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This page contains the setup guide and reference information for the S3 connector.

Configure and schedule S3 metadata workflows from the CLI:

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, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

OpenMetadata 1.0 or later

To deploy OpenMetadata, check the Deployment guides.

To run the metadata ingestion, we need the following permissions in AWS:

For all the buckets that we want to ingest, we need to provide the following:

  • s3:ListBucket
  • s3:GetObject
  • s3:GetBucketLocation
  • s3:ListAllMyBuckets

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:

  • cloudwatch:GetMetricData
  • cloudwatch:ListMetrics

The policy would look like:

To run the Athena ingestion, you will need to install:

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 /t for TSV files.

After ingesting this container, we will bring in the schema of the data in the dataPath.

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 name and dataType.

When passing 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 openmetadata_storage_manifest.json.

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Athena.

In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following JSON Schema

This is a sample config for Athena:

  • awsAccessKeyId & awsSecretAccessKey: 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, wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY).

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.

awsSessionToken: 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.

awsRegion: 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.

endPointURL: 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.

profileName: 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 default.

Find here more information about Named profiles for the AWS CLI.

assumeRoleArn: Typically, you use AssumeRole within 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 AssumeRole.

Find more information on AssumeRole.

assumeRoleSessionName: 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 OpenMetadataSession.

Find more information about the Role Session Name.

assumeRoleSourceIdentity: The source identity specified by the principal that is calling the AssumeRole operation. You can use source identity information in AWS CloudTrail logs to determine who took actions with a role.

Find more information about Source Identity.

The sourceConfig is defined here:

containerFilterPattern: Note that the filter supports regex as include or exclude. You can find examples here.

storageMetadataConfigSource: Path to the openmetadata_storage_manifest.json global manifest file. It can be located in S3, a local path or as a URL to the file.

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

Logger Level

You can specify the loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with DEBUG will give you far more traces for identifying issues.

JWT Token

JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.

Store Service Connection

If set to true (default), we will store the sensitive information either encrypted via the Fernet Key in the database or externally, if you have configured any Secrets Manager.

If set to false, the service will be created, but the service connection information will only be used by the Ingestion Framework at runtime, and won't be sent to the OpenMetadata server.

Store Service Connection

If set to true (default), we will store the sensitive information either encrypted via the Fernet Key in the database or externally, if you have configured any Secrets Manager.

If set to false, the service will be created, but the service connection information will only be used by the Ingestion Framework at runtime, and won't be sent to the OpenMetadata server.

SSL Configuration

If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set verifySSL to ignore, or have it as validate, which will require you to set the sslConfig.certificatePath with a local path where your ingestion runs that points to the server certificate file.

Find more information on how to troubleshoot SSL issues here.

Connection Options (Optional): Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.

Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs.


First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.