Imagine a fine orchestra where each instrument is masterfully arranged in a piece to create a rich comprehensive sound.

The world of data is much like musical interplay – each system, database, and application plays its part in creating a unified data flow. It's through the art of data orchestration that these disparate elements come together to create an accurate view of business operations and end-to-end data-driven systems.

Evolution of data management domains in the enterprise. Adapted from: Managing Data Orchestration and Integration at Scale
Evolution of data management domains in the enterprise. Adapted from: Managing Data Orchestration and Integration at Scale

Choosing the right data orchestration tool can be daunting.

There are so many options out there and the terminology is often not unified, which makes it tricky to decide. Plus, you´d need to think about the learning curves, whether the tool fits the current infrastructure, how much it'll scale, what it's going to cost you in the long run, and so on.

In this article, we'll explore the world of data orchestration and review the top 10 orchestration platforms with their key features, pros & cons, and pricing offers.

Here’s the TL;DR:

List of top 10 data orchestration tools

Software Key Features Starting Price Free Plan
n8n Source-available extendable low-code tool.
Available in cloud and self-hosted versions.
Free for Community
self-hosted version,
From €20/Mo for cloud
Prefect An open-source tool that requires knowledge
of Python programming. Offers cloud and
self-hosted options.
Free open-source tool
Luigi Fully open-source Python module with GUI for
easy observation. Lightweight and extendable.
Free open-source tool
Fully open-source advanced project
requiring Python programming skills.
Free open-source tool
Google's fully managed Airflow service
with even more advanced features and
seamless GCP integrations.
Usage-based pricing
AWS Glue A fully-managed serverless tool from Amazon,
tightly integrated with the AWS platform.
Usage-based pricing
Data Factory
Microsoft's own cloud tool that integrates
seamlessly with the Azure platform.
Usage-based pricing ❌, with
free trial
SAP Data
Arguably the most advanced data orchestration
platform. Supports the full data management
lifecycle, integrated with ML models for
data analysis.
Another proprietary tool integrated
with the Databricks Lakehouse Platform.
Usage-based pricing ❌, with
free trial
Fivetran A more traditional cloud-based data integration
tool with limited data orchestration
features. Available in cloud and On-Prem.
From $500/Mo according
to the price calculator

What is a data orchestration tool?

A data orchestration tool is a software solution that helps you automate the way data moves and is processed. It also keeps data tasks in check, making sure everything runs on time across all the different systems and apps that your org's data infrastructure is built on.

Unlike traditional data integration tools, it should be able to work with data of different types (SQL, streaming IoT data, non-structured documents and images, etc.) and connect to various supporting tools for data pre-processing and analysis.

A good data orchestrator helps you:

  • keep your data in sync,
  • automate repetitive tasks and processes,
  • ensure high data quality and consistency,
  • increase scalability,
  • prevent unnecessary data duplication and corruption and fix data inconsistencies

All of these capabilities improve efficiency and decision making, save lots of time and resources, and protect your applications from potential errors and inaccuracies.

How do you orchestrate data?

When orchestrating data as an engineer, the approach is hands-on and detail-oriented:

  • Identify main data modalities: structured (e.g. SQL databases), semi-structured, streaming data, IoT or unstructured texts, documents and media;
  • Identify data sources: cloud-based, on-premises or delivered by third-parties;
  • Create separate workflows for data pre-processing or initial analysis of unstructured data;
  • Unify data in a warehouse or catalog;
  • Create models and dashboards to provide actionable insights into data;
  • Continuously oversight, debug and fix workflows and data pipelines.

10 best data orchestration tools


Open-source: ❌, source-available

Free Tier: ✅ (self-hosted community version)

n8n is a source-available workflow automation platform that utilizes the concept of data orchestration by allowing users to connect different apps and services through a visual workflow editor.

With n8n's extensibility and openness, users can create new nodes, provide custom code (JS and Python are supported out of the box), integrate with other tools, and create complex workflows that can include bulk operations, error handling, and more.

n8n offers data transformation features to manipulate data within workflows, including:

  • conditional logic for dynamic process adjustments
  • data mapping to align data with target structures
  • transformation functions to modify and meet specific data needs.
n8n Pros
  • Many pre-built nodes: connect hundreds of apps and services out of the box
  • Drag-and-drop workflows: no coding required for most steps
  • Self-hosted: free community version or paid enterprise version for maximum control over your data
  • Support for various APIs: REST, GraphQL, MQTT, etc
  • Event-driven: workflows start upon certain triggers
  • Team-friendly: multi-user collaboration on paid plans
  • Clear pricing: fixed cloud or enterprise plans
n8n Cons
  • Self-hosting requires tech skills and setup
  • Setting up credentials for nodes to establish connections with external services or APIs takes some time
  • Complex data tasks require custom work: for example, ML models or advanced data processing must be created separately.
n8n Pricing
  • Self-hosted Community version is free
  • Starter single-user cloud plan – €20/Mo
  • Cloud Pro plan for teams – from €50/Mo
  • Contact sales for a quote on an enterprise plan (either cloud or self-hosted)


