Part 3: Apache Iceberg and Big Data Ecosystem Integration - Enterprise Data Platform

Complete guide to Apache Iceberg integration with Spark, Flink, Presto/Trino, comparison with Delta Lake and Hudi, cloud storage optimization, and building large-scale data lakehouse through practical projects.

📋 Table of Contents

Apache Spark and Iceberg Integration
Apache Flink and Iceberg Integration
Presto/Trino and Iceberg Integration
Table Format Comparison Analysis
Cloud Storage Optimization
Practical Project: Large-scale Data Lakehouse Construction
Learning Summary

🔥 Apache Spark and Iceberg Integration

Spark-Iceberg Integration Overview

Apache Spark is one of the most powerful partners of Iceberg, providing a perfect combination for large-scale data processing and analytics.

Spark-Iceberg Integration Strategy

Integration Area	Strategy	Implementation Method	Benefits
Batch Processing	• Spark SQL + Iceberg • DataFrame API Utilization • Partition Optimization	• Iceberg Spark Connector • Automatic Partition Pruning • Schema Evolution Support	• Large-scale Data Processing • Complex Analytical Queries • Scalability
Streaming Processing	• Structured Streaming • Micro-batch Processing • Real-time Updates	• Delta Lake-style Processing • ACID Transactions • Schema Evolution	• Real-time Data Processing • Consistency Guarantee • Fault Recovery
ML Pipeline	• MLlib Integration • Feature Store • Model Version Management	• Iceberg-based Feature Storage • Experiment Tracking • Model Serving	• ML Workflow Integration • Experiment Management • Production Deployment

Spark-Iceberg Integration Implementation

class SparkIcebergIntegration:
    def __init__(self):
        self.spark_session = None
        self.iceberg_catalog = None
    
    def setup_spark_iceberg_environment(self):
        """Spark-Iceberg Environment Setup"""
        
        # Spark Configuration
        spark_config = {
            "spark.sql.extensions": "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions",
            "spark.sql.catalog.spark_catalog": "org.apache.iceberg.spark.SparkSessionCatalog",
            "spark.sql.catalog.spark_catalog.type": "hadoop",
            "spark.sql.catalog.spark_catalog.warehouse": "/warehouse",
            "spark.sql.defaultCatalog": "spark_catalog"
        }
        
        # Iceberg Configuration
        iceberg_config = {
            "write.target-file-size-bytes": "134217728",  # 128MB
            "write.parquet.compression-codec": "zstd",
            "write.metadata.delete-after-commit.enabled": "true",
            "write.data.delete-mode": "copy-on-write"
        }
        
        return spark_config, iceberg_config
    
    def demonstrate_spark_iceberg_operations(self):
        """Spark-Iceberg Operations Demonstration"""
        
        # Table Creation
        create_table_sql = """
        CREATE TABLE IF NOT EXISTS spark_catalog.default.user_events (
            user_id BIGINT,
            event_type STRING,
            event_data STRUCT<page_url: STRING, session_id: STRING>,
            timestamp TIMESTAMP
        ) USING iceberg
        PARTITIONED BY (days(timestamp))
        TBLPROPERTIES (
            'write.target-file-size-bytes' = '134217728',
            'write.parquet.compression-codec' = 'zstd'
        )
        """
        
        # Data Insertion
        insert_data_sql = """
        INSERT INTO spark_catalog.default.user_events
        SELECT 
            user_id,
            event_type,
            struct(page_url, session_id) as event_data,
            timestamp
        FROM source_table
        WHERE timestamp >= '2023-01-01'
        """
        
        # Schema Evolution
        evolve_schema_sql = """
        ALTER TABLE spark_catalog.default.user_events
        ADD COLUMN device_type STRING
        """
        
        # Partition Evolution
        evolve_partition_sql = """
        ALTER TABLE spark_catalog.default.user_events
        ADD PARTITION FIELD hours(timestamp)
        """
        
        return {
            "create_table": create_table_sql,
            "insert_data": insert_data_sql,
            "evolve_schema": evolve_schema_sql,
            "evolve_partition": evolve_partition_sql
        }

Spark Structured Streaming and Iceberg

Streaming Processing Strategy

Processing Mode	Description	Implementation Method	Use Cases
Append Mode	Add new data only	• INSERT INTO • Micro-batch	• Log Data • Event Streams
Update Mode	Update existing data	• MERGE INTO • Upsert Operations	• User Profiles • Order Status
Complete Mode	Rewrite entire table	• TRUNCATE + INSERT • Full Scan	• Aggregation Tables • Summary Data

