Enterprise Data Lake Architecture

System Architecture

A comprehensive data lake architecture that combines multi-zone storage, data governance, and automated processing capabilities.

Core Components

1. Data Ingestion Pipeline

class DataIngestionPipeline:
    def __init__(self, config: Dict[str, Any]):
        self.source_connectors = {
            'kafka': KafkaConnector(config['kafka_config']),
            'rest': RestAPIConnector(config['api_config']),
            'database': DatabaseConnector(config['db_config']),
            'file': FileSystemConnector(config['file_config'])
        }
        self.validator = DataValidator(config['validation_config'])
        self.router = ZoneRouter(config['routing_config'])
        
    async def ingest_data(
        self,
        source: str,
        data_spec: DataSpec
    ) -> IngestionResult:
        # Get appropriate connector
        connector = self.source_connectors[source]
        
        # Extract data
        raw_data = await connector.extract_data(data_spec)
        
        # Validate data
        validation_result = await self.validator.validate(
            raw_data,
            data_spec.schema
        )
        
        if validation_result.is_valid:
            # Route to appropriate zone
            zone_path = await self.router.route_data(
                raw_data,
                data_spec
            )
            
            # Write to zone
            write_result = await self._write_to_zone(
                raw_data,
                zone_path
            )
            
            return IngestionResult(
                status='success',
                location=zone_path,
                metadata=write_result.metadata
            )
        
        return IngestionResult(
            status='failed',
            errors=validation_result.errors
        )

2. Zone Management

class ZoneManager:
    def __init__(self, config: Dict[str, Any]):
        self.zones = {
            'raw': RawZone(config['raw_config']),
            'cleansed': CleansedZone(config['cleansed_config']),
            'curated': CuratedZone(config['curated_config']),
            'consumption': ConsumptionZone(config['consumption_config'])
        }
        self.policy_manager = PolicyManager(config['policy_config'])
        
    async def process_data(
        self,
        data: Any,
        zone_path: str
    ) -> ProcessingResult:
        # Get zone handler
        zone = self._get_zone_handler(zone_path)
        
        # Apply zone-specific processing
        processed_data = await zone.process_data(data)
        
        # Apply policies
        await self.policy_manager.apply_policies(
            processed_data,
            zone_path
        )
        
        return ProcessingResult(
            data=processed_data,
            zone=zone_path
        )

class CuratedZone:
    def __init__(self, config: Dict[str, Any]):
        self.quality_checker = DataQualityChecker(
            config['quality_config']
        )
        self.enricher = DataEnricher(config['enrichment_config'])
        self.partitioner = DataPartitioner(
            config['partition_config']
        )

3. Data Governance

class GovernanceEngine:
    def __init__(self, config: Dict[str, Any]):
        self.metadata_store = MetadataStore(config['metadata_config'])
        self.lineage_tracker = LineageTracker(
            config['lineage_config']
        )
        self.access_manager = AccessManager(
            config['access_config']
        )
        self.compliance_checker = ComplianceChecker(
            config['compliance_config']
        )
        
    async def govern_data(
        self,
        data: Any,
        metadata: Dict[str, Any]
    ) -> GovernanceResult:
        # Track lineage
        lineage = await self.lineage_tracker.track(
            data,
            metadata
        )
        
        # Check compliance
        compliance_result = await self.compliance_checker.check(
            data,
            metadata
        )
        
        if not compliance_result.is_compliant:
            return GovernanceResult(
                status='failed',
                issues=compliance_result.issues
            )
            
        # Store metadata
        await self.metadata_store.store(
            metadata,
            lineage
        )
        
        # Set access controls
        access_policies = await self.access_manager.set_policies(
            data,
            metadata
        )
        
        return GovernanceResult(
            status='success',
            lineage=lineage,
            access_policies=access_policies
        )

4. Query Engine

class QueryEngine:
    def __init__(self, config: Dict[str, Any]):
        self.query_optimizer = QueryOptimizer(
            config['optimizer_config']
        )
        self.execution_engine = QueryExecutor(
            config['executor_config']
        )
        self.cache_manager = QueryCache(config['cache_config'])
        
    async def execute_query(
        self,
        query: Query,
        context: Optional[Dict[str, Any]] = None
    ) -> QueryResult:
        # Check cache
        cached_result = await self.cache_manager.get_result(
            query
        )
        if cached_result:
            return cached_result
            
        # Optimize query
        optimized_query = await self.query_optimizer.optimize(
            query,
            context
        )
        
        # Execute query
        result = await self.execution_engine.execute(
            optimized_query
        )
        
        # Cache result
        await self.cache_manager.store_result(
            query,
            result
        )
        
        return result

class QueryOptimizer:
    def __init__(self, config: Dict[str, Any]):
        self.cost_estimator = CostEstimator(config['cost_config'])
        self.plan_generator = PlanGenerator(
            config['plan_config']
        )
        self.statistics = StatisticsManager(
            config['stats_config']
        )

Usage Example

# Initialize data lake system
config = {
    'storage_config': {
        'raw_zone': 's3://data-lake/raw',
        'cleansed_zone': 's3://data-lake/cleansed',
        'curated_zone': 's3://data-lake/curated'
    },
    'governance_config': {
        'metadata_store': 'postgresql://metadata-db',
        'compliance_rules': 'compliance.yaml'
    },
    'processing_config': {
        'batch_size': 1000,
        'processing_threads': 8
    }
}

data_lake = DataLakeSystem(config)

# Ingest data
data_spec = DataSpec(
    source='kafka',
    topic='user_events',
    schema='user_events.avsc',
    partition_key='timestamp'
)

ingestion_result = await data_lake.ingest_data(
    'kafka',
    data_spec
)

# Process data through zones
processing_result = await data_lake.process_data(
    ingestion_result.location
)

# Query data
query = Query(
    select=['user_id', 'event_type', 'timestamp'],
    from_path='curated/user_events',
    where={'event_type': 'purchase'},
    partition_date='2024-02-10'
)

results = await data_lake.query_data(query)

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