Open-source: ✅

Free Tier: ✅

Prefect is a modern, open-source workflow management system for data orchestration. The main idea behind Prefect is to allow teams to focus on data and its logic without worrying about the underlying execution environment.

With Prefect, engineers and data scientists can define workflows as Python code, which provides flexibility and ease of use, making it accessible to professionals accustomed to working in a Python ecosystem. The tool’s UI and dashboard provide a clear and intuitive interface to track workflow runs, status and history, thus providing visibility into the orchestration process.

Prefect Pros
  • Hybrid execution: supports cloud and on-prem setups
  • Parameterized flows: allows dynamic workflow adjustments
  • Easy integration: works with popular tools and cloud services including Kubernetes, AWS, GCP and Azure
  • Version control: Improves collaboration by tracking workflows
Prefect Cons
  • Programming knowledge: mostly Python-based workflows
  • Custom connectors: may need to build connections for niche sources
  • Community size: small user base may affect support
  • Some of the more advanced features and integrations are only available in the paid cloud version
Prefect Pricing
  • Free tier: basic workflows with limited features
  • Pro plan: from $450/month per workflow + $79/month for an additional user
  • Enterprise: Contact for a custom quote


Open-source: ✅

Free Tier: ✅

Luigi is an open-source Python module that helps to stitch together complex workflows. Developed by Spotify, it is designed to manage interdependent tasks, focusing on the success of the whole rather than individual parts.

Its Pythonic design makes it flexible and easily adaptable, allowing developers to write their tasks using Python code. Luigi is not just limited to data integration, but can also be used in scenarios where data needs to be processed in different ways and at different stages, facilitating data orchestration

Luigi Pros
  • Handles task dependencies automatically
  • Provides a web UI for data pipeline visualization
  • Easily extensible with Python packages
  • Integrates with Hadoop, Spark and AWS
  • Supports dynamic tasks creation with parameters
  • Tasks are also managed via the command line
Luigi Cons
  • Requires a good knowledge of Python
  • Not suitable for very large and complex workflows
  • Limited number of built-in alerts for task issues
  • Infrequent updates compared to competitors
Luigi Pricing
  • Free, as it's an open-source Python module

Apache Airflow

Open-source: ✅

Free Tier: ✅

Apache Airflow is an advanced open-source tool designed to programmatically create, schedule and monitor workflows. Airflow enables data engineers and developers to define workflows as Directed Acyclic Graphs (DAGs), with each node of the graph representing a task in a pipeline. This modular approach ensures that dependencies are strictly enforced, facilitating efficient and reliable execution of complex data processing tasks.

Airflow’s rich user interface makes it easy to visualize pipelines running in production, monitor their progress and troubleshoot issues when they arise. Furthermore, because Airflow supports scripting, it can accommodate complex logic, which is often required when orchestrating data across multiple environments and systems.

Apache Airflow Pros
  • Dynamic pipelines: configure with code for flexibility
  • Extensibility: add custom operators and plugins
  • Rich UI: detailed views of tasks and dependencies
  • Community support: strong and active contributors
  • Advanced scheduling: cron-like and backfill options
  • Role-based access: manage user permissions effectively
  • Cloud integration: connect to AWS, GCP, Azure
Apache Airflow Cons
  • Steep learning curve: complex setup and concepts
  • In addition, Airflow requires Python programming knowledge
  • Resource-heavy for big deployments
  • Limited data transformation capabilities: relies on external systems for heavy lifting
Apache Airflow Pricing
  • Free: open-source availability
  • Managed service costs vary (e.g., Google Cloud Composer)

Google Cloud Composer

Open-source: ❌

Free Tier: ❌

Google Cloud offers several orchestration tools designed for different use cases. One of them is Cloud Composer, a fully managed workflow orchestration service built on Apache Airflow.

Cloud Composer integrates seamlessly with other Google Cloud services, providing a centralized way to manage and orchestrate data across multiple environments. Its power lies in its ability to connect and control complex data workflows, ensuring efficient and reliable processing.

Here are some additional features of Cloud Composer compared to the original Airflow.

