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  • OP Labs Data Platform
  • Development Guide
  • OP Labs Data Platform
  • Development Guide

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  • Architecture
  • Onchain Data Processing
    • Design Principles
    • Marker Metadata Layer
    • Ingestion
    • Blockbatch Models
  • Third-Party Data Ingestion
    • Overview
    • DailyDataset (GCS)
    • ClickHouseDataset
  • Data Warehouse
    • Onchain Data Pipelines: Blockbatch Load
    • General Purpose Data Pipelines: Transforms
    • Comparison to dbt and SQLMesh
  • OP Labs Public BigQuery Data
  • OP Labs Data Platform
  • Data Warehouse

Data Warehouse#

  • Onchain Data Pipelines: Blockbatch Load
    • Ad-hoc Blockbatch Queries
    • Data Loading Jobs
      • Loading by Blockbatch
      • Loading by Date and Chain
    • Load Job Specification
      • Location
      • Naming convention
      • Job Specification
    • Data Readiness, Markers, and Job Idempotency
    • Building Data Pipelines
      • Execution
      • Prototyping and Backfilling
      • Scheduling
      • Monitoring
  • General Purpose Data Pipelines: Transforms
    • Transform Groups
    • Transform Specification
      • Directory Structure and Naming Convention
    • Transform Execution Model
    • Building Data Pipelines
      • Execution
      • Prototyping and Backfilling
      • Scheduling
      • Markers
      • Monitoring
  • Comparison to dbt and SQLMesh
    • What dbt and SQLMesh offer
    • Why we didn’t chose dbt or SQLMesh
      • Lineage
      • Testability
      • SQL First (no indirection)
    • When dbt and SQLMesh might be a better fit

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Onchain Data Pipelines: Blockbatch Load

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