Streaming Processing Implementation

class SparkStreamingIceberg:
    def __init__(self):
        self.streaming_query = None
    
    def setup_streaming_processing(self):
        """Streaming Processing Setup"""
        
        # Kafka Source Configuration
        kafka_source_config = {
            "kafka.bootstrap.servers": "localhost:9092",
            "subscribe": "user_events",
            "startingOffsets": "latest",
            "failOnDataLoss": "false"
        }
        
        # Iceberg Sink Configuration
        iceberg_sink_config = {
            "checkpointLocation": "/checkpoint/streaming",
            "outputMode": "append",
            "trigger": "processingTime=30 seconds"
        }
        
        return kafka_source_config, iceberg_sink_config
    
    def implement_streaming_pipeline(self):
        """Streaming Pipeline Implementation"""
        
        # Streaming Query
        streaming_query = """
        (spark
         .readStream
         .format("kafka")
         .option("kafka.bootstrap.servers", "localhost:9092")
         .option("subscribe", "user_events")
         .load()
         .select(
             from_json(col("value").cast("string"), schema).alias("data")
         )
         .select(
             col("data.user_id").cast("long").alias("user_id"),
             col("data.event_type").alias("event_type"),
             struct(
                 col("data.page_url").alias("page_url"),
                 col("data.session_id").alias("session_id")
             ).alias("event_data"),
             col("data.timestamp").cast("timestamp").alias("timestamp")
         )
         .writeStream
         .format("iceberg")
         .option("checkpointLocation", "/checkpoint/streaming")
         .trigger(processingTime="30 seconds")
         .toTable("spark_catalog.default.user_events")
         .start()
        )
        """
        
        return streaming_query

⚡ Apache Flink and Iceberg Integration

Flink-Iceberg Integration Overview

Apache Flink is specialized for real-time streaming processing and can implement real-time data lakehouse through integration with Iceberg.

Flink-Iceberg Integration Strategy

Integration Area	Strategy	Implementation Method	Benefits
Streaming Processing	• DataStream API • Table API • SQL API	• Flink Iceberg Connector • Real-time Snapshots • Exactly-once Processing	• Low-latency Processing • High Throughput • Fault Recovery
Batch Processing	• DataSet API • Batch Snapshots • Historical Data Processing	• Iceberg Table Reading • Partition Scanning • Schema Evolution	• Large-scale Batch Processing • Historical Analysis • Data Migration
State Management	• Flink State Backend • Iceberg Metadata • Checkpoint Integration	• State Persistence • Metadata Consistency • Recovery Optimization	• State Recovery • Consistency Guarantee • Performance Optimization

Flink-Iceberg Integration Implementation

class FlinkIcebergIntegration:
    def __init__(self):
        self.flink_env = None
        self.table_env = None
    
    def setup_flink_iceberg_environment(self):
        """Flink-Iceberg Environment Setup"""
        
        # Flink Configuration
        flink_config = {
            "execution.runtime-mode": "streaming",
            "execution.checkpointing.interval": "30s",
            "execution.checkpointing.externalized-checkpoint-retention": "retain-on-cancellation",
            "state.backend": "rocksdb",
            "state.checkpoints.dir": "file:///checkpoints"
        }
        
        # Iceberg Configuration
        iceberg_config = {
            "write.target-file-size-bytes": "134217728",
            "write.parquet.compression-codec": "zstd",
            "write.metadata.delete-after-commit.enabled": "true"
        }
        
        return flink_config, iceberg_config
    
    def implement_flink_streaming_pipeline(self):
        """Flink Streaming Pipeline Implementation"""
        
        # Streaming Processing using Table API
        streaming_pipeline = """
        # Create Kafka Source Table
        CREATE TABLE kafka_source (
            user_id BIGINT,
            event_type STRING,
            page_url STRING,
            session_id STRING,
            timestamp TIMESTAMP(3),
            WATERMARK FOR timestamp AS timestamp - INTERVAL '5' SECOND
        ) WITH (
            'connector' = 'kafka',
            'topic' = 'user_events',
            'properties.bootstrap.servers' = 'localhost:9092',
            'format' = 'json'
        )
        
        # Create Iceberg Sink Table
        CREATE TABLE iceberg_sink (
            user_id BIGINT,
            event_type STRING,
            event_data STRUCT<page_url STRING, session_id STRING>,
            timestamp TIMESTAMP
        ) PARTITIONED BY (days(timestamp))
        WITH (
            'connector' = 'iceberg',
            'catalog-name' = 'hadoop_catalog',
            'catalog-type' = 'hadoop',
            'warehouse' = '/warehouse',
            'database-name' = 'default',
            'table-name' = 'user_events'
        )
        
