Amazon DynamoDB NoSQL Database

class DynamoDBHook(AwsBaseHook):
    """
    Hook for Amazon DynamoDB operations.
    
    Parameters:
    - table_keys: list - partition key and sort key for the table
    - table_name: str - target DynamoDB table name
    - aws_conn_id: str - Airflow connection for AWS credentials
    - region_name: str - AWS region name
    """
    def __init__(
        self,
        table_keys: list = None,
        table_name: str = None,
        aws_conn_id: str = 'aws_default',
        region_name: str = None,
        **kwargs
    ): ...
    
    def write_batch_data(self, items: Iterable) -> bool:
        """
        Write batch items to DynamoDB table with provisioned throughput capacity.
        
        Parameters:
        - items: Iterable - list of DynamoDB items to write
        
        Returns:
        bool: True if batch write succeeds
        """
        ...
    
    def get_import_status(self, import_arn: str) -> tuple[str, str, str]:
        """
        Get import status from DynamoDB.
        
        Parameters:
        - import_arn: str - Amazon Resource Name (ARN) for the import
        
        Returns:
        tuple: Import status, error code, error message
        """
        ...

class DynamoDBValueSensor(AwsBaseSensor):
    """
    Wait for an attribute value to be present for an item in a DynamoDB table.
    
    Parameters:
    - table_name: str - DynamoDB table name
    - partition_key_name: str - DynamoDB partition key name
    - partition_key_value: str - DynamoDB partition key value
    - attribute_name: str - DynamoDB attribute name to monitor
    - attribute_value: Any - expected DynamoDB attribute value
    - sort_key_name: str - DynamoDB sort key name (optional)
    - sort_key_value: str - DynamoDB sort key value (optional)
    - aws_conn_id: str - Airflow connection for AWS credentials
    - region_name: str - AWS region name
    - poke_interval: int - time in seconds between checks
    - timeout: int - maximum time to wait
    - mode: str - sensor mode ('poke' or 'reschedule')
    
    Returns:
    bool: True when attribute value matches expected value
    """
    def __init__(
        self,
        table_name: str,
        partition_key_name: str,
        partition_key_value: str,
        attribute_name: str,
        attribute_value: Any,
        sort_key_name: str = None,
        sort_key_value: str = None,
        aws_conn_id: str = 'aws_default',
        region_name: str = None,
        **kwargs
    ): ...

class S3ToDynamoDBOperator(BaseOperator):
    """
    Load data from S3 to DynamoDB table.
    
    Parameters:
    - s3_bucket: str - S3 bucket name containing source data
    - s3_key: str - S3 key path to source data
    - dynamodb_table: str - target DynamoDB table name
    - table_keys: list - partition key and sort key configuration
    - aws_conn_id: str - Airflow connection for AWS credentials
    - region_name: str - AWS region name
    - input_format: str - input data format ('JSON', 'CSV', 'DYNAMODB_JSON')
    - input_compression: str - compression type ('GZIP', 'ZSTD', 'NONE')
    
    Returns:
    str: Import job ARN
    """
    def __init__(
        self,
        s3_bucket: str,
        s3_key: str,
        dynamodb_table: str,
        table_keys: list = None,
        aws_conn_id: str = 'aws_default',
        region_name: str = None,
        input_format: str = 'DYNAMODB_JSON',
        input_compression: str = 'NONE',
        **kwargs
    ): ...

class DynamoDBToS3Operator(BaseOperator):
    """
    Export data from DynamoDB table to S3.
    
