concepts

DE//AGENT currently lists 4 resources under concepts: 4 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:

• Explain storage/compute separation with one managed example per layer — via Hold the cloud data stack mental model — storage, catalog, compute, IAM
• Name the IAM surface each managed service needs before the first query — via Hold the cloud data stack mental model — storage, catalog, compute, IAM
• Map any cloud data stack into one data flow — storage, catalog, compute, serve — via Hold the cloud data stack mental model — storage, catalog, compute, IAM
• Separate infrastructure metrics from data signals — freshness, volume, schema, distribution — via Hold the observability mental model: infrastructure metrics versus data signals
• Place lineage and the catalog on the observability map, next to metrics and traces — via Hold the observability mental model: infrastructure metrics versus data signals
• State what each layer answers and which tool owns it — via Hold the observability mental model: infrastructure metrics versus data signals
• Say why analytics engines read columnar files — columnar files scan less data — via Hold the row-versus-column mental model
• Place file-format decisions on the storage stage of the data engineering lifecycle — via Hold the row-versus-column mental model

4 more objectives live on the individual resource pages.

These resources map to stages: Cloud (1), Observability (1), Foundations (1), Streaming (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. Hold the cloud data stack mental model — storage, catalog, compute, IAM score 34.15 — beginner
2. Hold the observability mental model: infrastructure metrics versus data signals score 34.15 — beginner
3. Hold the row-versus-column mental model score 34.15 — beginner

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

Start here if you're new: Hold the cloud data stack mental model — storage, catalog, compute, IAM, Hold the observability mental model: infrastructure metrics versus data signals, Hold the row-versus-column mental model, Hold the streaming mental model: events, partitions, delivery semantics.

• Hold the cloud data stack mental model — storage, catalog, compute, IAM — Build the map before opening a console: object storage holds the bytes, a managed catalog describes them, and serverless compute bills for what a query scans. Walk the same shape t… details
• Hold the observability mental model: infrastructure metrics versus data signals — Two layers live on one data platform: infrastructure metrics (CPU, lag, job success) and data signals (freshness, volume, schema, distribution). This lesson separates them, places … details
• Hold the row-versus-column mental model — Before you pick a file format, understand why analytics engines read columnar files at all: row groups carrying min/max statistics let a reader prune most of the file before decodi… details
• Hold the streaming mental model: events, partitions, delivery semantics — Build the vocabulary before touching a cluster: events instead of rows, partitions instead of tables, at-least-once delivery instead of exactly-once, and the lambda/kappa decision … details

Adjacent topics: tutorial (4 shared), architecture (2 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.