        # Execute Streaming Query
        INSERT INTO iceberg_sink
        SELECT 
            user_id,
            event_type,
            STRUCT(page_url, session_id) as event_data,
            timestamp
        FROM kafka_source
        WHERE event_type IN ('page_view', 'click', 'purchase')
        """
        
        return streaming_pipeline
    
    def implement_flink_batch_processing(self):
        """Flink Batch Processing Implementation"""
        
        # Batch Processing Pipeline
        batch_pipeline = """
        # Historical Data Processing
        CREATE TABLE historical_data (
            user_id BIGINT,
            event_type STRING,
            event_count BIGINT,
            processing_date DATE
        ) PARTITIONED BY (processing_date)
        WITH (
            'connector' = 'iceberg',
            'catalog-name' = 'hadoop_catalog',
            'catalog-type' = 'hadoop',
            'warehouse' = '/warehouse',
            'database-name' = 'default',
            'table-name' = 'daily_event_summary'
        )
        
        # Daily Event Aggregation
        INSERT INTO historical_data
        SELECT 
            user_id,
            event_type,
            COUNT(*) as event_count,
            DATE(timestamp) as processing_date
        FROM iceberg_sink
        WHERE DATE(timestamp) = '2023-01-01'
        GROUP BY user_id, event_type, DATE(timestamp)
        """
        
        return batch_pipeline

🚀 Presto/Trino and Iceberg Integration

Presto/Trino-Iceberg Integration Overview

Presto and Trino are query engines optimized for interactive analytics, providing fast ad-hoc analysis through integration with Iceberg.

Presto/Trino-Iceberg Integration Strategy

Integration Area	Strategy	Implementation Method	Benefits
Interactive Queries	• SQL Interface • Partition Pruning • Column Pruning	• Iceberg Connector • Metadata Caching • Query Optimization	• Fast Response Time • Complex Analytics • User-friendly
Distributed Queries	• MPP Architecture • Parallel Processing • Resource Management	• Cluster Scaling • Query Scheduling • Memory Management	• High Throughput • Scalability • Resource Efficiency
Metadata Management	• Unified Catalog • Schema Inference • Statistics Information	• Hive Metastore Integration • AWS Glue Support • Automatic Schema Detection	• Unified Management • Automation • Compatibility

Presto/Trino-Iceberg Integration Implementation

class PrestoTrinoIcebergIntegration:
    def __init__(self):
        self.catalog_config = {}
        self.query_optimizer = None
    
    def setup_presto_trino_catalog(self):
        """Presto/Trino Catalog Setup"""
        
        # Iceberg Catalog Configuration
        catalog_config = {
            "connector.name": "iceberg",
            "hive.metastore.uri": "thrift://localhost:9083",
            "iceberg.catalog.type": "hive_metastore",
            "iceberg.catalog.warehouse": "/warehouse",
            "iceberg.file-format": "PARQUET",
            "iceberg.compression-codec": "ZSTD"
        }
        
        # Query Optimization Configuration
        optimization_config = {
            "optimizer.use-mark-distinct": "true",
            "optimizer.optimize-metadata-queries": "true",
            "optimizer.partition-pruning": "true",
            "optimizer.column-pruning": "true"
        }
        
        return catalog_config, optimization_config
    
    def demonstrate_analytical_queries(self):
        """Analytical Queries Demonstration"""
        
        # Complex Analytical Queries
        analytical_queries = {
            "user_behavior_analysis": """
            SELECT 
                user_id,
                COUNT(*) as total_events,
                COUNT(DISTINCT event_type) as unique_event_types,
                COUNT(DISTINCT DATE(timestamp)) as active_days,
                MAX(timestamp) as last_activity,
                AVG(CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END) as purchase_rate
            FROM iceberg.default.user_events
            WHERE timestamp >= CURRENT_DATE - INTERVAL '30' DAY
            GROUP BY user_id
            HAVING COUNT(*) >= 10
            ORDER BY total_events DESC
            LIMIT 100
            """,
            
            "real_time_metrics": """
            WITH hourly_metrics AS (
                SELECT 
                    DATE_TRUNC('hour', timestamp) as hour,
                    event_type,
                    COUNT(*) as event_count,
                    COUNT(DISTINCT user_id) as unique_users
                FROM iceberg.default.user_events
                WHERE timestamp >= CURRENT_TIMESTAMP - INTERVAL '24' HOUR
                GROUP BY DATE_TRUNC('hour', timestamp), event_type
            )
            SELECT 
                hour,
                SUM(event_count) as total_events,
                SUM(unique_users) as total_unique_users,
                COUNT(DISTINCT event_type) as event_types
            FROM hourly_metrics
            GROUP BY hour
            ORDER BY hour DESC
            """,
            
