import boto3
import pandas as pd

s3 = boto3.client('s3')

def load_adx_dataset(bucket_name, dataset_id, revision_id):
    # List objects in the dataset revision
    response = s3.list_objects_v2(
        Bucket=bucket_name,
        Prefix=f"adx/{dataset_id}/{revision_id}/"
    )

    # Load each asset into a DataFrame
    dfs = []
    for obj in response.get('Contents', []):
        if obj['Key'].endswith('.csv'):
            s3_uri = f"s3://{bucket_name}/{obj['Key']}"
            df = pd.read_csv(s3_uri)
            dfs.append(df)

    return pd.concat(dfs, ignore_index=True)

import boto3

def audit_data_usage(subscription_id):
    client = boto3.client('dataexchange')

    # Get subscription details
    response = client.get_subscription(
        SubscriptionId=subscription_id
    )

    # Track data exports
    revisions = client.list_revision_assets(
        DatasetId=response['Arn'].split('/')[-1]
    )

    for revision in revisions['Assets']:
        # Log usage for compliance
        print(f"Asset: {revision['Name']}")
        print(f"Accessed: {revision['CreatedAt']}")
        print(f"Size: {revision['Size']} bytes")

    return {
        'subscription': response['Arn'],
        'assets': len(revisions['Assets']),
        'compliance_status': 'tracked'
    }

import boto3
from datetime import datetime

def manage_revisions(dataset_id):
    client = boto3.client('dataexchange')

    # List all revisions
    revisions = client.list_revisions(DatasetId=dataset_id)

    for revision in revisions['Revisions']:
        revision_id = revision['Id']
        created = revision['CreatedAt']

        # Check if new revision has new assets
        assets = client.list_revision_assets(
            DatasetId=dataset_id,
            RevisionId=revision_id
        )

        print(f"Revision {revision_id}: {len(assets['Assets'])} assets")
        print(f"Created: {created}")

    # For production, pin to a specific revision
    pinned_revision = revisions['Revisions'][0]['Id']
    return pinned_revision

import pandas as pd
import great_expectations as ge

def validate_adx_data(df, dataset_name):
    # Create Great Expectations suite
    df_ge = ge.from_pandas(df)

    # Schema validation
    expected_columns = {
        'id': 'int64',
        'timestamp': 'datetime64[ns]',
        'value': 'float64',
        'category': 'object'
    }

    for col, dtype in expected_columns.items():
        assert col in df.columns, f"Missing column: {col}"

    # Completeness checks
    df_ge.expect_column_values_to_not_be_null('id')
    df_ge.expect_column_values_to_not_be_null('timestamp')

    # Range checks
    df_ge.expect_column_values_to_be_between(
        'value', min_value=0, max_value=1000000
    )

    # Uniqueness checks
    df_ge.expect_column_values_to_be_unique('id')

    # Run validation
    results = df_ge.validate()

    if not results.success:
        raise ValueError(f"Data quality validation failed: {results}")

    return {
        'dataset': dataset_name,
        'records': len(df),
        'validation_passed': True,
        'timestamp': datetime.now().isoformat()
    }

import boto3
from datetime import datetime, timedelta

def monitor_adx_costs():
    cloudwatch = boto3.client('cloudwatch')

    # Monitor S3 storage for Data Exchange data
    response = cloudwatch.get_metric_statistics(
        Namespace='AWS/S3',
        MetricName='BucketSizeBytes',
        Dimensions=[
            {'Name': 'BucketName', 'Value': 'adx-data-bucket'},
            {'Name': 'StorageType', 'Value': 'StandardStorage'}
        ],
        StartTime=datetime.now() - timedelta(days=30),
        EndTime=datetime.now(),
        Period=86400,
        Statistics=['Average']
    )

    # Track data transfer costs
    transfer_response = cloudwatch.get_metric_statistics(
        Namespace='AWS/S3',
        MetricName='BytesDownloaded',
        Dimensions=[
            {'Name': 'BucketName', 'Value': 'adx-data-bucket'}
        ],
        StartTime=datetime.now() - timedelta(days=30),
        EndTime=datetime.now(),
        Period=86400,
        Statistics=['Sum']
    )

    return {
        'storage_bytes': response['Datapoints'][-1]['Average'],
        'transfer_bytes': transfer_response['Datapoints'][-1]['Sum'],
        'estimated_monthly_cost': calculate_cost(
            response['Datapoints'][-1]['Average'],
            transfer_response['Datapoints'][-1]['Sum']
        )
    }

import boto3

class DataMarketplace:
    def __init__(self):
        self.glue = boto3.client('glue')
        self.adx = boto3.client('dataexchange')

    def catalog_dataset(self, dataset_id, metadata):
        # Register in Glue Data Catalog
        self.glue.create_table(
            DatabaseName='data_marketplace',
            TableInput={
                'Name': metadata['name'],
                'Description': metadata['description'],
                'StorageDescriptor': {
                    'Location': f"s3://adx-bucket/{dataset_id}/",
                    'InputFormat': 'org.apache.hadoop.mapred.TextInputFormat',
                    'OutputFormat': 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat',
                    'SerdeInfo': {
                        'SerializationLibrary': 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
                    }
                },
                'Parameters': {
                    'adx.dataset.id': dataset_id,
                    'adx.provider': metadata['provider'],
                    'adx.category': metadata['category']
                }
            }
        )

    def request_access(self, user_id, dataset_id):
        # Check user permissions
        if self.check_permission(user_id, dataset_id):
            # Grant access via Data Exchange
            self.adx.create_data_set_grant(
                DataSetId=dataset_id,
                GranteeArn=f"arn:aws:iam::{user_id}"
            )
            return {'status': 'granted'}
        return {'status': 'denied'}