Amazon DataZone introduces OpenLineage-compatible data lineage visualization in preview | AWS Big Data Blog

Feb 25, 2025

We are excited to announce the preview of API-driven, OpenLineage-compatible data lineage in Amazon DataZone to help you capture, store, and visualize lineage of data movement and transformations of data assets on Amazon DataZone.

With the Amazon DataZone OpenLineage-compatible API, domain administrators and data producers can capture and store lineage events beyond what is available in Amazon DataZone, including transformations in Amazon Simple Storage Service (Amazon S3), AWS Glue, and other AWS services. This provides a comprehensive view for data consumers browsing in Amazon DataZone, who can gain confidence of an asset’s origin, and data producers, who can assess the impact of changes to an asset by understanding its usage.

In this post, we discuss the latest features of data lineage in Amazon DataZone, its compatibility with OpenLineage, and how to get started capturing lineage from other services such as AWS Glue, Amazon Redshift, and Amazon Managed Workflows for Apache Airflow (Amazon MWAA) into Amazon DataZone through the API.

Data lineage gives you an overarching view into data assets, allowing you to see the origin of objects and their chain of connections. Data lineage enables tracking the movement of data over time, providing a clear understanding of where the data originated, how it has changed, and its ultimate destination within the data pipeline. With transparency around data origination, data consumers gain trust that the data is correct for their use case. Data lineage information is captured at levels such as tables, columns, and jobs, allowing you to conduct impact analysis and respond to data issues because, for example, you can see how one field impacts downstream sources. This equips you to make well-informed decisions before committing changes and avoid unwanted changes downstream.

Data lineage in Amazon DataZone is an API-driven, OpenLineage-compatible feature that helps you capture and visualize lineage events from OpenLineage-enabled systems or through an API, to trace data origins, track transformations, and view cross-organizational data consumption. The lineage visualized includes activities inside the Amazon DataZone business data catalog. Lineage captures the assets cataloged as well as the subscribers to those assets and to activities that happen outside the business data catalog captured programmatically using the API.

Additionally, Amazon DataZone versions lineage with each event, enabling you to visualize lineage at any point in time or compare transformations across an asset’s or job’s history. This historical lineage provides a deeper understanding of how data has evolved, which is essential for troubleshooting, auditing, and enforcing the integrity of data assets.

The following screenshot shows an example lineage graph visualized with the Amazon DataZone data catalog.

The need to capture data lineage consistently across various analytical services and combine them into a unified object model is key in uncovering insights from the lineage artifact. OpenLineage is an open source project that offers a framework to collect and analyze lineage. It also offers reference implementation of an object model to persist metadata along with integration to major data and analytics tools.

The following are key concepts in OpenLineage:

The Amazon DataZone data lineage API is OpenLineage compatible and extends OpenLineage’s functionality by providing a materialization endpoint to persist the lineage outputs in an extensible object model. OpenLineage offers integrations for certain sources, and integration of these sources with Amazon DataZone is straightforward because the Amazon DataZone data lineage API understands the format and translates to the lineage data model.

The following diagram illustrates an example of the Amazon DataZone lineage data model.

In Amazon DataZone, every lineage node represents an underlying resource—there is a 1:1 mapping of the lineage node with a logical or physical resource such as table, view, or asset. The nodes represent a specific job with a specific run, or a node for a table or asset, and one node for a subscription target.

Each version of a node captures what happened to the underlying resource at that specific timestamp. In Amazon DataZone, lineage not only shares the story of data movement outside it, but it also represents the lineage of activities inside Amazon DataZone, such as asset creation, curation, publishing, and subscription.

To hydrate the lineage model in Amazon DataZone, two types of lineage are captured:

There are two different types of lineage nodes available in Amazon DataZone:

Amazon DataZone offers a comprehensive experience for data producers and consumers. The asset details page provides a graphical representation of lineage, making it straightforward to visualize data relationships upstream or downstream. The asset details page provides the following capabilities to navigate the graph:

The following screenshot shows an example of data lineage visualization.

You can experience the visualization with sample data by choosing Preview on the Lineage tab and choosing the Try sample lineage link. This opens a new browser tab with sample data to test and learn about the feature with or without a guided tour, as shown in the following screenshot.

