What is data orchestration?

Data orchestration is the management and coordination of data flows across different systems, processes and tools. It helps organize and streamline data pipeline stages, including data collection, ingestion, transformation, integration and storage.

Through a successful data orchestration process, information flows reliably and efficiently to various target destinations—and is ready for data analysis and other uses upon arrival. These core capabilities make it a critical data management practice in the era of big data workloads and data-driven decision-making. 

Data engineers rely on data orchestration tools and orchestration platforms to streamline data movement and support the scalability of enterprise data initiatives. Automation is central to many modern data orchestration solutions. It enables data tasks such as data integration and transformation to run in a logical order without human intervention. 

To harness the power of their growing data volumes, businesses must navigate increasingly complex data ecosystems. Their data often originates from different sources and in varying data formats. 

It is also commonly stored across both cloud-based and on-premises repositories, such as data lakes and data warehouses, around the world. And in many organizations, data is used in different tools by different teams and employees—CRM systems for sales teams, analytics platforms for marketers, and so on. According to a 2024 IDC survey of IT and line of business leaders, operational data is sourced from 35 different systems and integrated into 18 different analytical data repositories, on average.¹

Such complicated data environments are prone to data silos, low-quality data and other issues that create bottlenecks in data pipelines and introduce errors into downstream analysis. Effective data orchestration can help enterprises overcome these challenges and unlock value from their data.

Data orchestration helps enterprises use their data for valuable insights, informed decision-making and innovation. Specific benefits include:

Data orchestration and data integration are closely related but not identical concepts. While both enable the consolidation and unification of data for analytics use cases, data integration is more granular while data orchestration is an overarching practice. 

Data orchestration optimizes data movement through different systems and processes. Data integration is one of those processes, which uses different methods (such as extract, transform and load, or ETL) to combine and harmonize data from different sources and then load it into a target system.

Data orchestration helps organizations tackle the enormous complexity of their data ecosystems. The practice itself is commonly broken down into three basic steps:

• Organization: Data is collected from a range of internal and external sources and then organized—often in a central location—so that it’s ready for transformation.
  
  
• Transformation: Raw data is converted into a unified format, cleaned and validated to confirm consistency and accuracy.
  
  
• Activation: Data is made available for analysis, routing to dashboard tools and other purposes.

Underlying the basic steps of data orchestration are several key functions. Among them:  

• Defining task dependencies and sequencing tasks
• Automating data workflows
• Monitoring and sending alerts

Data orchestration often begins with defining data processing tasks and specifying their order of execution in data pipelines and workflows. It helps ensure that when one task depends on the outcome of another task, the latter is completed first. Such sequencing of tasks—that is, one based on dependencies—helps organizations avoid costly pipeline failures.

To design and organize task sequences, data engineers often use directed acyclic graphs, or DAGs—graphs in which nodes are linked by one-way connections that do not form any cycles. Different nodes in a DAG can represent different data processes, such as data ingestion and data transformation, and the sequence in which they should be performed. The edges connecting nodes represent the dependencies between the processes.

An alternative to DAGs in defining and ordering tasks is a code-centric approach. A popular code-centric approach uses the open-source programming language Python to create functions for workflow management—a set-up often considered better for accommodating dynamic workflows.

Modern data orchestration automates multiple data workflows—such as ETL, ELT (extract, load, transform) and data transformation within data warehouses—to ensure consistency and minimize or eliminate human intervention. A person can initiate an automated data task, but tasks can also be scheduled through three types of triggers:²

• Time-based triggers: Tasks run at prescribed intervals or times.
  
  
• Dependency-based triggers: Tasks run only after other specified tasks are completed.
  
  
• Event-driven triggers: Real-world signals, such as API calls, activate a task.

While monitoring data pipelines is often considered a data observability practice, it also plays a role in data orchestration by helping ensure that data flows and is processed as intended. 

Organizations can monitor several types of metrics, including performance metrics such as latency and throughput; resource utilization metrics such as CPU and memory usage, and data quality metrics such as accuracy, completeness and consistency.³

When a data pipeline problem is detected, such as a task failure, notification tools can send timely alerts to data teams so they can address the issue quickly. Orchestration solutions may also allow retries to mitigate issues—that is, a failed task may be rerun automatically a specified number of times—before notifications are delivered.

Data orchestration is similar but notably distinct from two other types of orchestration: workflow orchestration and process orchestration. Both of these practices are broader than data orchestration, and data orchestration can be considered a type of both.
 
Workflow orchestration focuses on coordinating and managing a series of interconnected tasks, systems and tools to achieve a specific outcome. It emphasizes the end-to-end execution and integration of workflows across different environments, helping tasks occur in the correct order while meeting dependencies.

Process orchestration refers to managing and integrating multiple business processes, often involving workflows, people and systems. Instead of focusing on workflow management, it entails the end-to-end coordination of entire business processes, promoting alignment with organizational goals.

Organizations and data teams can choose among many different data orchestration solutions as they seek to streamline the way they process data. The best solution for an organization will depend on its specific priorities, such as costs (open source vs. commercial); observability needs; and integrations with other popular data solutions (analytics tools such as dbt, cloud-based data platforms such as Snowflake).

The most widely used data orchestration tools and platforms typically offer options for connecting to other data solutions, but they vary in other ways. Below is a closer look at several data orchestration solutions:

• Apache Airflow
• AWS Step Functions
• Azure Data Factory
• Dagster
• IBM DataOps platforms
• Prefect

The most well-known data orchestration solution, Apache Airflow is an open-source platform designed mainly for batch processing. It enables data workflow scheduling, with workflows defined as DAGs. Airflow features an architecture that supports scaling and parallel execution, making it suitable for managing complex, data-intensive pipelines.

AWS Step Functions is a serverless orchestration service from Amazon featuring a visual interface for coordinating distributed applications and microservices. It’s often recommended for organizations already relying on Amazon infrastructure, but it can also integrate with third-party applications.

Azure Data Factory, from Microsoft, is a fully managed, serverless data integration service that integrates natively with other Azure services. It features a visual user interface for integrating data sources and ETL and ELT data pipeline orchestration.

Dagster is known for its focus on observability and data quality, with capabilities such as data lineage and metadata tracking. Its features also include local testing and reusable components to support AI-ready data products and modern software engineering practices. 

®IBM offers a selection of DataOps tools and platforms featuring data orchestration capabilities. IBM® watsonx.data intelligence provides a data catalog to automate data discovery and data quality management. IBM® watsonx.data integration offers a unified control plane for building reusable pipelines. And IBM Cloud Pak for Data uses data virtualization, pipelines and connectors to combine data from siloed sources, while eliminating the need for physical data movement.

Prefect is a data orchestration tool that comes in an open-source version and cloud-managed solution with additional features for enterprises. Unlike other data orchestration solutions, Prefect does not rely on DAGs and instead takes a code-centric approach, which some prefer for more dynamic orchestration.