    Parameters:
    - dynamodb_table_name: str - source DynamoDB table name
    - s3_bucket_name: str - target S3 bucket name
    - s3_key_prefix: str - S3 key prefix for exported data
    - dynamodb_scan_kwargs: dict - additional scan parameters
    - s3_key_prefix_datetime_format: str - datetime format for S3 key prefix
    - process_func: Callable - function to process items before export
    - aws_conn_id: str - Airflow connection for AWS credentials
    - region_name: str - AWS region name
    
    Returns:
    str: S3 export location
    """
    def __init__(
        self,
        dynamodb_table_name: str,
        s3_bucket_name: str,
        s3_key_prefix: str = '',
        dynamodb_scan_kwargs: dict = None,
        s3_key_prefix_datetime_format: str = '%Y/%m/%d',
        process_func: Callable = None,
        aws_conn_id: str = 'aws_default',
        region_name: str = None,
        **kwargs
    ): ...

class HiveToDynamoDBOperator(BaseOperator):
    """
    Move data from Hive to DynamoDB.
    
    Parameters:
    - sql: str - SQL query to extract data from Hive
    - table_name: str - target DynamoDB table name
    - table_keys: list - partition key and sort key configuration
    - pre_process: Callable - function to preprocess data before insertion
    - pre_process_args: tuple - arguments for preprocessing function
    - pre_process_kwargs: dict - keyword arguments for preprocessing function
    - region_name: str - AWS region name
    - schema: str - Hive database schema
    - hiveserver2_conn_id: str - Airflow connection for Hive
    - aws_conn_id: str - Airflow connection for AWS credentials
    
    Returns:
    bool: True if transfer succeeds
    """
    def __init__(
        self,
        sql: str,
        table_name: str,
        table_keys: list = None,
        pre_process: Callable = None,
        pre_process_args: tuple = None,
        pre_process_kwargs: dict = None,
        region_name: str = None,
        schema: str = 'default',
        hiveserver2_conn_id: str = 'hiveserver2_default',
        aws_conn_id: str = 'aws_default',
        **kwargs
    ): ...

from airflow.providers.amazon.aws.hooks.dynamodb import DynamoDBHook

def load_user_data(**context):
    """Load user data into DynamoDB."""
    dynamodb_hook = DynamoDBHook(
        table_name='users',
        table_keys=['user_id'],  # Partition key
        aws_conn_id='aws_default'
    )
    
    # Sample user data
    users = [
        {
            'user_id': '001',
            'name': 'Alice Johnson',
            'email': 'alice@example.com',
            'tier': 'premium',
            'signup_date': '2023-01-15'
        },
        {
            'user_id': '002', 
            'name': 'Bob Smith',
            'email': 'bob@example.com',
            'tier': 'standard',
            'signup_date': '2023-02-20'
        }
    ]
    
    # Write batch data
    success = dynamodb_hook.write_batch_data(users)
    return f"Batch write success: {success}"

# Use with PythonOperator
load_users = PythonOperator(
    task_id='load_user_data',
    python_callable=load_user_data
)

from airflow.providers.amazon.aws.sensors.dynamodb import DynamoDBValueSensor
from airflow.providers.amazon.aws.transfers.s3_to_dynamodb import S3ToDynamoDBOperator

# Wait for processing status to be complete
wait_for_completion = DynamoDBValueSensor(
    task_id='wait_for_processing_complete',
    table_name='job_status',
    partition_key_name='job_id',
    partition_key_value='batch_job_001',
    attribute_name='status',
    attribute_value='COMPLETED',
    poke_interval=30,
    timeout=3600,  # 1 hour timeout
    aws_conn_id='aws_default'
)

# Load processed data from S3 to DynamoDB
load_results = S3ToDynamoDBOperator(
    task_id='load_processed_results',
    s3_bucket='processed-data-bucket',
    s3_key='batch-results/job_001/results.json',
    dynamodb_table='processed_results',
    table_keys=['result_id', 'timestamp'],  # Partition and sort keys
    input_format='DYNAMODB_JSON',
    aws_conn_id='aws_default'
)

wait_for_completion >> load_results

from airflow.providers.amazon.aws.transfers.s3_to_dynamodb import S3ToDynamoDBOperator

