What is a Data Orchestrator

A data orchestrator is a software platform that helps automate, monitor, and manage ETL (extract, transform, load) processes and data pipelines orchestrating the flow of data between databases, warehouses, lakes and other systems.

Data orchestrators provide centralized data integration by coordinating tasks and data across disparate sources, pipelines, formats and systems. Examples include Apache Airflow, Kubeflow Pipelines, Azure Data Factory. Data orchestrators are commonly used with data warehouses and data processing engines.

What does it do/how does it work?

A data orchestrator enables defining data pipelines as reusable templates that can be automatically executed on schedules or triggers. It handles workflow orchestration, scheduling, monitoring, and managing pipelines.

The orchestrator tracks metadata and lineage, provides APIs to monitor pipeline health, leverages scaling and fault tolerance capabilities of underlying data processing engines. It simplifies building robust data integration workflows.

Why is it important? Where is it used?

Data orchestrators streamline building resilient, reusable data pipelines for use cases like data ingestion, ETL, machine learning, streaming analytics.

They help structure workflows from disparate data sources and processing systems into reliable data pipelines. This powers key applications spanning business analytics, data science, IoT, marketing and more across industries.

FAQ

How is a data orchestrator different from a processing engine?

While data processing engines focus on data transformations, orchestrators enable coordinating pipelines across systems and handle workflow orchestration, scheduling, monitoring.

• Workflow orchestration, scheduling pipelines.
• Visual pipeline authoring and monitoring.
• Metadata, lineage, and artifact tracking.
• Integration of multiple processing engines like Spark, Kafka.

When should you use a data orchestrator?

Data orchestrators help streamline building managed data pipelines, ideal for:

• Coordinating complex ETL across siloed data sources and warehouses.
• Productionizing machine learning workflows with versioning and monitoring.
• Building resilient real-time data integration pipelines.
• Managing incremental data processing tasks.

What are examples of popular data orchestrators?

Some widely used pipeline orchestration frameworks:

• Apache Airflow - Open source platform to programmatically author, schedule and monitor data pipelines.
• Azure Data Factory - Cloud data integration service to orchestrate data movement and transformation.
• Kubeflow Pipelines - Machine learning toolkit to build and deploy portable ML workflows on Kubernetes.
• Prefect - Modern open source workflow orchestration framework in Python.

What are key challenges with data orchestrators?

However, data orchestrators also come with complexities around monitoring, reuse, and DevOps:

• Visualizing pipeline health and bottlenecks.
• Making workflows reusable and parameterized.
• Versioning and managing pipeline configurations and dependencies.
• Handling role-based access control and security.
• Monitoring and alerting for production pipelines.

What are examples of data orchestrators?

• Apache Airflow
• Dagster
• Azure Data Factory
• Kubeflow Pipelines
• Prefect Cloud

References

• [Book] Managing Data Orchestration and Integration at Scale, O'Reilly Media, Inc.
• [Post] Data Orchestration — A Primer
• [Post] The What, Why, And When of Data Orchestration