at higher workloads |
| Cloud Integrations | Works with AWS, GCP, Azure, and on-prem tools, supports event-driven and scheduled workflows | Limited to Microsoft technologies, with deep Azure integration but fewer external connectors |
| Vendor Lock-in | Fully open-source and can run on any cloud or on-prem | Proprietary solution restricted to the Azure ecosystem |
Getting Started
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