            "funnel_analysis": """
            WITH user_journey AS (
                SELECT 
                    user_id,
                    session_id,
                    timestamp,
                    event_type,
                    ROW_NUMBER() OVER (
                        PARTITION BY user_id, session_id 
                        ORDER BY timestamp
                    ) as step_number
                FROM iceberg.default.user_events
                WHERE timestamp >= CURRENT_DATE - INTERVAL '7' DAY
            ),
            funnel_steps AS (
                SELECT 
                    step_number,
                    event_type,
                    COUNT(DISTINCT CONCAT(user_id, '-', session_id)) as sessions
                FROM user_journey
                WHERE step_number <= 5
                GROUP BY step_number, event_type
            )
            SELECT 
                step_number,
                event_type,
                sessions,
                LAG(sessions) OVER (ORDER BY step_number) as previous_step_sessions,
                ROUND(sessions * 100.0 / LAG(sessions) OVER (ORDER BY step_number), 2) as conversion_rate
            FROM funnel_steps
            ORDER BY step_number, event_type
            """
        }
        
        return analytical_queries
    
    def implement_performance_optimization(self):
        """Performance Optimization Implementation"""
        
        # Query Optimization Strategies
        optimization_strategies = {
            "partition_pruning": {
                "description": "I/O optimization through partition pruning",
                "implementation": "Add partition column conditions in WHERE clause",
                "benefit": "Reduce number of partitions to scan"
            },
            "column_pruning": {
                "description": "I/O optimization by selecting only necessary columns",
                "implementation": "Specify only required columns in SELECT clause",
                "benefit": "Reduce network and memory usage"
            },
            "predicate_pushdown": {
                "description": "Push filter conditions to storage level",
                "implementation": "Optimize WHERE clause conditions",
                "benefit": "Reduce I/O through storage-level filtering"
            },
            "statistics_utilization": {
                "description": "Utilize table statistics information",
                "implementation": "Update statistics with ANALYZE TABLE command",
                "benefit": "Query planner optimization"
            }
        }
        
        return optimization_strategies

🔄 Table Format Comparison Analysis

Major Table Format Comparison

Characteristic	Apache Iceberg	Delta Lake	Apache Hudi
Developer	Netflix → Apache	Databricks	Uber → Apache
Primary Language	Java, Python, Scala	Scala, Python, Java	Java, Scala
Schema Evolution	✅ Full Support	✅ Full Support	✅ Full Support
Partition Evolution	✅ Full Support	❌ Not Supported	✅ Partial Support
ACID Transactions	✅ Full Support	✅ Full Support	✅ Full Support
Time Travel	✅ Supported	✅ Supported	✅ Supported
Cloud Support	✅ Excellent	✅ Excellent	🟡 Good
Performance	🟢 Optimized	🟢 Optimized	🟡 Good
Ecosystem	🟢 Extensive	🟢 Spark-centric	🟡 Limited

Detailed Feature Comparison

Schema Management

Feature	Iceberg	Delta Lake	Hudi
Schema Addition	✅ Backward Compatible	✅ Backward Compatible	✅ Backward Compatible
Schema Deletion	✅ Backward Compatible	✅ Backward Compatible	✅ Backward Compatible
Type Change	✅ Conditionally Compatible	✅ Conditionally Compatible	✅ Conditionally Compatible
Schema Registry	✅ Supported	✅ Supported	❌ Not Supported

Partitioning

Feature	Iceberg	Delta Lake	Hudi
Partition Addition	✅ Runtime	❌ Requires Reconfiguration	✅ Runtime
Partition Deletion	✅ Runtime	❌ Requires Reconfiguration	✅ Runtime
Partition Transformation	✅ Runtime	❌ Requires Reconfiguration	✅ Runtime
Hidden Partitioning	✅ Supported	❌ Not Supported	❌ Not Supported

Performance Characteristics

Characteristic	Iceberg	Delta Lake	Hudi
Read Performance	🟢 Optimized	🟢 Optimized	🟡 Good
Write Performance	🟢 Optimized	🟢 Optimized	🟡 Good
Commit Performance	🟢 Fast	🟡 Good	🟡 Good
Metadata Size	🟢 Small	🟡 Good	🔴 Large

Selection Guide

Iceberg Selection Scenarios

Scenario	Reason	Implementation Method
Multiple Query Engines	• Spark, Flink, Presto/Trino Support • Vendor Neutrality	• Unified Catalog Construction • Standard SQL Interface
Partition Evolution	• Runtime Partition Changes • Hidden Partitioning	• Gradual Partition Strategy • Automatic Optimization
Cloud Native	• S3, ADLS, GCS Optimization • Object Storage Friendly	• Cloud Storage Integration • Cost Optimization

