DLT MCP ByteBuddy Cookbook

This cookbook introduces how to use DLT (Data Loading Tool) MCP to build efficient data pipelines, enabling the AI assistant to process and transform various data formats.

Overview

DLT MCP provides powerful data processing and transformation capabilities, allowing ByteBuddy to:

• Extract data from multiple sources
• Perform complex data transformations
• Build scalable data pipelines
• Monitor data quality

Configuration

1. Install DLT MCP

pip install dlt-mcp-server
# Or
npm install -g dlt-mcp-server

2. Configure ByteBuddy

{
  "mcpServers": {
    "dlt": {
      "command": "python",
      "args": ["-m", "dlt_mcp_server"],
      "env": {
        "DLT_SECRET": "your-dlt-secret",
        "DLT_PROJECT": "your-project-name"
      }
    }
  }
}

3. Data Source Configuration

# dlt_sources.py
import dlt
from dlt.sources.helpers import requests

def github_source(org_name: str, repo_name: str):
    @dlt.resource(name="issues")
    def get_issues():
        url = f"https://api.github.com/repos/{org_name}/{repo_name}/issues"
        response = requests.get(url)
        yield from response.json()

    @dlt.resource(name="pull_requests")
    def get_pull_requests():
        url = f"https://api.github.com/repos/{org_name}/{repo_name}/pulls"
        response = requests.get(url)
        yield from response.json()

    return get_issues, get_pull_requests

Use Cases

Scenario 1: GitHub Data Analysis

// Analyze GitHub repository data
async function analyzeGitHubRepository(org: string, repo: string) {
  // Load data
  const loadData = await mcp.call("dlt.load", {
    source: "github",
    dataset: "github_analysis",
    config: {
      org_name: org,
      repo_name: repo,
    },
  });

  // Perform data transformation
  const transformedData = await mcp.call("dlt.transform", {
    input: loadData.dataset,
    transformations: [
      {
        type: "filter",
        condition: 'state == "open"',
      },
      {
        type: "aggregate",
        groupBy: ["author.login"],
        aggregations: {
          total_issues: "count(*)",
          avg_comments: "avg(comments)",
        },
      },
    ],
  });

  return transformedData;
}

Scenario 2: Real-Time Data Pipeline

// Build real-time data processing pipeline
class RealTimeDataPipeline {
  async setupPipeline(config: any) {
    const pipeline = await mcp.call("dlt.createPipeline", {
      name: "real_time_pipeline",
      destination: "bigquery",
      dataset_name: "analytics",
    });

    // Add data source
    await mcp.call("dlt.addSource", {
      pipeline: pipeline.id,
      source: {
        name: "web_events",
        type: "kafka",
        config: {
          bootstrap_servers: ["kafka1:9092", "kafka2:9092"],
          topic: "user_events",
          consumer_group: "analytics_group",
        },
      },
    });

    // Add transformation rules
    await mcp.call("dlt.addTransformation", {
      pipeline: pipeline.id,
      transformation: {
        name: "event_enrichment",
        type: "map",
        code: `
          def enrich_event(event):
            event['processed_at'] = datetime.utcnow()
            event['session_id'] = generate_session_id(event['user_id'])
            return event
        `,
      },
    });

    return pipeline;
  }

  async startMonitoring(pipelineId: string) {
    return await mcp.call("dlt.monitor", {
      pipeline: pipelineId,
      metrics: ["throughput", "latency", "error_rate"],
      alerts: {
        error_rate_threshold: 0.05,
        latency_threshold: 5000,
      },
    });
  }
}

