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method#Data#Integration#Architecture#Platform

ETL Design

Structured approach to planning extraction, transformation and loading of data between systems with a focus on reliability and maintainability.

ETL design is a structured approach to extracting, transforming, and loading data between systems.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Databases (Postgres, MySQL, Oracle)Message brokers (Kafka, Pulsar)Orchestration tools (Airflow, NiFi)

Principles & goals

Explicit separation of extraction, transformation and loadingDesign for observability and repeatabilityEmbed data quality and governance
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data loss or corruption due to faulty transformations
  • Undetected schema drift due to missing tests
  • Operational overload from non-scaled jobs
  • Automated tests for transformation rules
  • Idempotent load processes and sensible retries
  • Embed schema and data quality checks early

I/O & resources

  • Source data (DB dumps, APIs, events)
  • Mapping and validation rules
  • Permissions and credentials
  • Target datasets in data warehouse or data lake
  • Monitoring and audit logs
  • Error reports and SLA metrics

Description

ETL design is a structured approach to extracting, transforming, and loading data between systems. It defines architecture, data flow, error handling, scalability, data quality and governance, as well as interfaces and batch or streaming strategies. The goal is reliable, traceable and maintainable data pipelines with monitoring, performance tuning and security.

✔

  • Consistent data landscape for analytics
  • Clear error handling and recovery processes
  • Scalable pipelines for growing data volumes

✖

  • Initial implementation effort
  • Complexity with heterogeneous data sources
  • Latency in batch-heavy processes

  • Throughput (events/s or GB/h)

    Measures processed data volume per time unit.

  • End-to-end latency

    Time from ingestion to data availability in target.

  • Error rate / failed jobs

    Share of failed or faulty loads.

ETL for sales analytics

Enterprise pipeline consolidating POS data, cleansing it and loading into a reporting warehouse.

SaaS onboarding integration

Automated ETL jobs synchronize user and billing data from a SaaS provider.

Streaming for fraud detection

Real-time pipeline transforms events and writes suspected cases to a monitoring dashboard.

1

Requirements analysis: define sources, targets, SLAs

2

Design architecture: batch vs streaming, central vs decentralized

3

Implementation: implement transformation logic and tests

4

Operate: introduce monitoring, alerting and runtime optimization

⚠️ Technical debt & bottlenecks

  • Insufficient test coverage for transformations
  • Hard-coded mapping rules in jobs
  • Old, non-scalable orchestration workflows
Network/IOSchema driftTransformation complexity
  • Full recomputation of large datasets for small fixes
  • Storing credentials in plaintext
  • Ignoring schema changes in source systems
  • Underestimating operationalization costs
  • Bundling complex transformation logic into a single step
  • No rollback strategy for failed loads
Data modeling and SQLKnowledge of ETL tools and orchestrationUnderstanding of data quality and governance
Data volume and throughput requirementsLatency requirements for consumersData quality and governance requirements
  • • Limited bandwidth to source systems
  • • Privacy and compliance requirements
  • • Existing legacy interfaces