Delta Lake Selection Scenarios

Scenario	Reason	Implementation Method
Spark-centric	• Spark Ecosystem Integration • Databricks Support	• Spark-based Pipelines • Databricks Platform
ML/AI Workloads	• MLlib Integration • Feature Store	• ML Pipeline Construction • Experiment Management
Existing Spark Users	• Minimal Learning Curve • Code Reuse	• Gradual Migration • Compatibility Maintenance

Hudi Selection Scenarios

Scenario	Reason	Implementation Method
Real-time Processing	• Streaming Optimization • Low-latency Updates	• Kafka Integration • Real-time Pipelines
CDC (Change Data Capture)	• Database Change Detection • Real-time Synchronization	• Debezium Integration • CDC Pipelines
Upsert-centric	• Frequent Updates • Deduplication	• Upsert Strategy • Data Quality Management

☁️ Cloud Storage Optimization

Cloud Storage Comparison

Storage	Iceberg Support	Optimization Features	Cost Model	Performance
Amazon S3	✅ Full Support	• Intelligent Tiering • S3 Select • Transfer Acceleration	• Storage Class-based Pricing • Request-based Pricing	🟢 Excellent
Azure Data Lake Storage	✅ Full Support	• Hierarchical Namespace • Blob Storage Integration • Azure Analytics	• Hot/Cool/Archive • Access Frequency-based	🟢 Excellent
Google Cloud Storage	✅ Full Support	• Lifecycle Management • Nearline/Coldline • Transfer Service	• Storage Class-based Pricing • Network Pricing	🟢 Excellent

Cloud-specific Optimization Strategies

Amazon S3 Optimization

Optimization Area	Strategy	Implementation Method	Effect
Storage Classes	• Intelligent Tiering • Automatic Lifecycle	• S3 Lifecycle Policies • Access Pattern Analysis	• 40-60% Cost Savings • Automatic Optimization
Transfer Optimization	• Transfer Acceleration • Multipart Upload	• CloudFront Integration • Parallel Upload	• 50-500% Speed Improvement • Stability Enhancement
Request Optimization	• S3 Select • Glacier Select	• Column-based Queries • Direct Compressed Data Queries	• 80% Network Reduction • Query Speed Improvement

Azure Data Lake Storage Optimization

Optimization Area	Strategy	Implementation Method	Effect
Hierarchical Namespace	• Directory-based Policies • Metadata Optimization	• ACL-based Access Control • Per-directory Policies	• Enhanced Security • Management Efficiency
Storage Tiers	• Hot/Cool/Archive • Automatic Tier Movement	• Lifecycle Policies • Access Pattern-based Movement	• 30-70% Cost Savings • Automatic Management
Analytics Integration	• Azure Synapse • Azure Databricks	• Native Integration • Optimized Connectors	• Performance Enhancement • Unified Management

Google Cloud Storage Optimization

Optimization Area	Strategy	Implementation Method	Effect
Lifecycle Management	• Automatic Class Changes • Deletion Policies	• Lifecycle Rules • Condition-based Policies	• 40-80% Cost Savings • Automatic Management
Transfer Optimization	• Transfer Service • Parallel Processing	• Large Data Transfer • Network Optimization	• Transfer Speed Improvement • Stability Enhancement
Security Optimization	• IAM Integration • Encryption	• Fine-grained Permission Management • Customer-managed Keys	• Enhanced Security • Compliance

Cloud Storage Optimization Implementation

class CloudStorageOptimizer:
    def __init__(self):
        self.storage_configs = {}
        self.optimization_rules = {}
    
    def setup_s3_optimization(self):
        """S3 Optimization Setup"""
        
        # Storage Class Optimization
        storage_class_config = {
            "standard": {
                "use_case": "Frequently accessed data",
                "retention": "30_days",
                "cost_per_gb": 0.023
            },
            "standard_ia": {
                "use_case": "Occasionally accessed data",
                "retention": "90_days",
                "cost_per_gb": 0.0125
            },
            "glacier": {
                "use_case": "Long-term archived data",
                "retention": "365_days",
                "cost_per_gb": 0.004
            },
            "intelligent_tiering": {
                "use_case": "Data with irregular access patterns",
                "automation": True,
                "cost_per_gb": "variable"
            }
        }
        
