This page contains the setup guide and reference information for the S3 connector.
Configure and schedule S3 metadata workflows from the OpenMetadata UI:
To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. If you want to install it manually in an already existing Airflow host, you can follow this guide.
If you don't want to use the OpenMetadata Ingestion container to configure the workflows via the UI, then you can check the following docs to run the Ingestion Framework in any orchestrator externally.
Run Connectors from the OpenMetadata UILearn how to manage your deployment to run connectors from the UI
Run the Connector ExternallyGet the YAML to run the ingestion externally
External SchedulersGet more information about running the Ingestion Framework Externally
To deploy OpenMetadata, check the Deployment guides.
We need the following permissions in AWS:
For all the buckets that we want to ingest, we need to provide the following:
Note that the
Resources should be all the buckets that you'd like to scan. A possible policy could be:
Which is used to fetch the total size in bytes for a bucket and the total number of files. It requires:
The policy would look like:
In any other connector, extracting metadata happens automatically. In this case, we will be able to extract high-level metadata from buckets, but in order to understand their internal structure we need users to provide an
openmetadata.json file at the bucket root.
You can learn more about this here. Keep reading for an example on the shape of the manifest file.
Our manifest file is defined as a JSON Schema, and can look like this:
Entries: We need to add a list of
entries. Each inner JSON structure will be ingested as a child container of the top-level one. In this case, we will be ingesting 4 children.
Simple Container: The simplest container we can have would be structured, but without partitions. Note that we still need to bring information about:
- dataPath: Where we can find the data. This should be a path relative to the top-level container.
- structureFormat: What is the format of the data we are going to find. This information will be used to read the data.
- separator: Optionally, for delimiter-separated formats such as CSV, you can specify the separator to use when reading the file. If you don't, we will use
,for CSV and
/tfor TSV files.
After ingesting this container, we will bring in the schema of the data in the
Partitioned Container: We can ingest partitioned data without bringing in any further details.
By informing the
isPartitioned field as
true, we'll flag the container as
Partitioned. We will be reading the source files schemas', but won't add any other information.
Single-Partition Container: We can bring partition information by specifying the
partitionColumns. Their definition is based on the JSON Schema definition for table columns. The minimum required information is the
partitionColumns, these values will be added to the schema, on top of the inferred information from the files.
Multiple-Partition Container: We can add multiple columns as partitions.
Note how in the example we even bring our custom
displayName for the column
dataTypeDisplay for its type.
Again, this information will be added on top of the inferred schema from the data files.
You can also manage a single manifest file to centralize the ingestion process for any container. In that case, you will need to add a
containerName entry to the structure above. For example:
You can also keep local manifests in each container, but if possible, we will always try to pick up the global manifest during the ingestion.
We will look for a file named
The first step is ingesting the metadata from your sources. Under Settings, you will find a Services link an external source system to OpenMetadata. Once a service is created, it can be used to configure metadata, usage, and profiler workflows.
To visit the Services page, select Services from the Settings menu.
Find Dashboard option on left panel of the settings page
Add a new Service from the Storage Services page
Select your service from the list
Provide a Name and description for your Service
In this step, we will configure the connection settings required for this connector. Please follow the instructions below to ensure that you've configured the connector to read from your S3 service as desired.
Configure the service connection by filling the form
- AWS Access Key ID & AWS Secret Access Key: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and authorize your requests (docs).
Access keys consist of two parts: An access key ID (for example,
AKIAIOSFODNN7EXAMPLE), and a secret access key (for example,
You must use both the access key ID and secret access key together to authenticate your requests.
You can find further information on how to manage your access keys here.
- AWS Region: Each AWS Region is a separate geographic area in which AWS clusters data centers (docs).
As AWS can have instances in multiple regions, we need to know the region the service you want reach belongs to.
Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the services programmatically, there are different ways in which we can extract and use the rest of AWS configurations.
You can find further information about configuring your credentials here.
- AWS Session Token (optional): If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID and AWS Secrets Access Key. Also, these will include an AWS Session Token.
You can find more information on Using temporary credentials with AWS resources.
- Endpoint URL (optional): To connect programmatically to an AWS service, you use an endpoint. An endpoint is the URL of the entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically use the default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests.
Find more information on AWS service endpoints.
- Profile Name: A named profile is a collection of settings and credentials that you can apply to a AWS CLI command. When you specify a profile to run a command, the settings and credentials are used to run that command. Multiple named profiles can be stored in the config and credentials files.
You can inform this field if you'd like to use a profile other than
Find here more information about Named profiles for the AWS CLI.
- Assume Role Arn: Typically, you use
AssumeRolewithin your account or for cross-account access. In this field you'll set the
ARN(Amazon Resource Name) of the policy of the other account.
A user who wants to access a role in a different account must also have permissions that are delegated from the account administrator. The administrator must attach a policy that allows the user to call
AssumeRole for the
ARN of the role in the other account.
This is a required field if you'd like to
Find more information on AssumeRole.
- Assume Role Session Name: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role is assumed by different principals or for different reasons.
By default, we'll use the name
Find more information about the Role Session Name.
- Assume Role Source Identity: The source identity specified by the principal that is calling the
AssumeRoleoperation. You can use source identity information in AWS CloudTrail logs to determine who took actions with a role.
Find more information about Source Identity.
Once the credentials have been added, click on Test Connection and Save the changes.
Test the connection and save the Service
In this step we will configure the metadata ingestion pipeline, Please follow the instructions below
Configure Metadata Ingestion Page
- Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
- Container Filter Pattern (Optional): To control whether to include a container as part of metadata ingestion.
- Include: Explicitly include containers by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all containers with names matching one or more of the supplied regular expressions. All other containers will be excluded.
- Exclude: Explicitly exclude containers by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all containers with names matching one or more of the supplied regular expressions. All other containers will be included.
- Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug.
- Storage Metadata Config Source: Here you can specify the location of your global manifest
openmetadata_storage_manifest.jsonfile. It can be located in S3, a local path or HTTP.
Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.
Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.
If something doesn't look right, click the Back button to return to the appropriate step and change the settings as needed.
After configuring the workflow, you can click on Deploy to create the pipeline.
Schedule the Ingestion Pipeline and Deploy
If there were any errors during the workflow deployment process, the Ingestion Pipeline Entity will still be created, but no workflow will be present in the Ingestion container.
- You can then Edit the Ingestion Pipeline and Deploy it again.
- From the Connection tab, you can also Edit the Service if needed.