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Technology Competency Model & Stop Boundaries

Why technology is in this program

Technology is included only when it makes a DMBOK concept easier to understand, remember, distinguish, diagnose, apply, or prove. The learning target remains Data Management/Data Governance.

Four competency depths

The detailed technology reference uses progressively stronger competency expectations rather than “watched a course = learned it.” For any required technology, readiness should eventually include:

  1. Recognize / explain — know what the technology/object is and why it matters.
  2. Use with guidance — complete a bounded task while understanding each important step.
  3. Modify / diagnose — change inputs/conditions and interpret common failures.
  4. Rebuild / transfer — recreate the learning-critical portion from a blank editor and choose the approach in a changed Meridian scenario.

Not every tool needs the same final depth. SQL/pandas receive more working fluency than optional tools such as OpenMetadata or Metabase.

Technology-selection test

Before adding a technology, ask:

Does this technology make a DMBOK concept easier to understand, remember, distinguish, diagnose, apply, or prove?

If the answer is no, omit it.

High-value technical concepts

The roadmap gives priority to practical skills such as:

  • SQL SELECT, filtering, joins, grouping/aggregation, keys/constraints, views, role-aware queries, and basic transactions;
  • relational schema/table/key/constraint understanding;
  • metadata/schema inspection;
  • profiling, null/duplicate/reference checks and reconciliation;
  • CSV/JSON/Parquet literacy;
  • practical APIs where a chapter needs them;
  • Python/pandas for controlled profiling, transformation, validation and repeatability;
  • Git for versioned evidence and reproducibility.

Stop boundaries

The roadmap intentionally stops before:

  • database internals/tuning, clustering, replication administration, and production high availability;
  • production ETL/ELT pipeline architecture and orchestration;
  • Spark/distributed data engineering specialization;
  • Kubernetes/complex cloud-platform operations;
  • advanced software engineering/framework development;
  • algorithm-heavy computer science;
  • machine-learning/model-building specialization;
  • tool feature memorization detached from a governance/data-management use case.

Independence standard

A lab script is scaffolding. The learner must be able to:

  • annotate important logic;
  • modify it safely;
  • predict output before running;
  • rebuild the learning-critical portion;
  • explain the business/Data Management reason for the work.

What counts as evidence of technology readiness

Depending on the skill:

  • a small original query/script;
  • a modified exercise with predicted result;
  • an explanation of schema/object metadata;
  • a before/after profiling result;
  • a versioned Git commit;
  • a diagnostic repair;
  • a changed Meridian scenario handled without replaying the tutorial.

Source: Technology Competency Roadmap and Detailed Technology Competency Reference.