Scenario 3: Data Quality Checks

// Implement data quality checks
class DataQualityChecker {
  async checkDataQuality(datasetId: string) {
    const qualityChecks = await mcp.call("dlt.runQualityChecks", {
      dataset: datasetId,
      checks: [
        {
          name: "completeness_check",
          type: "not_null",
          columns: ["user_id", "event_type", "timestamp"],
        },
        {
          name: "uniqueness_check",
          type: "unique",
          columns: ["event_id"],
        },
        {
          name: "range_check",
          type: "range",
          column: "event_value",
          min: 0,
          max: 10000,
        },
        {
          name: "referential_integrity",
          type: "foreign_key",
          column: "user_id",
          reference: "users.id",
        },
      ],
    });

    const issues = qualityChecks.filter((check) => !check.passed);

    return {
      overall_quality: issues.length === 0 ? "excellent" : "needs_improvement",
      passed_checks: qualityChecks.length - issues.length,
      failed_checks: issues.length,
      issues: issues.map((issue) => ({
        check_name: issue.name,
        issue_type: issue.issue_type,
        affected_rows: issue.affected_rows,
        recommendation: this.getRecommendation(issue),
      })),
    };
  }

  private getRecommendation(issue: any): string {
    const recommendations = {
      null_values: "Check data source or add default values",
      duplicate_values:
        "Implement deduplication logic or use unique constraints",
      out_of_range: "Validate input range or add data validation",
      broken_references: "Ensure referential integrity or clean orphaned data",
    };

    return recommendations[issue.issue_type] || "Needs further investigation";
  }
}

Data Source Connectors

1. Database Connectors

// Support multiple databases
class DatabaseConnector {
  async connectPostgreSQL(config: any) {
    return await mcp.call("dlt.connectDatabase", {
      type: "postgresql",
      host: config.host,
      port: config.port,
      database: config.database,
      username: config.username,
      password: config.password,
      ssl: config.ssl || false,
    });
  }

  async connectMongoDB(config: any) {
    return await mcp.call("dlt.connectDatabase", {
      type: "mongodb",
      connection_string: config.connection_string,
      database: config.database,
    });
  }

  async connectRedis(config: any) {
    return await mcp.call("dlt.connectDatabase", {
      type: "redis",
      host: config.host,
      port: config.port,
      password: config.password,
      database: config.database || 0,
    });
  }
}

2. API Connectors

// Generic API connector
class APIConnector {
  async createRestApiSource(config: any) {
    const sourceConfig = {
      name: config.name,
      type: "rest_api",
      base_url: config.base_url,
      headers: config.headers || {},
      auth: config.auth,
      pagination: config.pagination,
      rate_limit: config.rate_limit,
    };

    return await mcp.call("dlt.createApiSource", sourceConfig);
  }

  async createGraphQLSource(config: any) {
    const sourceConfig = {
      name: config.name,
      type: "graphql",
      endpoint: config.endpoint,
      headers: config.headers || {},
      auth: config.auth,
      query: config.query,
      variables: config.variables,
    };

    return await mcp.call("dlt.createApiSource", sourceConfig);
  }
}

Data Transformation

1. Advanced Transformations

// Complex data transformation
class DataTransformer {
  async transformUserData(userData: any[]) {
    return await mcp.call("dlt.transform", {
      input: userData,
      operations: [
        {
          type: "map",
          function: `
            def transform_user(user):
              # Clean and normalize data
              user['email'] = user['email'].lower().strip()
              user['full_name'] = f"{user['first_name']} {user['last_name']}"
              user['age_group'] = categorize_age(user['age'])
              user['registration_date'] = parse_date(user['created_at'])
              return user
          `,
        },
        {
          type: "filter",
          condition: "email is not None and age >= 18",
        },
        {
          type: "aggregate",
          groupBy: ["registration_date"],
          aggregations: {
            daily_registrations: "count(*)",
            avg_age: "avg(age)",
            gender_distribution: "count_distinct(gender)",
          },
        },
        {
          type: "window",
          functions: {
            cumulative_registrations:
              "sum(daily_registrations) over (order by registration_date)",
            registration_growth_rate:
              "daily_registrations / lag(daily_registrations) over (order by registration_date)",
          },
        },
      ],
    });
  }

  async detectAnomalies(data: any[], column: string) {
    return await mcp.call("dlt.detectAnomalies", {
      data,
      method: "isolation_forest",
      columns: [column],
      contamination: 0.1,
      features: ["z_score", "iqr_score", "modified_z_score"],
    });
  }
}

