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concept#Data#Analytics#Architecture#Platform

Data Engineering Lifecycle

An organizational and technical model that defines stages and responsibilities for collecting, processing, validating and delivering data across the entire pipeline.

The Data Engineering Lifecycle defines stages and practices for collecting, transforming, validating, storing, and delivering reliable data for analytics and applications.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Streaming platforms (e.g., Kafka)Orchestration tools (e.g., Airflow)Data warehouses and data lakes

Principles & goals

Treat data as a productCareful schema and contract managementAutomated validation and observability
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Insufficient data quality leading to wrong decisions
  • Missing lineage impedes compliance and root-cause analysis
  • Excessive complexity increases operational cost
  • Early schema contracts and versioning
  • Automated tests and data quality gates
  • Implement observability and lineage from the start

I/O & resources

  • Source systems and data feeds
  • Schemas, contracts and requirement documents
  • Infrastructure for storage and processing
  • Cleaned and transformed data products
  • Monitoring and quality reports
  • Lineage and audit metadata

Description

The Data Engineering Lifecycle defines stages and practices for collecting, transforming, validating, storing, and delivering reliable data for analytics and applications. It clarifies responsibilities across ingestion, processing, data quality, orchestration, lineage, governance and operational monitoring. The model helps teams balance scalability, maintainability and data quality across pipelines.

✔

  • Improved data quality and trust in reports
  • Scalable and auditable data pipelines
  • Clearer responsibilities and faster troubleshooting

✖

  • Requires upfront effort for setup and governance
  • Complexity grows with the number of data sources
  • Not all legacy data can be easily standardized

  • Pipeline latency

    Measure of time between data ingestion and availability in the target system.

  • Data quality error rate

    Share of records failing validation rules.

  • Throughput (records/s)

    Number of records processed per second in the pipeline.

Established batch ETL in a retail company

Daily aggregation of sales data, dedicated quality checks and a BI schema for reporting.

Streaming architecture for telemetry data

Low-latency pipeline with event streaming, materialized views and monitoring.

Data quality framework in a financial product

Rule-based validations, SLA-driven alerts and data lineage for auditability.

1

Situation analysis and stakeholder alignment

2

Define standards (schemas, quality SLAs, contracts)

3

Build prototype pipeline and validation workflows

4

Integrate monitoring, lineage and alerts

5

Rollout, training and incremental improvement

⚠️ Technical debt & bottlenecks

  • Outdated ad-hoc scripts instead of reusable components
  • No versioning of transformations
  • Insufficient test coverage for edge cases
Single point of failure in orchestrationNetwork or storage I/O bottlenecksLack of test coverage for data quality
  • Directly writing unknown raw data into reporting schema
  • Skipping validation stages to speed up delivery
  • Missing documentation of schema changes
  • Underestimating costs for storage and I/O
  • Introducing lineage mechanisms too late
  • Omitting monitoring until issues arise
ETL/ELT development and SQL skillsUnderstanding of data modeling and schema designKnowledge in orchestration, observability and testing
Scalability of data processingData quality and trustworthinessTraceability and compliance (lineage)
  • • Existing legacy schemas and dependencies
  • • Budget and operational costs for infrastructure
  • • Compliance and data protection requirements