Now that we understand the capabilities of the new data lineage feature in Amazon DataZone, let’s explore how you can get started in capturing lineage from AWS Glue tables and ETL (extract, transform, and load) jobs, Amazon Redshift, and Amazon MWAA.

The getting started scripts are also available in Amazon DataZone’s new GitHub repository.

For this walkthrough, you should have the following prerequisites:

If the AWS account you use to follow this post uses AWS Lake Formation to manage permissions on the AWS Glue Data Catalog, make sure that you log in as a user with access to create databases and tables. For more information, refer to Implicit Lake Formation permissions.

To create your resources for this use case using AWS CloudFormation, complete the following steps:

Wait for the stack formation to finish provisioning the resources. When you see the CREATE_COMPLETE status, you can proceed to the next steps.

For this example, we use CloudShell, which is a browser-based shell, to run the commands necessary to harvest lineage metadata from AWS Glue tables. Complete the following steps:

When the crawler is complete, you’ll see a Succeeded status.

Now let’s harvest the lineage metadata using CloudShell.

You should get the following results.

You should receive a notification indicating that the script ran successfully.

After you capture the lineage information from the Inventory table, complete the following steps to run the data source.

For this example, we had a data source job called SalesDLDataSourceV2 already created pointing to the awsome_retail_db database. To learn more about how to create data source jobs, refer to Create and run an Amazon DataZone data source for the AWS Glue Data Catalog.

After the job runs successfully, you should see a confirmation message.

Now let’s view the lineage diagram generated by Amazon DataZone.

On the Lineage tab, you can see that Amazon DataZone created three nodes:

If you choose the Dataset node, Amazon DataZone offers information about the S3 object used to create the asset.

In the previous section, we covered how to generate a data lineage diagram on top of a data asset. Now let’s see how we can create one for an AWS Glue job.

The CloudFormation template that we launched earlier created an AWS Glue job called Inventory_Insights. This job gets data from the Inventory table and creates a new table called Inventory_Insights with the aggregated data of the total products available in all the stores.

The CloudFormation template also copied the openlineage-spark_2.12-1.9.1.jar file to the S3 bucket created for this post. This file is necessary to generate lineage metadata from the AWS Glue job. We use version 1.9.1, which is compatible with AWS Glue 3.0, the version used to create the AWS Glue job for this post. If you’re using a different version of AWS Glue, you need to download the corresponding OpenLineage Spark plugin file that matches your AWS Glue version.

The OpenLineage Spark plugin is not able to extract data lineage from AWS Glue Spark jobs that use AWS Glue DynamicFrames. Use Spark SQL DataFrames instead.

You will see the following message; this means that the script is ready to get the AWS Glue job lineage metadata after you run it.

Now let’s run the AWS Glue job created by the Cloud formation template.

On the Job details tab, you will notice that the job has the following configuration:

During the run of the job, you will see the following output on the CloudShell console.

This means that the script has successfully harvested the lineage metadata from the AWS Glue job.

Now let’s create an AWS Glue table based on the data created by the AWS Glue job. For this example, we use an AWS Glue crawler.

When the crawler is complete, you will see the following message.

Now let’s open the Amazon DataZone portal to see how the diagram is represented in Amazon DataZone.

On the Lineage tab, you can see the diagram created by Amazon DataZone. It shows three nodes:

Now you can see the full lineage diagram for the Inventory_insights table.

You can see the evolution of the columns and the transformations that they had.

When you choose any of the nodes that are part of the diagram, you can see more details. For example, the inventory_insights node shows the following information.

Let’s explore how to generate a lineage diagram from Amazon Redshift. In this example, we use AWS Cloud9 because it allows us to configure the connection to the virtual private cloud (VPC) where our Redshift cluster resides. For more information about AWS Cloud9, refer to the AWS Cloud9 User Guide.

The CloudFormation template included as part of this post doesn’t cover the creation of a Redshift cluster or the creation of the tables used in this section. To learn more about how to create a Redshift cluster, see Step 1: Create a sample Amazon Redshift cluster. We use the following query to create the tables needed for this section of the post:

Remember to add the IP address of your AWS Cloud9 environment to the security group with access to the Redshift cluster.

You should be able to see the following messages.

If the configuration was done correctly, you will see the following confirmation message.

Now let’s see how the diagram was created in Amazon DataZone.