2. Stream Processing

// Stream data processing
class StreamProcessor {
  async setupStreamProcessing(config: any) {
    const streamConfig = {
      input_source: config.input_source,
      output_destination: config.output_destination,
      processing_window: config.window || "1m",
      batch_size: config.batch_size || 1000,
      checkpoint_interval: config.checkpoint_interval || 10000,
    };

    return await mcp.call("dlt.createStream", streamConfig);
  }

  async addStreamProcessor(streamId: string, processor: any) {
    return await mcp.call("dlt.addStreamProcessor", {
      stream: streamId,
      processor: {
        name: processor.name,
        type: processor.type,
        config: processor.config,
        code: processor.code,
      },
    });
  }
}

Monitoring and Debugging

1. Performance Monitoring

// Data pipeline performance monitoring
class PipelineMonitor {
  async monitorPipeline(pipelineId: string) {
    const metrics = await mcp.call("dlt.getMetrics", {
      pipeline: pipelineId,
      timeRange: "1h",
      metrics: [
        "throughput",
        "latency",
        "error_rate",
        "data_quality_score",
        "resource_utilization",
      ],
    });

    return {
      performance: {
        avg_throughput: metrics.throughput.avg,
        peak_throughput: metrics.throughput.max,
        avg_latency: metrics.latency.avg,
        p95_latency: metrics.latency.p95,
      },
      quality: {
        error_rate: metrics.error_rate.current,
        data_quality_score: metrics.data_quality_score.current,
        failed_records: metrics.failed_records.total,
      },
      resources: {
        cpu_usage: metrics.resource_utilization.cpu,
        memory_usage: metrics.resource_utilization.memory,
        disk_io: metrics.resource_utilization.disk,
      },
    };
  }

  async generateOptimizationSuggestions(pipelineId: string) {
    const metrics = await this.monitorPipeline(pipelineId);
    const suggestions = [];

    if (metrics.performance.avg_latency > 5000) {
      suggestions.push({
        issue: "High latency detected",
        suggestion:
          "Consider increasing parallelism or optimizing transformations",
        impact: "high",
      });
    }

    if (metrics.quality.error_rate > 0.02) {
      suggestions.push({
        issue: "Elevated error rate",
        suggestion: "Review data quality checks and add error handling",
        impact: "medium",
      });
    }

    if (metrics.resources.memory_usage > 0.8) {
      suggestions.push({
        issue: "High memory usage",
        suggestion: "Increase memory allocation or optimize memory usage",
        impact: "high",
      });
    }

    return suggestions;
  }
}

Best Practices

1. Data Modeling

// Data model definition
const dataModels = {
  user_events: {
    schema: {
      event_id: "string",
      user_id: "string",
      event_type: "string",
      event_data: "json",
      timestamp: "timestamp",
      session_id: "string",
      device_info: "json",
    },
    indexes: [
      { columns: ["user_id", "timestamp"] },
      { columns: ["event_type", "timestamp"] },
      { columns: ["session_id"] },
    ],
    partitioning: {
      column: "timestamp",
      granularity: "daily",
    },
  },
};

2. Error Handling

// Robust error handling
class RobustDataPipeline {
  async executeWithRetry(operation: any, maxRetries: number = 3) {
    for (let attempt = 1; attempt <= maxRetries; attempt++) {
      try {
        return await operation();
      } catch (error) {
        if (attempt === maxRetries) {
          await this.logError(error, operation);
          throw error;
        }

        const delay = Math.pow(2, attempt) * 1000; // Exponential backoff
        await new Promise((resolve) => setTimeout(resolve, delay));
      }
    }
  }

  async handleDataErrors(errors: any[]) {
    const errorCategories = this.categorizeErrors(errors);

    for (const [category, categoryErrors] of Object.entries(errorCategories)) {
      switch (category) {
        case "validation_errors":
          await this.handleValidationErrors(categoryErrors);
          break;
        case "schema_mismatches":
          await this.handleSchemaMismatches(categoryErrors);
          break;
        case "connection_errors":
          await this.handleConnectionErrors(categoryErrors);
          break;
      }
    }
  }
}

With DLT MCP, ByteBuddy can provide powerful data processing and pipeline building capabilities, enabling data engineers to quickly build reliable data infrastructure.