Catalog
concept#Data#Platform#Integration#Observability

Data Engineering

Discipline for designing, implementing and operating data pipelines and platforms that deliver reliable data for analytics and applications.

Data engineering is the discipline of designing, building and operating data pipelines and platforms that collect, process and deliver reliable data for analysis and applications.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Message brokers (e.g. Kafka)Data stores (e.g. data lake, object storage)Orchestration tools (e.g. Airflow)

Principles & goals

Treat data as a productPromote automation and versioningEnsure end-to-end observability
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Outdated data pipelines cause inconsistent results
  • Insufficient data quality control leads to bad decisions
  • Lack of observability hampers troubleshooting
  • Provide data as a product with owners
  • Automated tests and CI/CD for pipelines
  • Clear metadata and schema management

I/O & resources

  • Source systems and raw data
  • Schemas, metadata and SLAs
  • Infrastructure for processing and storage
  • Cleaned, versioned data products
  • Monitoring and quality metrics
  • Documented data lineage and metadata

Description

Data engineering is the discipline of designing, building and operating data pipelines and platforms that collect, process and deliver reliable data for analysis and applications. It covers ingestion, transformation, storage, metadata and operational concerns such as observability and data quality. Teams focus on scalability, maintainability and reproducibility.

✔

  • Improved data reliability and reproducibility
  • Faster delivery of analytical insights
  • Scalable, reusable data pipelines

✖

  • High initial implementation effort
  • Complexity in governance and data privacy
  • Higher demand for specialized skills

  • Pipeline latency

    Time between data ingestion and availability in target system.

  • Error rate per run

    Share of failed pipeline executions against all executions.

  • Data quality rules passed

    Percentage of records that pass defined quality checks.

Enterprise analytics platform project

Consolidation of fragmented data silos into a central lakehouse with ETL and streaming pipelines.

Real-time event processing for personalization

Streaming ingest using Kafka and feature serving for personalized recommendations.

Feature store integration for ML teams

Versioned feature exports and consistent reproduction of training data across pipelines.

1

Assess current data sources and needs

2

Design architecture and governance model

3

Implement proof-of-concept for core pipelines

⚠️ Technical debt & bottlenecks

  • Temporary scripts instead of reusable components
  • No versioning of data pipelines
  • Missing automated data quality checks
Data transferSchema evolutionProcessing latency
  • Direct use of raw data in analyses without cleansing
  • Excessive normalization for analytical workloads
  • Ad-hoc feature engineering in production systems
  • Unclear ownership leads to orphaned pipelines
  • Underestimating operational costs
  • Missing tests for schema changes
Data modeling and ETL/ELT skillsProgramming and pipeline orchestrationOperational monitoring and basic SRE skills
Data quality and lineageScalability and throughputObservability and operational reliability
  • • Privacy and compliance requirements
  • • Legacy systems with limited interfacing
  • • Budget and resource constraints