Google Cloud Composer Pros
  • Fully managed: less infrastructure and operational work
  • Built on Apache Airflow: access to a recognized open-source tool
  • Scalable: adjusts resources based on workflow needs
  • Secure: utilizes Google Cloud security features
  • Monitoring: includes advanced monitoring and logging
Google Cloud Composer Cons
  • Can be expensive for high-volume or complex workflows
  • Less control over the underlying infrastructure due to a fully managed setup
  • Setup complexity: requires deep knowledge of Google Cloud and Airflow
Google Cloud Composer Pricing
  • Pay-as-you-go: fees are charged based on compute and storage utilization
  • Estimated at $63/month for a small setup

AWS Glue

Open-source: ❌

Free Tier: ❌

AWS Glue is a fully managed extract, transform, and load (ETL) service. AWS Glue workflows capability handles both batch and streaming data and provides support for a wide range of data orchestration scenarios, from real-time analytics to large-scale data processing jobs.

Optimized for the cloud, AWS Glue is serverless and eliminates the need to provision or manage servers, reducing time spent on infrastructure tasks. It has a data catalog integrated with Amazon S3, RDS, Redshift and other AWS services, making it a flexible choice for a variety of data integration workflows.

AWS Glue Pros
  • Serverless: No server management required
  • Data Catalog for automatic schema discovery and management
  • Visual ETL editor simplifies job creation without coding expertise
  • Scalability: Adjusts resources to match job size
  • AWS Integration: Works with many AWS services
  • Automatic code generation for data transformation
  • Machine Learning integration: makes it easy to implement ML in apps
AWS Glue Cons
  • A steep learning curve for AWS newbies
  • AWS-Only: best suited for AWS users
  • Complex pricing: costs may escalate for intensive jobs
  • Restrictive: less flexibility compared to custom ETL solutions
AWS Glue Pricing
  • Pay-as-you-go: no upfront costs
  • Charges for ETL runtime, Data Catalog storage and DPUs
  • Region-specific pricing with possible data transfer fees

Azure Data Factory

Open-source: ❌

Free Tier: ❌

Azure Data Factory (ADF) is a cloud-based data integration service from Microsoft that allows users to create, schedule and orchestrate data pipelines. It provides a rich graphical interface for designing data-driven workflows and a variety of built-in activities and data transformations.

As part of Microsoft's Azure cloud platform, ADF integrates seamlessly with other Azure services such as Azure Data Lake Storage, Azure Synapse Analytics and Azure Machine Learning, providing a comprehensive data orchestration solution suitable for advanced analytics and big data projects.

Azure Data Factory Pros
  • Intuitive visual interface for creating pipelines
  • Integrates with both cloud and on-premise data sources
  • Wide variety of connectors for diverse data sources, such as SQL Server, Azure Blob Storage and Salesforce
  • Secure authentication with managed Azure identity without storing credentials in ADF.
  • Supports existing SQL Server Integration Services (SSIS) packages for easy cloud migration
  • Enables custom code with C#, Python or .NET
  • Globally available for compliance and low latency
  • Automatically scales to meet workload requirements
Azure Data Factory Cons
  • Costs can escalate quickly with large data sets
  • While ADF offers many transformations, it still requires custom code for complex transformations
  • Some users report that debugging can be difficult in complex pipelines
Azure Data Factory Pricing
  • Initially $200 in Azure credits for 30 days upon registering
  • Pay-as-you-go billing based on pipeline activity and data volume. Certain small or infrequent activities may be covered by the service's free tier
  • Additional charges may apply for integration with other Azure services

SAP Data Intelligence

Open-source: ❌

Free Tier: ❌

SAP Data Intelligence is a comprehensive data management solution that facilitates the orchestration of data in complex data landscapes. It bridges the gap between data integration and business process innovation, providing a unique platform that combines data-driven insights with action.

With SAP Data Intelligence, organizations can leverage a wide range of capabilities to manage the entire data lifecycle. From data collection and preparation to data integration and management, the tool ensures that high-quality data is accessible and actionable. SAP Data Intelligence effectively supports the new era of hybrid data processing, in which transactional and analytical domains converge.

