Catalog
concept#Data#Integration#Observability#Platform

Data Orchestration

Coordination and control of data flows, processing steps, and dependencies across heterogeneous systems.

Data orchestration coordinates data flows, processing steps, and dependencies across heterogeneous systems to deliver reliable end-to-end pipelines.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Apache Airflow as scheduler/orchestratorApache Kafka for event streamingKubernetes as execution and resource manager

Principles & goals

Explicit orchestration logic instead of distributed ad-hoc controlIdempotence and observable execution stepsSeparation of control plane and data processing
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Single point of failure in the orchestrator
  • Inconsistencies from incorrect pipeline versioning
  • Excessive centralization reduces flexibility
  • Version pipelines and transformations
  • Build observability and lineage from the start
  • Define clear retry and SLA strategies

I/O & resources

  • Data sources (databases, message brokers, files)
  • Processing logic (jobs, containers, functions)
  • Operational rules and SLAs
  • Transformed, validated target artifacts
  • Monitoring and audit metrics
  • Lineage and version information of the pipeline

Description

Data orchestration coordinates data flows, processing steps, and dependencies across heterogeneous systems to deliver reliable end-to-end pipelines. It defines control logic, scheduling, error handling, and operational practices for both batch and streaming workloads. Implementations integrate monitoring, pipeline versioning, and data-quality policies to ensure predictable, repeatable delivery.

✔

  • Predictable, repeatable pipelines
  • Improved fault tolerance and retry strategies
  • Clearer responsibilities and traceability

✖

  • Increased operational overhead from controllers and schedulers
  • Complexity with heterogeneous data sources and formats
  • Potential latency due to central coordination

  • Throughput (events/s or bytes/s)

    Measures amount of data processed per unit time.

  • End-to-end latency

    Time from event arrival to complete processing and storage.

  • Error rate and Mean Time To Recover (MTTR)

    Share of failed executions and average recovery time.

Apache Airflow for batch orchestration

Airflow controls DAG-based ETL jobs, scheduling and retry logic in many organizations.

Flink connectors for streaming orchestration

Apache Flink combines stream processing with checkpointing and state management for orchestrated pipelines.

Kubernetes as an execution platform

Kubernetes provides resource management, scheduling and lifecycle for orchestrated data jobs.

1

Analyze data flows, define SLAs, choose orchestrator

2

Design pipelines, idempotence and checkpoint strategies

3

Introduce automated deployment, monitoring and backfill processes

⚠️ Technical debt & bottlenecks

  • Hard-coded endpoints and credentials
  • Lack of modularization of transformation logic
  • Outdated monitoring and alerting rules
Network throughputState managementIO-bound transformations
  • Using the orchestrator as a manual task UI only
  • Stateful workloads without checkpointing in streaming
  • Bundling all transformations in a single task
  • Underestimating operational costs
  • Ignoring rollback and backfill scenarios
  • Missing isolation between test and production pipelines
Data architecture and ETL conceptsOperating distributed systemsMonitoring, alerting and debugging
Data consistency and lineageOperational stability and recoverabilityScalability for volume and latency
  • • Limited infrastructure resources
  • • Regulatory requirements for data residency
  • • Heterogeneous source system interfaces