For this post, we already created a data source job called Sales_DW_Enviroment-default-datasource to add the Redshift data source to our Amazon DataZone project. To learn how to create a data source job, refer to Create and run an Amazon DataZone data source for Amazon Redshift

After you run the job, you’ll see the following confirmation message.

Amazon DataZone create a three-node lineage diagram for the total sales table; you can choose any node to view its details.

The Job Info section shows the query that was used to create the total sales table.

Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Amazon MWAA is a managed service for Airflow that lets you use your current Airflow platform to orchestrate your workflows. OpenLineage supports integration with Airflow 2.6.3 using the openlineage-airflow package, and the same can be enabled on Amazon MWAA as a plugin. Once enabled, the plugin converts Airflow metadata to OpenLineage events, which are consumable by DataZone.PostLineageEvent.

The following diagram shows the setup required in Amazon MWAA to capture data lineage using OpenLineage and publish it to Amazon DataZone.

The workflow uses an Amazon MWAA DAG to invoke a data pipeline. The process is as follows:

The example used in the post uses Amazon MWAA version 2.6.3 and OpenLineage plugin version 1.4.1. For other Airflow versions supported by OpenLineage, refer to Supported Airflow versions.

When harvesting lineage using OpenLineage, a Transport configuration needs to be set up, which tells OpenLineage where to emit the events to, for example the console or an HTTP endpoint. You can use ConsoleTransport, which logs the OpenLineage events in the Amazon MWAA task CloudWatch log group, which can then be published to Amazon DataZone using a helper function.

Specify the following in the requirements.txt file added to the S3 bucket configured for Amazon MWAA:

openlineage-airflow==1.4.1

In the Airflow logging configuration section under the MWAA configuration for the Airflow environment, enable Airflow task logs with log level INFO. The following screenshot shows a sample configuration.

A successful configuration will add a plugin to Airflow, which can be verified from the Airflow UI by choosing Plugins on the Admin menu.

In this post, we use a sample DAG to hydrate data to Redshift tables. The following screenshot shows the DAG in graph view.

Run the DAG and upon successful completion of a run, open the Amazon MWAA task CloudWatch log group for your Airflow environment (airflow-env_name-task) and filter based on the expression console.py to select events emitted by OpenLineage. The following screenshot shows the results.

Now that you have the lineage events emitted to CloudWatch, the next step is to publish them to Amazon DataZone to associate them to a data asset and visualize them on the business data catalog.

The function extract_airflow_lineage.py filters the lineage events from the Amazon MWAA task log group and publishes the lineage to the specified domain within Amazon DataZone.

After the lineage is published to DataZone, open your DataZone project, navigate to the Data tab and chose a data asset that was accessed by the Amazon MWAA DAG. In this case, it is a subscribed asset.

Navigate to the Lineage tab to visualize the lineage published to Amazon DataZone.

Choose a node to look at additional lineage metadata. In the following screenshot, we can observe the producer of the lineage has been marked as airflow.

In this post, we shared the preview feature of data lineage in Amazon DataZone, how it works, and how you can capture lineage events, from AWS Glue, Amazon Redshift, and Amazon MWAA, to be visualized as part of the asset browsing experience.

To learn more about Amazon DataZone and how to get started, refer to the Getting started guide. Check out the YouTube playlist for some of the latest demos of Amazon DataZone and short descriptions of the capabilities available.

Leonardo Gomez is a Principal Analytics Specialist at AWS, with over a decade of experience in data management. Specializing in data governance, he assists customers worldwide in maximizing their data’s potential while promoting data democratization. Connect with him on LinkedIn.

Priya Tiruthani is a Senior Technical Product Manager with Amazon DataZone at AWS. She focuses on improving data discovery and curation required for data analytics. She is passionate about building innovative products to simplify customers’ end-to-end data journey, especially around data governance and analytics. Outside of work, she enjoys being outdoors to hike, capture nature’s beauty, and recently play pickleball.

Ron Kyker is a Principal Engineer with Amazon DataZone at AWS, where he helps drive innovation, solve complex problems, and set the bar for engineering excellence for his team. Outside of work, he enjoys board gaming with friends and family, movies, and wine tasting.

Srinivasan Kuppusamy is a Senior Cloud Architect – Data at AWS ProServe, where he helps customers solve their business problems using the power of AWS Cloud technology. His areas of interests are data and analytics, data governance, and AI/ML.

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