Catalog
concept#Data#Integration#Architecture#Observability

Data Pipeline

Structured sequence of processes for ingesting, transforming and delivering data to targets such as analytics, storage or applications.

A data pipeline is an orchestrated sequence of processes for ingesting, transforming and loading data from source systems to targets.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Message brokers (e.g., Kafka)Storage solutions (e.g., S3, data warehouse)Orchestration tools (e.g., Airflow)

Principles & goals

Single responsibility: structure pipelines by clear responsibilities.Idempotence: design steps so retries do not produce incorrect results.Observability: plan monitoring, logging and tracing from the start.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data inconsistencies from incomplete error handling.
  • Excessive coupling between pipelines and source systems.
  • Scaling bottlenecks due to unsuitable infrastructure planning.
  • Ensure versioning of data and pipelines.
  • Implement schema validation and data quality gates.
  • Standardize observability (metrics, logs, traces).

I/O & resources

  • Source systems (databases, APIs, logs)
  • Schema and quality rules
  • Orchestration and runtime environment
  • Transformed datasets in target stores
  • Monitoring and audit logs
  • Notifications and alerts on failures

Description

A data pipeline is an orchestrated sequence of processes for ingesting, transforming and loading data from source systems to targets. It provides automation, monitoring and error handling to enable reliable, reproducible data flows for analytics, reporting and applications. Common components include ingestion, processing, orchestration and storage.

✔

  • Automated, reproducible data flows reduce manual effort.
  • Consistent transformations enable reliable analytics.
  • Scalable architecture allows handling growing data volumes.

✖

  • Operation and observability introduce additional effort.
  • Complex pipelines increase debugging and maintenance costs.
  • Latency requirements can constrain architectural choices.

  • Throughput (records/s)

    Number of records processed per second.

  • Latency (end-to-end)

    Time from ingestion to availability in the target system.

  • Error rate

    Share of failed processing operations.

Batch ETL for financial reports

Weekly aggregated transactions are extracted, validated and loaded into a data warehouse.

Streaming pipeline for usage metrics

Real-time events are processed, computed and written to time-series stores.

Hybrid pipeline for IoT sensors

Short-term edge aggregation combined with central batch processing for long-term storage.

1

Analyze requirements and data sources

2

Define target architecture and component interfaces

3

Build a proof-of-concept for core components

4

Integrate automated tests and monitoring

5

Migrate incrementally and enter production operation

⚠️ Technical debt & bottlenecks

  • Hard-coded paths and credentials in pipelines.
  • Missing automated tests for transformation logic.
  • Insufficient documentation of interfaces and schemas.
I/O bandwidthNetwork latencyCompute resources
  • Solving real-time requirements with pure batch design.
  • Uncontrolled duplication of transformation logic across pipelines.
  • Lack of test data and validation rules before going live.
  • Underestimating effort for observability and operations.
  • Ignoring schema evolution and compatibility.
  • Premature optimization instead of a clear, simple initial implementation.
Data modeling and ETL/ELT principlesKnowledge of streaming and batch processingOperations, monitoring and error handling
Availability and fault toleranceData quality and governanceScalability and cost control
  • • Privacy and compliance requirements
  • • Source system constraints (rate limits)
  • • Budget constraints for infrastructure