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Storage Services

This is the supported list of connectors for Storage Services:
https://mintcdn.com/openmetadata/bSeSGuVjATgAasqS/public/images/connectors/gcs.webp?fit=max&auto=format&n=bSeSGuVjATgAasqS&q=85&s=33a668655bbdebd4a4fc82c4c912334f

GCS

prod
PROD
https://mintcdn.com/openmetadata/TuW6c379o6GhuCGi/public/images/connectors/amazon-s3.webp?fit=max&auto=format&n=TuW6c379o6GhuCGi&q=85&s=26c10c00ceb2e5e8e75bfe12cf15bf9d

S3 Storage

prod
PROD
If you have a request for a new connector, don’t hesitate to reach out in Slack or open a feature request in our GitHub repo.

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Configuring the Ingestion

In any other connector, extracting metadata happens automatically. We have different ways to understand the information in the sources and send that to OpenMetadata. However, what happens with generic sources such as S3 buckets, or ADLS containers? In these systems we can have different types of information:
  • Unstructured data, such as images or videos,
  • Structured data in single and independent files (which can also be ingested with the S3 Data Lake connector)
  • Structured data in partitioned files, e.g., my_table/year=2022/...parquet, my_table/year=2023/...parquet, etc.
The Storage Connector will help you bring in Structured data in partitioned files.
Then the question is, how do we know which data in each Container is relevant and which structure does it follow? In order to optimize ingestion costs and make sure we are only bringing in useful metadata, the Storage Services ingestion process follow this approach:
  1. We list the top-level containers (e.g., S3 buckets), and bring generic insights, such as size and number of objects.
  2. If there is an openmetadata.json manifest file present in the bucket root, we will ingest the informed paths as children of the top-level container. Let’s see how that works.
Note that the current implementation brings each entry in the openmetadata.json as a child container of the top-level container. Even if your data path is s3://bucket/my/deep/table, we will bring bucket as the top-level container and my/deep/table as its child.We are flattening this structure to simplify the navigation.

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OpenMetadata Manifest

Our manifest file is defined as a JSON Schema, and can look like this:

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Global Manifest

You can also manage a single manifest file to centralize the ingestion process for any container, named openmetadata_storage_manifest.json. You can also keep local manifests openmetadata.json in each container, but if possible, we will always try to pick up the global manifest during the ingestion.

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Example

Let’s show an example on how the data process and metadata look like. We will work with S3, using a global manifest, and two buckets.

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S3 Data

In S3 we have: 
S3
|__ om-glue-test  # bucket
|    |__ openmetadata_storage_manifest.json  # Global Manifest
|__ openmetadata-demo-storage  # bucket
     |__ cities_multiple_simple/
     |    |__ 20230412/
     |         |__ State=AL/  # Directory with parquet files
     |         |__ State=AZ/  # Directory with parquet files
     |__ cities_multiple/
     |    |__ Year=2023/
     |         |__ State=AL/  # Directory with parquet files
     |         |__ State=AZ/  # Directory with parquet files
     |__ cities/
     |    |__ State=AL/  # Directory with parquet files
     |    |__ State=AZ/  # Directory with parquet files
     |__ transactions_separator/  # Directory with CSV files using ;
     |__ transactions/  # Directory with CSV files using ,
  1. We have a bucket om-glue-test where our openmetadata_storage_manifest.json global manifest lives.
  2. We have another bucket openmetadata-demo-storage where we want to ingest the metadata of 5 partitioned containers with different formats
    1. The cities_multiple_simple container has a time partition (formatting just a date) and a State partition.
    2. The cities_multiple container has a Year and a State partition.
    3. The cities container is only partitioned by State.
    4. The transactions_separator container contains multiple CSV files separated by ;.
    5. The transactions container contains multiple CSV files separated by ,.
The ingestion process will pick up a random sample of files from the directories (or subdirectories).

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Global Manifest

Our global manifest looks like follows: 
{
    "entries":[
        {
            "dataPath": "transactions",
            "structureFormat": "csv",
            "isPartitioned": false,
            "containerName": "openmetadata-demo-storage"
        },
        {
            "dataPath": "solution.pdf",
        },
        {
            "dataPath": "transactions_separator",
            "structureFormat": "csv",
            "isPartitioned": false,
            "separator": ";",
            "containerName": "openmetadata-demo-storage"
        },
        {
            "dataPath": "cities",
            "structureFormat": "parquet",
            "isPartitioned": true,
            "containerName": "openmetadata-demo-storage"
        },
        {
            "dataPath": "cities_multiple",
            "structureFormat": "parquet",
            "isPartitioned": true,
            "containerName": "openmetadata-demo-storage",
            "partitionColumns": [
                {
                    "name": "Year",
                    "dataType": "DATE",
                    "dataTypeDisplay": "date (year)"
                },
                {
                    "name": "State",
                    "dataType": "STRING"
                }
            ]
        },
        {
            "dataPath": "cities_multiple_simple",
            "structureFormat": "parquet",
            "isPartitioned": true,
            "containerName": "openmetadata-demo-storage",
            "partitionColumns": [
                {
                    "name": "State",
                    "dataType": "STRING"
                }
            ]
        }
    ]
}
We are specifying:
  1. Where to find the data for each container we want to ingest via the dataPath,
  2. The format,
  3. Indication if the data has sub partitions or not (e.g., State or Year),
  4. The containerName, so that the process knows in which S3 bucket to look for this data.

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Source Config

In order to prepare the ingestion, we will:
  1. Set the sourceConfig to include only the containers we are interested in.
  2. Set the storageMetadataConfigSource pointing to the global manifest stored in S3, specifying the container name as om-glue-test.
source:
   type: s3
   serviceName: s3-demo
   serviceConnection:
      config:
         type: S3
         awsConfig:
            awsAccessKeyId: ...
            awsSecretAccessKey: ...
            awsRegion: ...
   sourceConfig:
      config:
         type: StorageMetadata
         containerFilterPattern:
            includes:
               - openmetadata-demo-storage
               - om-glue-test
         storageMetadataConfigSource:
            securityConfig:
               awsAccessKeyId: ...
               awsSecretAccessKey: ...
               awsRegion: ...
            prefixConfig:
               containerName: om-glue-test
sink:
   type: metadata-rest
   config: {}
workflowConfig:
   openMetadataServerConfig:
      hostPort: http://localhost:8585/api
      authProvider: openmetadata
      securityConfig:
         jwtToken: "..."
You can run this same process from the UI, or directly with the metadata CLI via metadata ingest -c <path to yaml>.

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Checking the results

Once the ingestion process runs, we’ll see the following metadata: First, the service we called s3-demo, which has the two buckets we included in the filter. s3-demo Then, if we click on the openmetadata-demo-storage container, we’ll see all the children defined in the manifest. s3-demo
  • cities: Will show the columns extracted from the sampled parquet files, since there is no partition columns specified.
  • cities_multiple: Will have the parquet columns and the Year and State columns indicated in the partitions.
  • cities_multiple_simple: Will have the parquet columns and the State column indicated in the partition.
  • transactions and transactions_separator: Will have the CSV columns.