        # Lifecycle Policy
        lifecycle_policy = {
            "rules": [
                {
                    "id": "IcebergDataLifecycle",
                    "status": "Enabled",
                    "transitions": [
                        {
                            "days": 30,
                            "storage_class": "STANDARD_IA"
                        },
                        {
                            "days": 90,
                            "storage_class": "GLACIER"
                        }
                    ],
                    "expiration": {
                        "days": 2555  # 7 years
                    }
                }
            ]
        }
        
        return storage_class_config, lifecycle_policy
    
    def setup_azure_optimization(self):
        """Azure Storage Optimization Setup"""
        
        # Storage Tier Configuration
        storage_tiers = {
            "hot": {
                "use_case": "Frequently accessed data",
                "retention": "30_days",
                "cost_per_gb": 0.0184
            },
            "cool": {
                "use_case": "Occasionally accessed data",
                "retention": "90_days",
                "cost_per_gb": 0.01
            },
            "archive": {
                "use_case": "Long-term archived data",
                "retention": "365_days",
                "cost_per_gb": 0.00099
            }
        }
        
        # Lifecycle Management Policy
        lifecycle_management = {
            "rules": [
                {
                    "name": "IcebergDataLifecycle",
                    "enabled": True,
                    "type": "Lifecycle",
                    "definition": {
                        "filters": {
                            "blob_types": ["blockBlob"],
                            "prefix_match": ["iceberg/"]
                        },
                        "actions": {
                            "base_blob": {
                                "tier_to_cool": {
                                    "days_after_modification_greater_than": 30
                                },
                                "tier_to_archive": {
                                    "days_after_modification_greater_than": 90
                                },
                                "delete": {
                                    "days_after_modification_greater_than": 2555
                                }
                            }
                        }
                    }
                }
            ]
        }
        
        return storage_tiers, lifecycle_management
    
    def setup_gcs_optimization(self):
        """Google Cloud Storage Optimization Setup"""
        
        # Storage Class Configuration
        storage_classes = {
            "standard": {
                "use_case": "Frequently accessed data",
                "retention": "30_days",
                "cost_per_gb": 0.02
            },
            "nearline": {
                "use_case": "Data accessed monthly",
                "retention": "30_days",
                "cost_per_gb": 0.01
            },
            "coldline": {
                "use_case": "Data accessed quarterly",
                "retention": "90_days",
                "cost_per_gb": 0.007
            },
            "archive": {
                "use_case": "Data accessed annually",
                "retention": "365_days",
                "cost_per_gb": 0.0012
            }
        }
        
        # Lifecycle Rules
        lifecycle_rules = {
            "rules": [
                {
                    "action": {
                        "type": "SetStorageClass",
                        "storageClass": "nearline"
                    },
                    "condition": {
                        "age": 30
                    }
                },
                {
                    "action": {
                        "type": "SetStorageClass",
                        "storageClass": "coldline"
                    },
                    "condition": {
                        "age": 90
                    }
                },
                {
                    "action": {
                        "type": "SetStorageClass",
                        "storageClass": "archive"
                    },
                    "condition": {
                        "age": 365
                    }
                },
                {
                    "action": {
                        "type": "Delete"
                    },
                    "condition": {
                        "age": 2555
                    }
                }
            ]
        }
        
        return storage_classes, lifecycle_rules

🏗️ Practical Project: Large-scale Data Lakehouse Construction

Project Overview

Building an Iceberg-based data lakehouse for a large-scale e-commerce platform, integrating various query engines and cloud storage.

System Architecture

Overall Architecture

Layer	Components	Technology Stack	Role
Data Ingestion	• Real-time Streams • Batch Files • API Data	• Kafka, Flink • Spark, Airflow • REST API	• Data Collection • Real-time Processing • Batch Processing
Data Storage	• Raw Data • Refined Data • Aggregated Data	• Iceberg Tables • S3/ADLS/GCS • Partitioning	• Data Storage • Version Management • Schema Evolution
Data Processing	• ETL/ELT • Real-time Analytics • ML Pipeline	• Spark, Flink • Presto/Trino • MLlib, TensorFlow	• Data Transformation • Analytical Processing • ML Modeling
Data Serving	• BI Tools • API Services • Real-time Dashboards	• Tableau, PowerBI • REST API • Grafana, Kibana	• Data Visualization • API Provision • Monitoring

Data Domain Design

Data Domain	Table Count	Data Volume	Partition Strategy	Retention Policy
User Analytics	25 tables	500TB	Date + User Bucket	7 years
Order Analytics	15 tables	300TB	Date + Region	10 years
Product Catalog	10 tables	50TB	Category	Permanent
Marketing Analytics	20 tables	200TB	Campaign + Date	5 years
Financial Analytics	12 tables	100TB	Monthly	15 years

