Physical lineage

 

Physical lineage delves into the granular technical details of data movement, offering a comprehensive view of how data traverses through various hardware and software components, including databases, servers, and ETL (Extract, Transform, Load) jobs. This level of lineage is crucial for IT professionals responsible for managing, troubleshooting, and optimizing the data infrastructure of an organization. Here's an example illustrating physical lineage:

 

Example: E-commerce Data Processing Pipeline

Data Collection

Hardware/Software: Web servers collect user interaction data from the e-commerce website.

ETL Job: Apache Kafka streams capture and buffer website activity logs.

Database: Raw data is stored in a MongoDB database for temporary storage.

 

Data Transformation

ETL Job: Apache Spark job reads data from MongoDB, transforms it into a structured format, and enriches it with additional product information.

Server: Apache Spark cluster processes data transformations.

 

Data Loading

ETL Job: Transformed data is loaded into a PostgreSQL database for analytical purposes.

Database: PostgreSQL database stores structured data for analysis and reporting.

 

Analytics and Reporting

Software: Business intelligence tools like Tableau connect to the PostgreSQL database to generate reports and visualizations.

Server: Tableau Server hosts and serves interactive dashboards to business users.

 

In this example, physical lineage provides detailed insights into the hardware and software components involved in the e-commerce data processing pipeline. It outlines the exact path and transformations of data, from collection on web servers to buffering in Kafka streams, storage in MongoDB, transformation using Apache Spark, loading into PostgreSQL, and visualization with Tableau. This level of detail is essential for IT professionals to effectively manage, troubleshoot, and optimize the performance of each component within the data infrastructure.

{
  "data_collection": {
    "components": [
      {
        "type": "hardware",
        "name": "Web Servers",
        "description": "Collect user interaction data from the e-commerce website.",
        "details": {
          "location": "Data Center A",
          "type": "Virtual Machine",
          "IP_address": "192.168.1.100",
          "CPU": "4 cores",
          "RAM": "16 GB",
          "Storage": "500 GB SSD"
        }
      },
      {
        "type": "software",
        "name": "Apache Kafka",
        "description": "Stream website activity logs for buffering.",
        "details": {
          "location": "Data Center B",
          "type": "Docker Container",
          "FQDN": "kafka.example.com",
          "CPU": "2 cores",
          "RAM": "8 GB",
          "Storage": "100 GB HDD"
        }
      },
      {
        "type": "database",
        "name": "MongoDB",
        "description": "Store raw data for temporary storage.",
        "details": {
          "location": "Data Center C",
          "type": "Dedicated Server",
          "IP_address": "10.0.0.50",
          "CPU": "8 cores",
          "RAM": "32 GB",
          "Storage": "1 TB HDD"
        }
      }
    ]
  },
  "data_transformation": {
    "components": [
      {
        "type": "ETL Job",
        "name": "Apache Spark",
        "description": "Read data from MongoDB, transform it into a structured format, and enrich it with additional product information.",
        "details": {
          "location": "Data Center A",
          "type": "Virtual Machine",
          "IP_address": "192.168.1.200",
          "CPU": "8 cores",
          "RAM": "32 GB",
          "Storage": "1 TB SSD"
        }
      },
      {
        "type": "server",
        "name": "Apache Spark Cluster",
        "description": "Process data transformations.",
        "details": {
          "location": "Data Center A",
          "type": "Cluster",
          "IP_addresses": ["192.168.1.201", "192.168.1.202", "192.168.1.203"],
          "CPU": "64 cores",
          "RAM": "512 GB",
          "Storage": "10 TB SSD"
        }
      }
    ]
  },
  "data_loading": {
    "components": [
      {
        "type": "ETL Job",
        "name": "PostgreSQL Loader",
        "description": "Load transformed data into PostgreSQL for analytical purposes.",
        "details": {
          "location": "Data Center B",
          "type": "Virtual Machine",
          "IP_address": "172.16.0.100",
          "CPU": "4 cores",
          "RAM": "16 GB",
          "Storage": "500 GB SSD"
        }
      },
      {
        "type": "database",
        "name": "PostgreSQL",
        "description": "Store structured data for analysis and reporting.",
        "details": {
          "location": "Data Center B",
          "type": "Dedicated Server",
          "IP_address": "172.16.0.50",
          "CPU": "16 cores",
          "RAM": "64 GB",
          "Storage": "2 TB HDD"
        }
      }
    ]
  },
  "analytics_and_reporting": {
    "components": [
      {
        "type": "software",
        "name": "Tableau",
        "description": "Connect to PostgreSQL database to generate reports and visualizations."
      },
      {
        "type": "server",
        "name": "Tableau Server",
        "description": "Host and serve interactive dashboards to business users.",
        "details": {
          "location": "Data Center C",
          "type": "Virtual Machine",
          "IP_address": "10.20.30.40",
          "CPU": "12 cores",
          "RAM": "64 GB",
          "Storage": "500 GB SSD"
        }
      }
    ]
  }
}

 

 

 

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