Design Pattern - Generic - Control Framework
Purpose
Section titled “Purpose”This Design Pattern describes the reasoning for, and functionality provided by, a control framework for data logistics.
Motivation
Section titled “Motivation”Data solutions rely on predictable, restartable, and auditable processing. Without a common control framework, individual pipelines implement their own tracking, leading to inconsistent failure handling, gaps in auditability, and higher operational cost. A shared control framework centralizes state, logging, alerting, and restart semantics so every pipeline behaves consistently.
Applicability
Section titled “Applicability”This pattern is only applicable for every process in the data solution.
Structure
Section titled “Structure”The control framework typically provides:
- Orchestration and state: job definitions, dependencies, run states, and restart markers to guarantee idempotent runs.
- Audit and lineage: capture of row counts, checksums, timestamps, source/target identifiers, and lineage links for traceability.
- Exception handling hooks: standard outcomes for continue/reject/abort, plus routing into exception stores.
- Observability: centralized logging, metrics, and alerts for SLA tracking and incident response.
- Access control: role-based access for operators, developers, and auditors.
Implementation guidelines
Section titled “Implementation guidelines”- Standardize job metadata (run id, batch/window, source/target, row counts, checksums) and persist it per load.
- Enforce restartability: every step should be idempotent and able to resume from the last committed state using the control metadata.
- Define clear outcomes (success, partial with exceptions, failed) and route exceptions to a common store with enough detail to remediate.
- Provide SLA tracking and alerting on latency, freshness, and failures; avoid bespoke alert logic per job.
- Separate control-plane storage from data-plane storage to reduce blast radius and simplify recovery.
- Keep the framework technology-agnostic where possible so it can front multiple data logistics/ELT engines.
Considerations and consequences
Section titled “Considerations and consequences”- A control framework adds upfront implementation effort but reduces long-term operational toil.
- Centralization creates a dependency; design for high availability and clear fallback/runbook procedures.
- Excessive coupling to a specific engine or scheduler reduces portability; keep interfaces minimal and documented.