Compare 12 Data Orchestration Tools Based on 800+ Reviews in '24
She has experience as a quantitative market researcher and data analyst in the fintech industry.
Hazal received her master's degree from the University of Carlos III of Madrid and her bachelor's degree from Bilkent University.
According to big data stats, 90% of companies recognize data as a crucial driver for digital transformation. However, they only utilize about 25-40% of the data they generate, indicating that 60-75% of their data goes unused. This low rate of adoption can be attributed to several factors, including inadequate data pipeline orchestration, poor data quality, and challenges with version control.
In this article, AIMultiple identifies the leading data orchestration tools to help data teams select the best solutions for managing their data processes and data warehouses effectively.
| Product | Primary Use | The number of employees | The number of reviews | Average score | Workflow Design |
|---|---|---|---|---|---|
| ActiveBatch | Workload automation and data orchestration | 379 | 251 | 4.6 | Workflow design with low-code/no-code |
| Redwood RunMyJobs | Workload automation and job scheduling | 379 | 183 | 4.7 | Centralized console for managing workflows |
| Azure Data Factory | Data integration and orchestration | 224,242 | 71 | 4.5 | Visual pipeline design |
| Google Cloud Dataflow | Stream and batch data processing | 288,917 | 61 | 4.3 | Unified model for stream and batch data |
| Keboola | Data orchestration, open-source | 51 | 101 | 4.7 | Intuitive design for complex workflows |
| Prefect | Data orchestration and integration | 93 | 99 | 4.6 | Visual workflow design |
| Rivery | Data integration and orchestration | 30 | 98 | 4.7 | Visual-based data pipeline creation |
Screening data orchestration tools
The data orchestration tools market can be broadly divided into two categories: open-source tools, which offer flexibility and community-driven development, and commercial tools, which provide additional support, features, and enterprise-level scalability.
We selected companies for this benchmark based on two key criteria:
- The number of Employees: The employee count reflects the scale and capacity of a company’s operations. Larger companies with more employees often have greater resources for product development, customer support, and innovation. Evaluating companies based on employee numbers provides insights into their potential to meet complex customer needs and indicates their level of maturity in the market. Therefore, we excluded the companies with less than 30 employees on their LinkedIn profile.
- Presence in B2B Review Sites: A strong presence on B2B review sites indicates a company’s recognition and credibility within the business community. Companies with high visibility on these platforms often demonstrate customer satisfaction and product quality, offering valuable insights from users about the company’s tools and services.
Enterprise data orchestration tools
1. ActiveBatch
ActiveBatch is a workload automation platform designed for data orchestration. It integrates with multiple data sources and environments, providing a low-code/no-code interface to design complex workflows that span across cloud, on-premise, and hybrid systems. It includes features like conditional logic and resource management.
Users can orchestrate data flows by creating automated workflows and defining dependencies. ActiveBatch allows scheduling tasks, monitoring data pipelines in real-time, and setting up alerts for errors or specific events. The platform supports diverse use cases, from data extraction and transformation to end-to-end business processes.
2. RunMyJobs
RunMyJobs is a cloud-based workload automation tool designed for data orchestration across multiple platforms. It centralizes job scheduling and allows users to manage complex workflows in a unified console, with built-in features for data management and process automation.
Users can define workflows, set triggers for job execution, and schedule tasks flexibly. RunMyJobs supports advanced error handling, real-time monitoring, and customizable notifications. It allows users to automate data movement and integration across different environments, supporting a wide range of orchestration scenarios.
The visual below shows how RunMyJobs can coordinate and integrate various data flows and system activities, integrating across on-premises environments, operating system tasks, API adapters, and cloud service providers:
3. Azure data factory
Azure Data Factory is a cloud-based data integration service from Microsoft designed for data orchestration and ETL. It enables users to create, schedule, and orchestrate complex data pipelines across cloud and on-premise environments, with support for diverse data sources and destinations.
Users can design data pipelines, set up data transformations, and orchestrate data movements across Azure and other cloud platforms. Azure Data Factory provides a visual interface for creating workflows, along with real-time monitoring, error handling, and extensive integration options. It supports batch and streaming data processing, making it flexible for various orchestration needs.
