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How to configure a Validation Result store in Amazon S3

By default, Validation results are stored in JSON format in the uncommitted/validations/ subdirectory of your great_expectations/ folder. Since Validations may include examples of data (which could be sensitive or regulated) they should not be committed to a source control system. This guide will help you configure a new storage location for Validations in Amazon S3.

Prerequisites: This how-to guide assumes you have:

Steps#

  1. Configure boto3 to connect to the Amazon S3 bucket where Validation results will be stored.

    Instructions on how to set up boto3 with AWS can be found at boto3's documentation site.

  2. Identify your Data Context Validations Store

    Look for the following section in your Data Context's great_expectations.yml file:

    validations_store_name: validations_store
    stores:   validations_store:       class_name: ValidationsStore       store_backend:           class_name: TupleFilesystemStoreBackend           base_directory: uncommitted/validations/

    The configuration file tells Great Expectations to look for Validations in a store called validations_store. It also creates a ValidationsStore called validations_store that is backed by a Filesystem and will store validations under the base_directory uncommitted/validations (the default).

  3. Update your configuration file to include a new store for Validation results on S3.

    In the example below, the new store's name is set to validations_S3_store, but it can be any name you like. We also need to make some changes to the store_backend settings. The class_name will be set to TupleS3StoreBackend, bucket will be set to the address of your S3 bucket, and prefix will be set to the folder in your S3 bucket where Validation results will be located.

    caution

    If you are also storing Expectations in S3 (How to configure an Expectation store to use Amazon S3), or DataDocs in S3 (How to host and share Data Docs on Amazon S3), then please ensure that the prefix values are disjoint and one is not a substring of the other.

    validations_store_name: validations_S3_store
    stores:   validations_S3_store:       class_name: ValidationsStore       store_backend:           class_name: TupleS3StoreBackend           bucket: '<your_s3_bucket_name>'           prefix: '<your_s3_bucket_folder_name>'
  4. Copy existing Validation results to the S3 bucket. (This step is optional).

    One way to copy Validations into Amazon S3 is by using the aws s3 sync command. As mentioned earlier, the base_directory is set to uncommitted/validations/ by default. In the example below, two Validation results, Validation1 and Validation2 are copied to Amazon S3. Your output should looks something like this:

    aws s3 sync '<base_directory>' s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'upload: uncommitted/validations/val1/val1.json to s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'/val1.jsonupload: uncommitted/validations/val2/val2.json to s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'/val2.json
  5. Confirm that the new Validations store has been added by running great_expectations --v3-api store list .

    Notice the output contains two Validations Stores: the original validations_store on the local filesystem and the validations_S3_store we just configured. This is ok, since Great Expectations will look for Validation results on the S3 bucket as long as we set the validations_store_name variable to validations_S3_store.

    great_expectations --v3-api store list
    - name: validations_store  class_name: ValidationsStore  store_backend:    class_name: TupleFilesystemStoreBackend    base_directory: uncommitted/validations/
    - name: validations_S3_store  class_name: ValidationsStore  store_backend:    class_name: TupleS3StoreBackend    bucket: '<your_s3_bucket_name>'    prefix: '<your_s3_bucket_folder_name>'
  6. Confirm that the Validations store has been correctly configured.

    Run a Checkpoint to store results in the new Validations store on S3 then visualize the results by re-building Data Docs.

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