大型企业数据治理与数据资产化：从技术债到价值锚的演进之路

// 基于ShardingSphere的弹性分片配置样例
public class OrderShardingAlgorithm implements PreciseShardingAlgorithm<Long> {
    @Override
    public String doSharding(Collection<String> availableTargetNames, 
                            PreciseShardingValue<Long> shardingValue) {
        long orderId = shardingValue.getValue();
        return "ds_" + (orderId % 16 / 4); // 4库16表分片策略
    }
}

# 基于Apache Atlas的元数据图谱构建
def build_data_lineage(table_name):
    entity = atlas_client.get_entity_by_attribute("hive_table", "name", table_name)
    lineage = atlas_client.get_lineage(entity['guid'])
    
    # 生成DOT格式血缘图
    graph = Digraph()
    for process in lineage['processes']:
        graph.node(process['guid'], process['name'])
        for input in process['inputs']:
            graph.edge(input['guid'], process['guid'])
        for output in process['outputs']:
            graph.edge(process['guid'], output['guid'])
    return graph.source

// 基于Apache Griffin的质量规则引擎
case class DataQualityRule(
    dataset: String,
    completeness: Map[String, Double],  // 字段填充率阈值
    consistency: (String, String) => Boolean, // 逻辑一致性函数
    timeliness: Duration // 数据时效性要求
)
 
val equipmentRule = DataQualityRule(
    "iot_metrics",
    Map("temperature" -> 0.95, "vibration" -> 0.99),
    (ts, value) => !(value > 1000 && ts.hour > 22), // 夜间异常振动检测
    Duration("5 minutes")
)

# Lambda架构的典型实现（PySpark示例）
batch_view = spark.read.parquet("/data/batch").createOrReplaceTempView("batch")
stream_view = spark.readStream.format("kafka").createOrReplaceTempView("stream")
 
# 批流融合查询
result = spark.sql("""
    SELECT COALESCE(s.user_id, b.user_id) AS user_id,
           (COALESCE(s.amount,0) + COALESCE(b.amount,0)) AS total
    FROM stream s FULL OUTER JOIN batch b 
    ON s.user_id = b.user_id
""")

// 基于Flink的流式处理引擎
public class RiskControlProcessor extends 
    KeyedProcessFunction<String, TransactionEvent, AlertEvent> {
    
    private ValueState<Double> balanceState;
    
    public void processElement(TransactionEvent event, 
        Context ctx, Collector<AlertEvent> out) {
        
        Double currentBalance = balanceState.value();
        if (currentBalance == null) currentBalance = 0.0;
        
        // 实时余额校验
        if (event.getType() == TransactionType.WITHDRAW 
            && event.getAmount() > currentBalance) {
            out.collect(new AlertEvent(event.getAccountId(), "Insufficient balance"));
        }
        
        balanceState.update(currentBalance + event.getDelta());
    }
}

# 基于PySyft的联邦学习代码片段
import syft as sf
 
# 医院端（数据持有方）
hook = sf.TorchHook()
hospital = sf.VirtualWorker(hook, id="hospital")
 
# 药企端（模型需求方）
pharma = sf.VirtualWorker(hook, id="pharma")
 
# 数据加密与共享
hospital_data = torch.tensor([[0.2, 0.5], [0.7, 0.3]]).tag("hospital_data")
encrypted_data = hospital_data.encrypt(protocol="paillier", workers=[pharma])
 
# 安全聚合
@sf.func2protocol()
def secure_aggregation(data):
    return data.sum(dim=0)
 
result = secure_aggregation(encrypted_data)
decrypted_result = result.decrypt()

// 基于领域驱动设计的数据资产对象
public class ProductAsset {
    @DataProduct(id="PD001", domain="商品", owner="商品中心")
    private ProductCore productCore;
    
    @DataAsset(metric="CTR", calculation="clicks/impressions")
    private ProductBehavior behavior;
    
    @DataRelationship(source="productId", target="supplierId", 
                     type="BELONGS_TO")
    private SupplierRelationship supplier;
    
    public BigDecimal calculateLTV() {
        return behavior.getAvgPrice()
             .multiply(behavior.getRepurchaseRate())
             .multiply(new BigDecimal(behavior.getLifeCycle()));
    }
}

# 数据资产评估模型训练代码
from sklearn.ensemble import RandomForestRegressor
 
# 特征维度包括：
# - 数据质量评分
# - 访问频率
# - 关联系统数
# - 业务影响权重
X = df[['quality_score', 'access_freq', 'system_links', 'business_weight']]
y = df['transaction_price']  # 数据产品真实交易价格
 
model = RandomForestRegressor()
model.fit(X, y)
 
# 特征重要性分析
pd.Series(model.feature_importances_, index=X.columns).sort_values().plot.barh()

// 基于Spark Catalyst的自动化schema迁移
val optimizedPlan = spark.sessionState.executePlan(parsedLogicalPlan).optimizedPlan
 
val schemaEvolution = new SchemaEvolution()
schemaEvolution.onAnalysis({ analysis =>
    if (analysis.schemaChanges.nonEmpty) {
        val migrationPlan = schemaEvolution.generateMigrationPlan(
            currentSchema, 
            analysis.resolvedSchema
        )
        schemaEvolution.executeMigration(migrationPlan)
    }
})

// 基于Hyperledger Fabric的智能合约片段
contract DataAsset {
    struct Asset {
        string hash;
        address owner;
        uint256 timestamp;
    }
    
    mapping(string => Asset) private assets;
    
    function register(string memory _hash) public {
        require(assets[_hash].timestamp == 0, "Asset already exists");
        assets[_hash] = Asset(_hash, msg.sender, block.timestamp);
    }
    
    function verify(string memory _hash) public view returns (address) {
        return assets[_hash].owner;
    }
}