SAP Data Intelligence Pros
  • Centralized data orchestration for multiple sources and destinations
  • Advanced data processing and analysis with Python, R, TensorFlow and Spark
  • Integrated machine learning and AI capabilities
  • Data catalog for discovery, governance and metadata
  • Flexible support for multi-cloud and on-premise environments
SAP Data Intelligence Cons
  • Steep learning curve for beginners
  • Complex setup and ongoing maintenance
  • Potentially high cost for small and midsize businesses
  • Requires significant computing resources for advanced features
  • Integration with legacy systems can be challenging
SAP Data Intelligence Pricing
  • Cloud version with subscription or pay-as-you-go options
  • Prices for the on-premise version are available on request

Databricks Workflow

Open-source: ❌

Free Tier: ❌

Databricks Workflows is an integrated tool within the Databricks Lakehouse Platform designed specifically for data orchestration. It leverages the unified analytics platform for data engineering, collaborative data science, full-lifecycle machine learning and business analytics through a visual interface. An evolution of the previous job scheduler system, Databricks Workflows provides a more powerful and flexible way to build multi-step data pipelines that can handle complex orchestration tasks.

Databricks Workflows Pros
  • Unified orchestration in the Databricks Lakehouse
  • Supports Python, SQL, Scala and R for building custom pipelines
  • Delta Live Tables for simple data pipelines with quality enforcement and automatic error handling
  • Various scheduling options (time, API, events)
  • Collaboration with shared notebooks and repositories
  • Git integration for version control
Databricks Workflows Cons
  • Costly for smaller teams/projects
  • Complex for basic pipeline requirements
  • Potential for vendor lock-in with Databricks
  • Tied to the Databricks platform
Databricks Workflows Pricing
  • Based on Databricks Lakehouse Platform usage (data processing units – DBUs), consumed by compute and data storage resources
  • Tiers for different team sizes, including a self-service option for smaller teams and a fully-managed service for enterprises
  • 14-day free trial available


Open-source: ❌

Free Tier: ✅

Fivetran is a cloud-based data integration service that helps organizations of all sizes automate their ETL processes. With Fivetran, companies can streamline the integration of data from various applications, databases and other data sources with minimal configuration. The platform stands out for its ease of use and ability to quickly connect and centralize data, enabling more efficient data pipeline orchestration.

Fivetran Pros
  • User-friendly UI/UX for easy integration setup
  • Automated data sync reduces manual work and errors
  • Integrated with dbt for post-load data transformation
  • Rollback feature for sync errors and schema drift prevention
Fivetran Cons
  • Primarily for ETL to cloud data warehouses, may not suit all data orchestration requirements
  • Limited customization options for complex scenarios
  • Pricing can be very high for small budgets
Fivetran Pricing
  • Free tier for small-scale automation
  • Starter, Standard and Enterprise plans with variable pricing. The price calculator and pricing guide show figures ranging from hundreds of USD per month to tens of thousands of USD monthly
  • Contact sales for Business Critical and Private Deployment details


What is data orchestration vs ETL?

ETL is a sequential process that focuses on extracting, transforming and loading structured data into a warehouse. Data orchestration encompasses ETL while also managing complex workflows, integrating diverse data types like unstructured and real-time data, to optimize analysis and decision-making.

What is API orchestration?

API orchestration is the process of integrating multiple API services to work together seamlessly, allowing for more complex operations that a single API alone may not support.

What is AI orchestration?

AI orchestration involves coordinating various artificial intelligence services and models to improve decision-making processes and optimise tasks across multiple systems or applications.

What is database orchestration?

Database orchestration refers to the automated arrangement, coordination and management of multiple database systems and services. It involves automating database provisioning, scaling, backups and replication to ensure that databases operate efficiently and reliably in different environments, such as cloud-based platforms or across distributed architectures.

What is workflow orchestration?

Workflow orchestration is the coordinated execution of a series of automated tasks or workflows, ensuring that they run in the correct order and that dependencies and data sharing between tasks are managed effectively.

Is orchestration the same as automation?

Orchestration refers to the coordination and management of automated tasks and processes to ensure they work in harmony, while automation is the act of making a single task operate without human intervention. Orchestration can be seen as a higher-level, strategic arrangement of automated tasks.

Wrap Up

In this article, we reviewed the 10 best data orchestration platforms, detailing their significance in managing complex workflows. Our roundup features top data orchestration tools, including n8n, Apache Airflow, Prefect, and more, ranging from:

  • cloud to on-prem solutions
  • open-source to proprietary
  • coming from independent vendors or large-scale providers

n8n fits into the data orchestration landscape thanks to its open, extendable architecture and a balance between a low-code approach vs custom coding. While certain data orchestration tasks are performed with dedicated solutions, n8n is capable of handling most of the tasks with ease and at a much more competitive price tag.

So don't forget to set up your own n8n instance: in the cloud or on-premises. Our free and paid plans are aimed at both small and large teams. Also, take a look at our enterprise plans.