mental-model

DE//AGENT currently lists 8 resources under mental-model: 8 tutorials. Everything below is derived from the live registry — counts, capabilities, verification state and security posture come from the curated records, never from marketing copy.

Documented learning objectives across these resources:

• Name the four dimensions of data quality — freshness, completeness, validity, consistency — via Name the four dimensions of data quality
• Map each dimension to a concrete signal — SLA, null rate, range, uniqueness — via Name the four dimensions of data quality
• Rank failure modes by blast radius before writing any test — via Name the four dimensions of data quality
• Name the four causes of a slow batch job: skew, partition count, spill, over-scan — via The batch processing mental model
• Explain what a shuffle does to a hot key — via The batch processing mental model
• State the idempotent-write invariant for re-runnable batch jobs — via The batch processing mental model
• Name the three properties a consumer experiences — freshness, completeness, correctness — via The data reliability mental model — freshness, completeness, correctness
• Read a data contract's SLA section as the table's reliability promise — via The data reliability mental model — freshness, completeness, correctness

16 more objectives live on the individual resource pages.

These resources map to stages: Data Quality (1), Data Processing (1), Reliability (1), Lakehouse (1), Orchestration (1), Python + SQL (1), Warehousing (1), Agentic Data Engineering (1). Follow the four-phase roadmap to place this topic inside a progression instead of learning it in isolation.

Every entry, its shape and its trust state — sorted by the registry's composite score. This table is the honest version of a marketing comparison: no sponsored placements, no unverifiable claims.

How scores are computed: source quality score.

Highest composite score first:

1. Name the four dimensions of data quality score 34.15 — beginner
2. The batch processing mental model score 34.15 — beginner
3. The data reliability mental model — freshness, completeness, correctness score 34.15 — beginner

Scores rank provenance, completeness and trust — read how verification works before adopting anything.

Start here if you're new: Name the four dimensions of data quality, The batch processing mental model, The data reliability mental model — freshness, completeness, correctness, The lakehouse mental model — lake vs warehouse vs table formats vs zones.

• Name the four dimensions of data quality — Build the mental model before writing a single test: freshness, completeness, validity, consistency. See each dimension in a real registry artifact — the SLA part of a contract, th… details
• The batch processing mental model — Batch processing in partition terms. Learn what a partition, a shuffle and a spill are, why "the job is slow" resolves into four named causes — skew, partition count, spill, over-s… details
• The data reliability mental model — freshness, completeness, correctness — Build the map before writing a single check: a consumer experiences a table as three properties — freshness, completeness, correctness — declared as an SLA in a data contract, enfo… details
• The lakehouse mental model — lake vs warehouse vs table formats vs zones — Build the map before touching a table: object storage is files with no structure for trust or ownership, a table format (Iceberg or Delta) adds snapshots, manifests, hidden partiti… details

Adjacent topics: tutorial (8 shared), beginner (7 shared).

Study path: the roadmap places this topic in a progression · the scoring model explains the numbers · the trust rule catalog shows every security check these entries passed through.