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Data Engineering Workflows

Version: 1.0.0 Status: Production Last Updated: December 28, 2025 Category: Data Engineering

Workflow Overview​

This document provides a comprehensive library of data engineering workflows for the CODITECT platform. These workflows cover ETL/ELT pipelines, data quality management, real-time streaming, data migration, and data warehouse automation. Each workflow includes detailed phase breakdowns, inputs/outputs, and success criteria to ensure reliable data operations.

Inputs​

InputTypeRequiredDescription
source_configobjectYesData source connection configuration
schema_definitionobjectYesSchema for source and target data
transformation_rulesarrayYesData transformation specifications
quality_rulesarrayNoData quality validation rules
schedulestringNoCron expression for scheduled runs
destination_configobjectYesTarget destination configuration

Outputs​

OutputTypeDescription
job_idstringUnique identifier for the pipeline run
records_processedintegerNumber of records processed
records_failedintegerNumber of records that failed validation
quality_scorefloatOverall data quality score (0-100)
execution_metricsobjectPerformance metrics (duration, throughput)
lineage_infoobjectData lineage and transformation history

Phase 1: Data Ingestion​

Initial phase extracts data from source systems:

  1. Source Connection - Establish connection to data sources
  2. Schema Discovery - Detect and validate source schema
  3. Data Extraction - Extract data in batches or streams
  4. Initial Validation - Validate format and basic integrity
  5. Raw Storage - Store raw data in landing zone

Phase 2: Transformation & Quality​

Core phase transforms data and validates quality:

  1. Data Cleaning - Handle nulls, duplicates, inconsistencies
  2. Transformation - Apply business logic transformations
  3. Enrichment - Join with reference data
  4. Quality Validation - Run quality checks
  5. Quarantine Handling - Route failed records

Phase 3: Loading & Cataloging​

Final phase loads data to targets and updates metadata:

  1. Target Loading - Load transformed data to destination
  2. Index Updates - Update search and query indexes
  3. Catalog Update - Update data catalog with new datasets
  4. Lineage Recording - Record data lineage
  5. Notification - Alert stakeholders of completion

Data Engineering Workflow Library​

1. etl-pipeline-workflow​

  • Description: Complete ETL pipeline from source extraction to target loading
  • Trigger: /run-etl or schedule
  • Complexity: complex
  • Duration: 15-60m
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: data-engineer, data-architect
    • Commands: /extract, /transform, /load
  • Steps:
    1. Source connection - data-engineer - Connect to source systems
    2. Data extraction - data-engineer - Extract data in batches
    3. Transformation - data-engineer - Apply transformation rules
    4. Quality check - data-architect - Validate data quality
    5. Loading - data-engineer - Load to target destination
  • Tags: [etl, pipeline, batch, data-engineering]

2. data-quality-workflow​

  • Description: Comprehensive data quality validation and monitoring
  • Trigger: /data-quality-check or pipeline completion
  • Complexity: moderate
  • Duration: 5-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: data-engineer, data-analyst
    • Commands: /validate-data, /quality-report
  • Steps:
    1. Schema validation - data-engineer - Verify schema compliance
    2. Completeness check - data-engineer - Check for missing values
    3. Accuracy validation - data-analyst - Validate data accuracy
    4. Consistency check - data-engineer - Cross-reference validation
    5. Report generation - data-analyst - Generate quality report
  • Tags: [data-quality, validation, monitoring]

3. streaming-pipeline-workflow​

  • Description: Real-time data streaming pipeline with Kafka/Kinesis
  • Trigger: Continuous stream
  • Complexity: complex
  • Duration: Continuous
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: data-engineer, streaming-specialist
    • Commands: /stream-start, /stream-status
  • Steps:
    1. Stream setup - streaming-specialist - Configure stream consumers
    2. Event processing - data-engineer - Process events in real-time
    3. Windowing - streaming-specialist - Apply time windows
    4. State management - data-engineer - Manage processing state
    5. Output routing - streaming-specialist - Route to sinks
  • Tags: [streaming, kafka, real-time, event-driven]

4. data-migration-workflow​

  • Description: Large-scale data migration with validation and reconciliation
  • Trigger: /migrate-data or manual
  • Complexity: complex
  • Duration: 1h+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: data-engineer, data-architect, dba
    • Commands: /migrate, /reconcile
  • Steps:
    1. Source analysis - data-architect - Analyze source system
    2. Mapping definition - data-architect - Define source-target mapping
    3. Test migration - data-engineer - Run test migration
    4. Full migration - data-engineer - Execute full migration
    5. Reconciliation - dba - Verify data integrity
  • Tags: [migration, data-movement, reconciliation]

5. data-catalog-workflow​

  • Description: Automated data cataloging and metadata management
  • Trigger: /catalog-update or data ingestion
  • Complexity: moderate
  • Duration: 5-15m
  • QA Integration: validation: required, review: optional
  • Dependencies:
    • Agents: data-engineer, data-steward
    • Commands: /catalog-scan, /tag-data
  • Steps:
    1. Discovery - data-engineer - Scan for new datasets
    2. Profiling - data-engineer - Profile data characteristics
    3. Classification - data-steward - Classify sensitive data
    4. Tagging - data-steward - Apply metadata tags
    5. Publishing - data-engineer - Publish to catalog
  • Tags: [catalog, metadata, governance]

Success Criteria​

CriterionTargetMeasurement
Pipeline Success Rate>= 99.5%Successful runs / Total runs
Data Quality Score>= 95%Average quality score
Processing Latency< 15m batch, < 1s streamP95 latency
Data Freshness< 1h batch, < 5m streamTime since last update
Record Error Rate< 0.1%Failed records / Total records
Schema Evolution Success100%Compatible changes / Total changes

Error Handling​

Error TypeRecovery StrategyEscalation
Source connection failureRetry with backoffAlert after 3 failures
Schema mismatchQuarantine and alertAlert data steward
Transformation errorLog and skip recordAlert on high error rate
Quality validation failureRoute to quarantineAlert data owner
Target write failureRetry with idempotencyAlert on persistent failure

Maintainer: CODITECT Core Team Standard: CODITECT-STANDARD-WORKFLOWS v1.0.0