Project Implementation

class EnterpriseDataLakehouse:
    def __init__(self):
        self.catalog_manager = CatalogManager()
        self.schema_registry = SchemaRegistry()
        self.data_governance = DataGovernance()
    
    def design_data_architecture(self):
        """Data Architecture Design"""
        
        architecture = {
            "data_layers": {
                "bronze_layer": {
                    "purpose": "Raw data storage",
                    "tables": [
                        "user_events_raw",
                        "order_events_raw", 
                        "product_updates_raw",
                        "marketing_events_raw"
                    ],
                    "partitioning": "hourly",
                    "retention": "30_days",
                    "format": "parquet",
                    "compression": "snappy"
                },
                "silver_layer": {
                    "purpose": "Refined data storage",
                    "tables": [
                        "user_events_cleaned",
                        "order_events_cleaned",
                        "product_catalog",
                        "marketing_campaigns"
                    ],
                    "partitioning": "daily",
                    "retention": "7_years",
                    "format": "parquet",
                    "compression": "zstd"
                },
                "gold_layer": {
                    "purpose": "Business analytics aggregated data",
                    "tables": [
                        "user_behavior_summary",
                        "daily_sales_summary",
                        "product_performance",
                        "marketing_effectiveness"
                    ],
                    "partitioning": "monthly",
                    "retention": "10_years",
                    "format": "parquet",
                    "compression": "zstd"
                }
            },
            "integration_patterns": {
                "real_time_ingestion": {
                    "source": "Kafka topics",
                    "processing": "Apache Flink",
                    "destination": "Bronze layer",
                    "latency": "< 5 minutes"
                },
                "batch_processing": {
                    "source": "External systems",
                    "processing": "Apache Spark",
                    "destination": "Silver/Gold layers",
                    "frequency": "daily"
                },
                "streaming_analytics": {
                    "source": "Bronze layer",
                    "processing": "Apache Flink + Spark",
                    "destination": "Gold layer",
                    "latency": "< 30 minutes"
                }
            }
        }
        
        return architecture
    
    def implement_multi_engine_integration(self):
        """Multi-engine Integration Implementation"""
        
        integration_config = {
            "spark_integration": {
                "use_cases": [
                    "ETL jobs",
                    "Batch analytics",
                    "ML pipelines"
                ],
                "tables": [
                    "user_events_processed",
                    "order_analytics",
                    "ml_features"
                ],
                "optimization": {
                    "target_file_size": "128MB",
                    "compression": "zstd",
                    "partitioning": "adaptive"
                }
            },
            "flink_integration": {
                "use_cases": [
                    "Real-time streaming",
                    "Event processing",
                    "Real-time aggregation"
                ],
                "tables": [
                    "real_time_metrics",
                    "streaming_events",
                    "live_dashboards"
                ],
                "optimization": {
                    "checkpoint_interval": "30s",
                    "parallelism": "auto",
                    "state_backend": "rocksdb"
                }
            },
            "presto_trino_integration": {
                "use_cases": [
                    "Interactive analytics",
                    "Ad-hoc queries",
                    "BI tool integration"
                ],
                "tables": [
                    "analytical_views",
                    "summary_tables",
                    "reporting_data"
                ],
                "optimization": {
                    "metadata_caching": True,
                    "query_optimization": True,
                    "parallel_execution": True
                }
            }
        }
        
        return integration_config
    
    def setup_cloud_optimization(self):
        """Cloud Optimization Setup"""
        
        cloud_optimization = {
            "storage_optimization": {
                "s3_optimization": {
                    "storage_classes": {
                        "standard": "Frequently accessed data (30 days)",
                        "standard_ia": "Occasionally accessed data (90 days)",
                        "glacier": "Long-term archived data (365 days)"
                    },
                    "lifecycle_policies": {
                        "automated_tiering": True,
                        "cost_optimization": True,
                        "retention_management": True
                    }
                },
                "performance_optimization": {
                    "intelligent_tiering": True,
                    "transfer_acceleration": True,
                    "s3_select": True
                }
            },
            "compute_optimization": {
                "auto_scaling": {
                    "spark_cluster": "CPU-based scaling",
                    "flink_cluster": "Throughput-based scaling",
                    "presto_cluster": "Query queue-based scaling"
                },
                "resource_optimization": {
                    "spot_instances": "70% cost savings",
                    "reserved_instances": "30% stability",
                    "right_sizing": "Monthly optimization"
                }
            },
            "cost_optimization": {
                "storage_costs": {
                    "current_monthly": "$15,000",
                    "optimized_monthly": "$8,500",
                    "savings_percentage": "43%"
                },
                "compute_costs": {
                    "current_monthly": "$25,000",
                    "optimized_monthly": "$18,000",
                    "savings_percentage": "28%"
                },
                "total_savings": {
                    "monthly": "$13,500",
                    "annual": "$162,000",
                    "savings_percentage": "34%"
                }
            }
        }
        