This image is from Azure Data Factory, showcasing its capability to monitor triggered pipeline runs within a specified time period. Users can adjust the time range and filter the list by status, pipeline name, or annotation to manage and track pipeline activities:
4. Google Dataflow
Google Dataflow is a cloud-based data processing service from Google Cloud designed for stream and batch data processing. It’s a managed service for data orchestration, providing a unified model for processing large-scale data in real-time or in batches.
Users can create data pipelines for real-time data processing and integrate with other Google Cloud services like BigQuery. Dataflow allows users to orchestrate complex data workflows, apply transformations, and process data from various sources. It offers scalability, automatic resource provisioning, and built-in monitoring, enabling flexible data orchestration across a range of scenarios.
5. Prefect
Prefect is an open-source data orchestration tool designed to build, manage, and monitor complex workflows. It provides a flexible and extensible framework for defining and scheduling workflows with features like task retries, error handling, and comprehensive monitoring.
Users can create and manage workflows using Prefect’s intuitive API and UI. It allows orchestrating tasks, scheduling job execution, and handling errors gracefully. Prefect’s robust monitoring and alerting system helps users ensure the stability and reliability of data pipelines. It integrates with various data platforms and tools, providing flexibility in managing data workflows.
The visual below displays Prefect’s capabilities:
6. Rivery
Rivery is a cloud-based data orchestration platform designed for building and managing data pipelines. It focuses on data integration and ETL, providing a visual interface to create, schedule, and automate complex data workflows.
Users can build data pipelines by dragging and dropping tasks into a visual workflow. Rivery allows scheduling, real-time monitoring, and alerts to manage the orchestration process. It integrates with various data sources and destinations, enabling users to automate data extraction, transformation, and loading tasks across different platforms.
The video below shows how Rivery can serve as a DataOps management tool:
7. Keboola
Keboola is a data platform that combines data integration, transformation, and orchestration capabilities. It is designed to build complex data workflows and automate data processing tasks, focusing on simplifying data operations for business users.
Users can create, schedule, and manage data pipelines with Keboola’s visual interface. It supports data orchestration through flexible scheduling, error handling, and real-time monitoring. Keboola integrates with a wide range of data sources and platforms, providing a unified solution for orchestrating data workflows and automating ETL processes.
The image below shows an overview of Keboola platform:
Open-source data orchestration tools
Here is a list of top open-source data orchestration tools with GitHub stars:
| Tool | Primary Use | GitHub Star | Workflow Design |
|---|---|---|---|
| Apache Airflow | Workflow orchestration | 34.5k | Directed Acyclic Graph (DAG) |
| Dagster | Data orchestration | 10.2k | DAG with solid-based design |
| Mage | Data pipelines | 3.9k | Graph-based with low-code interface |
| Luigi | Data pipelines | 17.3k | DAG-based |
| Flyte | Data and ML workflow orchestration | 4.8k | Directed Graph |
- Apache Airflow: An open-source platform for orchestrating complex data workflows and pipelines. It allows users to define, schedule, and monitor workflows with a flexible and extensible design.
- Dagster: An open-source data orchestration framework designed for building and managing data pipelines. It provides a modern approach to data orchestration with a focus on flexibility, observability, and easy integration with other tools.
- Mage: An open-source data pipeline tool designed for simplicity and ease of use. Mage focuses on creating, running, and managing data pipelines, with a low-code interface that makes it accessible to a broader audience.
- Luigi: An open-source Python-based framework for building complex data pipelines. It is used for defining tasks and dependencies to orchestrate data workflows, with features for monitoring and error handling.
- Flyte: An open-source data and machine learning workflow orchestration platform. It is designed to manage complex workflows involving data processing and machine learning, with a focus on scalability and reproducibility.
What is data orchestration?
Data orchestration is the process of coordinating, integrating, and automating data workflows across different sources and systems to ensure seamless data movement and consistency. It involves managing data pipelines, transformations, and dependencies to deliver accurate and timely data for business insights.
A data orchestration tool is a category under orchestration tools to streamline management tasks by providing features like workflow design, scheduling, monitoring, and error handling. These tools help maintain data quality, reduce manual intervention, and support collaboration among data engineers, analysts, and data scientists.
