iiRecord
Agentic AI Atlas · obt-creation
lib-process:data-engineering-analytics--obt-creationa5c.ai
II.
LibraryProcess JSON

lib-process:data-engineering-analytics--obt-creation

Structured · live

obt-creation json

Inspect the normalized record payload exactly as the atlas UI reads it.

File · generated-library/processes.yamlCluster · generated-library
Record JSON
{
  "id": "lib-process:data-engineering-analytics--obt-creation",
  "_kind": "LibraryProcess",
  "_file": "generated-library/processes.yaml",
  "_cluster": "generated-library",
  "attributes": {
    "displayName": "obt-creation",
    "description": "One Big Table (OBT) Creation - Design and implement denormalized One Big Table (OBT) structures\nby joining fact and dimension tables, optimizing for analytical query performance, implementing appropriate\ndenormalization strategies, and identifying optimal use cases for OBT patterns in data warehouse architecture.",
    "libraryPath": "library/specializations/data-engineering-analytics/obt-creation.js",
    "specialization": "data-engineering-analytics",
    "references": [
      "- One Big Table Pattern: https://www.holistics.io/blog/the-one-big-table-gambit/\n- Denormalization Best Practices: https://www.kimballgroup.com/data-warehouse-business-intelligence-resources/kimball-techniques/dimensional-modeling-techniques/\n- Columnar Storage Optimization: https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions\n- BigQuery Denormalized Tables: https://cloud.google.com/bigquery/docs/best-practices-performance-patterns\n- Performance Optimization: https://www.databricks.com/blog/2022/05/20/five-simple-steps-for-implementing-a-star-schema-in-databricks-with-delta-lake.html"
    ],
    "example": "const result = await orchestrate('data-engineering-analytics/obt-creation', {\n  projectName: 'Sales Analytics OBT',\n  sourceSchema: 'star_schema',\n  targetSchema: 'obt_analytics',\n  performanceGoals: { queryTimeReduction: 80, targetLatency: '100ms' },\n  dataVolume: 'large', // 'small', 'medium', 'large', 'xlarge'\n  refreshStrategy: 'incremental', // 'full', 'incremental', 'streaming'\n  storageOptimization: 'columnar', // 'columnar', 'row-based', 'hybrid'\n  aggregationLevel: 'transaction' // 'transaction', 'daily', 'weekly', 'monthly'\n});",
    "usesAgents": [
      "schema-analyst",
      "use-case-analyst",
      "denormalization-architect",
      "join-optimizer",
      "obt-schema-designer",
      "performance-optimizer",
      "materialization-architect",
      "ddl-generator",
      "pipeline-architect",
      "query-specialist",
      "validation-architect",
      "documentation-specialist"
    ]
  },
  "outgoingEdges": [
    {
      "from": "lib-process:data-engineering-analytics--obt-creation",
      "to": "domain:data-engineering",
      "kind": "lib_applies_to_domain",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-engineering-analytics--obt-creation",
      "to": "workflow:data-pipeline-deployment",
      "kind": "lib_implements_workflow",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-engineering-analytics--obt-creation",
      "to": "specialization:data-engineering-analytics",
      "kind": "lib_belongs_to_specialization",
      "attributes": {
        "weight": 0.9
      }
    }
  ],
  "incomingEdges": []
}