        return cloud_optimization

Data Governance and Quality Management

Data Governance Framework

Governance Area	Policy	Implementation Method	Responsible
Data Quality	• Completeness > 95% • Accuracy > 99% • Consistency Validation	• Automated Quality Checks • Data Profiling • Outlier Detection	Data Quality Team
Data Security	• Encryption (Storage/Transit) • Access Control (RBAC) • Audit Logging	• KMS Key Management • IAM Policies • CloudTrail Logging	Security Team
Data Lifecycle	• Retention Policies • Deletion Policies • Archive Policies	• Automated Lifecycle • Policy Engine • Compliance Checks	Data Architect
Metadata Management	• Schema Registry • Data Lineage • Business Glossary	• Automated Metadata Collection • Lineage Tracking • Glossary Management	Data Steward

Data Quality Monitoring

Quality Metric	Measurement Method	Threshold	Action
Completeness	NULL value ratio	< 5%	Data collection review
Accuracy	Business rule validation	> 99%	Data transformation logic review
Consistency	Referential integrity check	100%	Relational constraint review
Timeliness	Data refresh delay	< 1 hour	Pipeline performance optimization
Validity	Data type validation	100%	Schema validation enhancement

Operational Monitoring and Alerting

Monitoring Dashboards

Dashboard	Target	Key Metrics	Refresh Interval
Operations Dashboard	Operations Team	• System Status • Throughput • Error Rate	1 minute
Business Dashboard	Business Team	• Data Quality • Processing Delay • Cost Trends	5 minutes
Developer Dashboard	Development Team	• Pipeline Performance • Query Performance • Resource Utilization	1 minute

Alert Rules

Alert Type	Condition	Severity	Action
System Alert	CPU > 80%	Warning	Scale up
Data Alert	Quality score < 90%	Critical	Data team notification
Performance Alert	Query time > 5 minutes	Warning	Query optimization
Cost Alert	Daily cost > $2,000	Warning	Cost review

📚 Learning Summary

What We Learned in This Part

Apache Spark and Iceberg Integration
- Batch processing, streaming processing, ML pipeline integration
- Structured Streaming and Iceberg integration
- Performance optimization strategies
Apache Flink and Iceberg Integration
- Real-time streaming processing integration
- State management and checkpoint integration
- Combination of batch and streaming processing
Presto/Trino and Iceberg Integration
- Interactive analytics query optimization
- Distributed query processing
- Metadata management integration
Table Format Comparison Analysis
- Detailed comparison of Iceberg vs Delta Lake vs Hudi
- Selection guide and scenario-specific recommendations
- Migration strategies
Cloud Storage Optimization
- S3, ADLS, GCS optimization strategies
- Cost optimization and performance optimization
- Lifecycle management
Practical Project
- Large-scale data lakehouse construction
- Multi-engine integration architecture
- Data governance and quality management

Core Technology Stack

Technology	Role	Importance	Learning Points
Spark-Iceberg	Large-scale Data Processing	⭐⭐⭐⭐⭐	Batch/Streaming Integration, ML Pipelines
Flink-Iceberg	Real-time Streaming	⭐⭐⭐⭐⭐	Low-latency Processing, State Management, Checkpoints
Presto/Trino-Iceberg	Interactive Analytics	⭐⭐⭐⭐	Query Optimization, Metadata Caching
Cloud Optimization	Cost/Performance Optimization	⭐⭐⭐⭐⭐	Storage Tiers, Lifecycle, Automation
Data Governance	Quality/Security Management	⭐⭐⭐⭐	Quality Monitoring, Security Policies, Metadata

Series Completion Summary

Through the Apache Iceberg Complete Guide Series, we have completely mastered:

Part 1: Fundamentals and Table Format - Iceberg’s core concepts and basic features
Part 2: Advanced Features and Performance Optimization - Production-grade optimization and operational management
Part 3: Big Data Ecosystem Integration - Enterprise data platform construction

Next Steps

Now that you have completely mastered Apache Iceberg, explore these topics:

Apache Kafka Complete Guide - Real-time streaming platform
Apache Spark Advanced Guide - Advanced large-scale data processing
Cloud Data Platform Architecture - Cloud data platform design

Series Complete: Apache Iceberg Complete Guide Series

Completely master enterprise-grade data platforms through Apache Iceberg and big data ecosystem integration! 🧊✨