Modern data stack
The “Modern Data Stack” (MDS) is a cloud-based data management and analysis approach which incorporates key elements of data infrastructure, such as:
Data collection is the first step in the MDS, gathering information from various sources like databases, SaaS applications, and APIs. It requires robust data engineering to ensure that data flows efficiently and reliably into the system, reducing the risk of siloed data.
Data infrastructure refers to the architecture that supports data operations. It includes cloud-based platforms and scalable storage solutions like Snowflake, BigQuery, and Amazon S3, which help centralize data and allow for easy scalability.
Data catalog tools play a crucial role in organizing and documenting datasets, providing a centralized resource for metadata and ensuring easy data discovery. This is key to preventing data silos and promoting collaboration across teams.
Data governance ensures that data is managed according to regulations and best practices. It involves setting policies, standards, and procedures for data use, ensuring compliance, and maintaining data quality. Tools for data observability, like Monte Carlo or Great Expectations, can aid in monitoring data quality and lineage.
Data engineering encompasses the processes and techniques used to prepare data for analysis. This includes data integration, transformation, and orchestration, with tools like Fivetran, dbt, and Apache Airflow. Effective data engineering ensures that data is consistent and ready for use in business intelligence and analytics.
Some of the tools that are utilized in MDS include:
Data orchestration tools connects various components of the MDS, ensuring that data flows seamlessly, is transformed correctly, and is available for analysis in a reliable and automated manner.
Data integration tools that extract, load, and transform data from various sources into a central repository.
Data warehousing tools which are centralized storage solutions to support large-scale data analysis.
Business intelligence (BI) and analytics tools that enable data exploration, visualization, and reporting.
Data Observability tools that can monitor and ensure data quality, lineage, and accuracy.
Data orchestration vs ETL orchestration
Similarities:
– Data Processing: Both ETL and data orchestration involve processing data to make it ready for analysis or other business uses.
– Automation: Both concepts emphasize automating workflows to streamline data management processes and reduce manual intervention.
– Data Integration: They both focus on integrating data from different sources to create a unified view.
Differences:
– Scope: ETL is a specific process involving extracting data from sources, transforming it into a desired format, and loading it into a target system. Data orchestration has a broader scope, covering the coordination and automation of data workflows, which may include ETL processes but can also manage more complex data pipelines.
– Purpose: ETL is designed primarily for data movement and transformation, while data orchestration focuses on orchestrating and managing multiple processes or workflows, which may involve ETL and other tasks like data validation, cleaning, or merging.
– Complexity: Data orchestration can manage complex dependencies and workflows involving multiple data pipelines, while ETL typically handles individual data flows.
– Tools: ETL tools are designed specifically for ETL tasks. Data orchestration tools provide a framework for orchestrating complex workflows, which can include ETL tasks alongside others.
| Aspect | ETL | Data Orchestration | Similarities |
|---|---|---|---|
| Scope | Focuses on extracting data from sources, transforming it, and loading
it into a target system.
| Coordinates and automates multiple data processes, often including ETL but
also other tasks.
| Both involve data workflows and automation. |
| Purpose | Designed to move and transform data. | Ensures seamless coordination of complex workflows, involving multiple processes. | Aimed at providing consistent and reliable data flows. |
| Complexity | Typically handles individual data flows and processes. | Manages complex dependencies and workflows involving multiple data pipelines. | Focus on reducing manual intervention through automation. |
| Tools | Uses specialized ETL tools like Talend, Informatica, etc. | Utilizes orchestration tools like Apache Airflow, Prefect, Dagster, etc. | Both tool types support automation and scheduling. |
| Flexibility | ETL tools are tailored for specific tasks, with less focus
on broader coordination.
| Data orchestration tools offer more flexibility in managing complex workflows
and dependencies.
| Both aim to improve efficiency and scalability in data management. |
| Error Handling | Basic error handling within the scope of data extraction, transformation,
and loading.
| Provides robust error handling, monitoring, and recovery for complex workflows. | Both aim to ensure consistent and accurate data processes. |
Further reading
Explore more on orchestration and automation software that can help manage and orchestrate data:
External sources
- 1. “ETL.” Redwood. Accessed April 24, 2024.
- 2. ” Monitor Visually.” Microsoft. Accessed April 24, 2024.
- 3. ” Prefect Docs.” Prefect. Accessed April 24, 2024.
- 4. ” Keboola Blog.” Keboola. Accessed April 24, 2024.
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