# Import customer data from S3 to DynamoDB
import_customers = S3ToDynamoDBOperator(
    task_id='import_customer_data',
    s3_bucket='data-lake-bucket',
    s3_key='customer-data/{{ ds }}/customers.json.gz',
    dynamodb_table='customers',
    table_keys=['customer_id'],
    input_format='DYNAMODB_JSON',
    input_compression='GZIP',
    aws_conn_id='aws_default'
)

from airflow.providers.amazon.aws.transfers.dynamodb_to_s3 import DynamoDBToS3Operator

def transform_user_data(item):
    """Transform DynamoDB item for analytics."""
    return {
        'user_id': item['user_id'],
        'name': item['name'],
        'tier': item['tier'],
        'signup_month': item['signup_date'][:7],  # YYYY-MM
        'is_premium': item['tier'] == 'premium'
    }

# Export user data to S3 for analytics
export_for_analytics = DynamoDBToS3Operator(
    task_id='export_users_for_analytics',
    dynamodb_table_name='users',
    s3_bucket_name='analytics-data-bucket',
    s3_key_prefix='user-data/{{ ds }}/',
    dynamodb_scan_kwargs={
        'ProjectionExpression': 'user_id, #n, email, tier, signup_date',
        'ExpressionAttributeNames': {'#n': 'name'},
        'FilterExpression': 'attribute_exists(tier)'
    },
    process_func=transform_user_data,
    s3_key_prefix_datetime_format='%Y/%m/%d',
    aws_conn_id='aws_default'
)

from airflow.providers.amazon.aws.hooks.dynamodb import DynamoDBHook

def sync_customer_orders(**context):
    """Synchronize customer data across multiple DynamoDB tables."""
    # Initialize hooks for different tables
    customers_hook = DynamoDBHook(
        table_name='customers',
        table_keys=['customer_id'],
        aws_conn_id='aws_default'
    )
    
    orders_hook = DynamoDBHook(
        table_name='orders',
        table_keys=['order_id'],
        aws_conn_id='aws_default'
    )
    
    # Process and sync data between tables
    # Implementation would fetch from one table and update another
    return "Synchronization completed"

# Multi-table synchronization task
sync_tables = PythonOperator(
    task_id='sync_customer_orders',
    python_callable=sync_customer_orders
)

from airflow.providers.amazon.aws.sensors.base_aws import AwsBaseSensor
from airflow.providers.amazon.aws.hooks.dynamodb import DynamoDBHook

class DynamoDBImportSensor(AwsBaseSensor):
    """Monitor DynamoDB import job status."""
    
    def __init__(self, import_arn: str, **kwargs):
        super().__init__(**kwargs)
        self.import_arn = import_arn
        self.aws_hook_class = DynamoDBHook
    
    def poke(self, context):
        hook = self.get_hook()
        status, error_code, error_msg = hook.get_import_status(self.import_arn)
        
        if status == 'COMPLETED':
            return True
        elif status == 'FAILED':
            raise AirflowException(f"Import failed: {error_code} - {error_msg}")
        else:
            self.log.info(f"Import status: {status}")
            return False

# Monitor import job
monitor_import = DynamoDBImportSensor(
    task_id='monitor_import_job',
    import_arn='arn:aws:dynamodb:us-west-2:123456789012:table/MyTable/import/01234567890123-abcdefgh',
    poke_interval=60,
    timeout=3600,
    aws_conn_id='aws_default'
)

from airflow.providers.amazon.aws.hooks.dynamodb import DynamoDBHook
from airflow.providers.amazon.aws.sensors.dynamodb import DynamoDBValueSensor
from airflow.providers.amazon.aws.transfers.s3_to_dynamodb import S3ToDynamoDBOperator
from airflow.providers.amazon.aws.transfers.dynamodb_to_s3 import DynamoDBToS3Operator
from airflow.providers.amazon.aws.transfers.hive_to_dynamodb import HiveToDynamoDBOperator