Architecture Documentation and Communication Questions

Technical Decision Record (TDR) — Template
Title: [Short decision name]
Date: [YYYY-MM-DD]
Status: [Proposed / Approved / Rejected]
Authors: [Name(s)]
Context & Problem Statement:

• Brief context (business, system) and the specific problem to solve.
  Decision:
• Chosen option (concise).
  Alternatives Considered:
• Alt A: short description
• Alt B: short description
  Evaluation Criteria:
• Performance (latency, throughput)
• Cost (infra, licensing, ops)
• Team skill / ramp-up
• Operational complexity (monitoring, debugging)
• Ecosystem & integration (connectors, SQL support)
• Reliability & fault tolerance
  Decision Rationale:
• Mapping of alternatives to criteria; highlight trade-offs and measurable reasons.
  Migration Plan:
• Phases (pilot, parallel-run, cutover), migration steps, data validation, timeline, responsible owners.
  Rollback Plan:
• Conditions that trigger rollback, rollback steps, data reconciliation approach, estimated RTO/RPO.
  Impacted Stakeholders:
• Teams, SLAs, downstream consumers, compliance, DBAs, SRE.
  Open Questions & Risks:
• List unknowns and mitigations.
  References:
• Links to benchmarks, docs, runbooks.

Filled Example — Spark vs Flink for Batch + Streaming ETL
Title: Choose ETL runtime for Unified Pipelines (Spark vs Flink)
Date: 2025-11-01
Status: Approved
Authors: Data Platform Team

Context & Problem Statement:
We need a single runtime to support nightly batch jobs (TB-scale) and low-latency streaming joins for enrichment (<5s) for analytics and ML features. Current stack is ad-hoc; goal: reduce maintenance, improve SLA, and enable feature engineering.

Decision:
Adopt Apache Spark Structured Streaming as primary runtime; introduce Flink for a narrow set of low-latency stateful streaming needs where Spark cannot meet latency requirements.

Alternatives Considered:

• Spark only (Structured Streaming + batch)
• Flink only (batch via Flink or Beam)
• Hybrid: Spark primary + Flink for strict low-latency

Evaluation Criteria & Summary:

• Performance: Spark excels at batch throughput and micro-batch streaming; Flink has lower end-to-end latency and better event-time state handling. → Tie, Flink wins for sub-second to few-second latency.
• Cost: Spark better utilizes EMR/Dataproc/YARN autoscaling for batch; Flink needs more always-on resources for low-latency stateful jobs. → Spark wins.
• Team skill: Team has 70% Spark experience, 10% Flink. Ramp cost for Flink significant. → Spark wins.
• Operational complexity: Flink operationally heavier (job manager, state backend tuning). → Spark wins.
• Ecosystem: Spark has broader connectors and SQL/ML integration. → Spark wins.

Decision Rationale:
Because majority workload is large-scale batch and micro-batch streaming, and team skill/cost favor Spark, choose Spark as primary. Reserve Flink for <5s stateful streaming/complex event processing where Spark cannot meet latency or exactly-once semantics. This hybrid minimizes cost and risk while enabling required latency where necessary.

Migration Plan:

1. Pilot (2 weeks): Replatform 3 representative batch jobs to Structured Streaming/batch on Spark; validate outputs.
2. Parallel-run (4 weeks): Run Spark pipelines in parallel with legacy. Add validation checks and row-level diffs.
3. Low-latency area: Implement one Flink job for strict latency use-case, run alongside Spark enrichment as fallback.
4. Cutover: Switch consumers once parity and monitoring are green. Owners: Platform Eng (migration), Data Owners (validation).

Rollback Plan:

• If data drift or SLA regressions > threshold (e.g., 1% bad rows or 30% latency increase), revert consumers to legacy pipelines or run Spark job in legacy mode.
• Keep previous cluster snapshots and use CDC/state checkpoints to replay events. Estimated RTO: 2 hours for batch; 15 min for streaming fallback.

Impacted Stakeholders:

• Data Platform, Analytics, ML Feature Teams, SRE, Compliance, Product owners.

Risks & Mitigations:

• Risk: Unexpected latency regressions — mitigation: keep Flink fallback and detailed SLIs.
• Risk: Skill gap — mitigation: training, pair-programming, hire 1 senior Flink engineer.

References:

• Internal benchmark results (link), Spark Structured Streaming guide, Flink stateful processing docs.

Situation: We needed a repeatable way to capture architecture trade-offs for a large data platform so engineers could move fast without fragmenting standards or creating hidden technical debt.

Process overview (what I’d implement):

• ADR workflow: propose → review → approve → implement → monitor → revisit/deprecate.
• ADR template (short): Title, Context, Decision, Alternatives considered (with pros/cons), Non-functional impact (cost, latency, throughput, security), Rollout plan, Rollback plan, Owners, Acceptance criteria, Review date, Links to design docs/PRs.

Who is involved:

• Author: proposing Data Engineer or Architect.
• Core reviewers: Data Platform Architect, Senior Data Engineer, SRE/Platform, Security/GDPR/Compliance rep.
• Stakeholders: Data Product Owner(s), Analytics lead, Cost/FinOps rep, PM.
• Final approval: Data Platform Architecture Board (small cross-functional group) or delegated architect for low-risk ADRs.

Approval criteria (must be explicit):

• Clear problem statement and measurable goals (SLAs, cost targets).
• Alternatives evaluated against criteria (cost, latency, maintainability, security, operational burden).
• Automated tests and observability planned.
• Rollout and rollback plans defined with canary thresholds and success/failure metrics.
• Identified owners and runbook for incidents.
• Budget/FinOps sign-off if > threshold.

Rollout & rollback:

• Phased rollout: dev → staging → canary (small percent of traffic) → full.
• Use feature flags or namespace routing for data flows when possible.
• Pre-defined success metrics (error rate, throughput, schema compatibility, downstream job failure rate).
• Automated monitoring + alerting; if thresholds breached, automated or manual rollback triggers.
• Post-deploy verification checklist and 24–72h stabilization window.

Decision visibility:

• Store ADRs in a versioned repo (docs/adr) and index by tags; link from project repositories and tickets.
• Expose summary dashboard: recent ADRs, status (proposed/approved/implemented/deprecated), next review date.
• Announce major ADRs in weekly architecture sync and record meeting minutes.

Revisit / deprecate:

• Every ADR includes an owner and a review cadence (default 12 months or sooner on incident).
• Maintain a deprecation pipeline: notify stakeholders, provide migration path, set sunset date, monitor adoption.
• Trigger conditions for revisit: incidents, major cost increase, new regulatory requirements, or better alternative available.
• Keep deprecated ADRs archived but searchable with reason and migration artifacts.

Governance and cultural practices:

• Lightweight governance: low-friction for small changes, stricter review for high-impact decisions (cost, schema, PII).
• Encourage short, focused ADRs and pair reviews to spread knowledge.
• Track technical debt from ADRs in backlog and prioritize in roadmap.

This balances speed and safety: decisions are documented, measurable, discoverable, and owned—so trade-offs are explicit and reversible.