gcp-pipeline-core 1.0.19

Core library for GCP data pipelines

gcp-pipeline-core

Foundation library - audit, monitoring, error handling, job control.

NO Apache Beam or Airflow dependencies.

---

Architecture

                         GCP-PIPELINE-CORE
                         ─────────────────

  ┌─────────────────────────────────────────────────────────────────┐
  │                     FOUNDATION LAYER                             │
  │                                                                  │
  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
  │  │   Audit     │  │  Monitoring │  │   Error     │              │
  │  │   Trail     │  │   Metrics   │  │  Handling   │              │
  │  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘              │
  │         │                │                │                      │
  │         ▼                ▼                ▼                      │
  │  ┌─────────────────────────────────────────────────────────┐    │
  │  │                  Utilities Layer                         │    │
  │  │  • Structured Logging (JSON)                             │    │
  │  │  • Run ID Generation                                     │    │
  │  │  • Configuration Management                              │    │
  │  └─────────────────────────────────────────────────────────┘    │
  │         │                │                │                      │
  │         ▼                ▼                ▼                      │
  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
  │  │   Clients   │  │ Job Control │  │   Schema    │              │
  │  │ GCS/BQ/PS   │  │  Repository │  │ Definitions │              │
  │  └─────────────┘  └─────────────┘  └─────────────┘              │
  │                                                                  │
  └─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
              Used by: gcp-pipeline-beam, gcp-pipeline-orchestration

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Modules

Module	Purpose	Key Classes
audit/	Lineage tracking, reconciliation	AuditTrail, ReconciliationEngine
monitoring/	Metrics, OTEL/Dynatrace	MetricsCollector, OTELExporter
finops/	Cost tracking and labeling	BigQueryCostTracker, FinOpsLabels
error_handling/	Error classification, retry	ErrorHandler, RetryPolicy
job_control/	Pipeline status tracking	JobControlRepository, PipelineJob
clients/	GCP service wrappers	GCSClient, BigQueryClient, PubSubClient
utilities/	Logging, run ID	configure_structured_logging, generate_run_id
data_quality/	Quality checks	validate_row_types, check_duplicate_keys
schema.py	Entity definitions	EntitySchema, SchemaField

---

Component Flow

Pipeline Start
      │
      ▼
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ generate_   │───►│ AuditTrail  │───►│ Structured  │
│ run_id()    │    │ .record_processing_start()    │    │ Logging     │
└─────────────┘    └─────────────┘    └─────────────┘
      │                  │                  │
      ▼                  ▼                  ▼
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ JobControl  │    │ Metrics     │    │ Error       │
│ .create()   │    │ .record()   │    │ Handler     │
└─────────────┘    └─────────────┘    └─────────────┘
      │                  │                  │
      └──────────────────┴──────────────────┘
                         │
                         ▼
                  ┌─────────────┐
                  │ Reconcile   │
                  │ & Complete  │
                  └─────────────┘

---

Key Findings

1. Audit Trail & Lineage

• AuditTrail: Implements robust tracking of run_id across all pipeline stages.
• DuplicateDetector: Provides idempotency by tracking seen records and preventing double-processing.
• Publisher: Supports automated publishing of audit records to BigQuery for centralized monitoring.

2. Sophisticated Error Handling

• ErrorClassifier: Categorizes exceptions into:
  • Validation: Data errors (no retry).
  • Integration: Connection/API errors (retry with backoff).
  • Resource: Quota/Rate limit errors (exponential backoff).
• RetryPolicy: Configurable backoff multipliers, jitter, and maximum retry attempts.

3. Job Control

• JobControlRepository: Centralized state management for pipeline executions.
• State Tracking: Granular tracking of failure stages, start/end times, and record counts.

4. Structured Logging

• Standardized JSON logging with automated context injection (run_id, system_id).
• Optimized for Cloud Logging and BigQuery ingestion.

5. FinOps & Cost Tracking

• Cost Estimation: Automated cost estimation for BigQuery (Query/Load), GCS (Storage/Upload), and Pub/Sub (Publishing).
• FinOpsLabels: Standardized GCP resource labeling for precise cost allocation.
• Monitoring Integration: Seamless integration with MigrationMetrics for real-time cost visibility in audit logs.
• Trackers:
  • BigQueryCostTracker: Estimates costs based on bytes billed and slot usage.
  • CloudStorageCostTracker: Estimates storage costs and upload fees.
  • PubSubCostTracker: Estimates throughput costs with 1KB minimum billing awareness.
• Decorators: @track_bq_cost for automated tracking of BigQuery jobs.

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Governance & Compliance

• Zero-Bleed Policy: This library MUST NOT import apache_beam or airflow.
• Portability: Must remain compatible with any Python environment (Cloud Functions, Cloud Run, local scripts, etc.).
• Testing: All new features require unit tests in tests/unit/.

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Usage

from gcp_pipeline_core.audit import AuditTrail, ReconciliationEngine
from gcp_pipeline_core.monitoring import MetricsCollector
from gcp_pipeline_core.utilities import configure_structured_logging, generate_run_id
from gcp_pipeline_core.schema import EntitySchema, SchemaField
from gcp_pipeline_core.job_control import JobControlRepository
from gcp_pipeline_core.error_handling import ErrorHandler
from gcp_pipeline_core.finops import BigQueryCostTracker, FinOpsLabels, track_bq_cost

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Tests

python3.11 -m pytest tests/ -v
# 256 passed