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Why Watchmen

Why build a fit-for-purpose data architecture instead of adopting a mainstream stack​

Industry focus​

  • Big data engines: Apache Spark, Apache Flink
  • Ecosystem: Modern Data Stack — https://www.moderndatastack.xyz/
  • Common belief: these are the “standard solutions” for data processing

Hidden complexity of toolchains​

  • Each stage demands a specialized tool and configuration
  • Typical components:
    • Batch processing systems
    • Stream processing engines
    • Data governance platforms
    • Data modeling tools
    • Configuration management systems
    • Application layer for data products
  • Result: an extremely complex technical architecture that is costly to operate

Pain heard from business teams​

  • “We have every tool but still cannot deliver analysis.”
  • Significant effort is spent maintaining and integrating open‑source and commercial tools, not producing insights

What organizations really need​

  • A platform that lets business users focus on business, not tool configuration
  • A platform that enables efficient cross‑role collaboration, not blocked by toolchain complexity
  • A platform that makes data easy to use across the team, not constrained by individual tool limits

Watchmen approach​

  • Unified platform that integrates ingestion, modeling, orchestration, governance, and analysis
  • Role‑based workspaces for clarity and collaboration: Admin, Console, DQC, Indicator
  • Visual orchestration and monitoring to shorten time‑to‑value

No‑code development lowers cognitive load​

  • Configure ingestion, modeling, pipelines, and analytics via GUI
  • Reduce onboarding time for business and data roles
  • Standardize patterns to avoid bespoke scripts and one‑off tooling

Serverless operations lower management cost​

  • Deploy components with serverless options to reduce ops overhead
  • Scale automatically and pay per use; minimize idle resource costs
  • Centralized configuration and observability replacing per‑service maintenance
Key takeaways
  • Reduce integration overhead and operational complexity
  • Improve collaboration and delivery speed across data roles
  • Build a fit‑for‑purpose architecture aligned with business outcomes
  • Lower cognitive load with no‑code workflows
  • Lower management cost with serverless operations