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tutorial
130 registry resources · 130 tutorials · roadmap: Data Quality, Observability, Reliability, Cloud, Lakehouse, Python + SQL, Foundations, Streaming, System Design, Agentic Data Engineering, Orchestration, Warehousing, Data Processing
What this topic covers
DE//AGENT currently lists 130 resources under tutorial: 130 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.
What you can learn here
Documented learning objectives across these resources:
- Write all six parts of a contract for a real dataset — via Author your first data contract
- Specify freshness and completeness numbers in the SLA — via Author your first data contract
- Name the CI test that fails when the contract is violated — via Author your first data contract
- Attribute compute cost to a pipeline run — via Compute unit economics for a pipeline
- Define a unit metric that resists regressions — via Compute unit economics for a pipeline
- Set an alert on cost-per-run, not on total spend — via Compute unit economics for a pipeline
- Read the scanned-bytes metric per query — via Cut Athena bytes scanned with partitions and projection
- Apply partition projection for high-cardinality columns — via Cut Athena bytes scanned with partitions and projection
378 more objectives live on the individual resource pages.
Where this fits in the learning roadmap
These resources map to stages: Data Quality (10), Observability (10), Reliability (10), Cloud (10), Lakehouse (10), Python + SQL (10), Foundations (10), Streaming (10), System Design (10), Agentic Data Engineering (10), Orchestration (10), Warehousing (10), Data Processing (10). Follow the four-phase roadmap to place this topic inside a progression instead of learning it in isolation.
Compare the registry's options
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.
118 more in the full list below.
How scores are computed: source quality score.
Where to start
Highest composite score first:
- Author your first data contract score 34.15 — beginner
- Compute unit economics for a pipeline score 34.15 — beginner
- Cut Athena bytes scanned with partitions and projection score 34.15 — beginner
Scores rank provenance, completeness and trust — read how verification works before adopting anything.
Good fits
Start here if you're new: Author your first data contract, Compute unit economics for a pipeline, Cut Athena bytes scanned with partitions and projection, Decide when a Delta table needs OPTIMIZE or Z-ORDER.
Reach for these with production experience: Capstone — the guarded agent checklist, Capstone checklist — a design is done when these all hold, Interview walkthrough — design a data platform end-to-end.
Cautions and limitations
Security-flagged entries: Catalogs and ownership: who owns this table and is it safe to use? (2 flags), Incident drill: a broken upstream caught by freshness and lineage (2 flags), Lineage tooling in practice: DataHub MCP and the lineage-graph plugin (2 flags), Production gotchas: alert fatigue, missing contracts, no-data silence (2 flags). Flags mean the record declares capabilities like credential use or write access — read the security posture on each page before connecting.
Production use cases
- Author your first data contract — Draft an enforceable contract for one real dataset by walking the six parts — owner, schema, semantics, SLA, breaking-change policy, enforcement — starting from the data-contract t… details
- Compute unit economics for a pipeline — Turn cluster and warehouse billing into unit economics — cost per run, per 1M rows, per model build — so a regression shows up as a number, not a surprise invoice. The Cost Optimiz… details
- Cut Athena bytes scanned with partitions and projection — Walk one expensive Athena query through the Partition Advisor's levers in order: confirm the per-TB bill model, partition on the dominant filter column, replace MSCK REPAIR with pa… details
- Decide when a Delta table needs OPTIMIZE or Z-ORDER — Apply the Delta Optimization Advisor's decision rules to one table: read file stats for the OPTIMIZE trigger, pick Z-ORDER columns from actual query predicates, weigh liquid cluste… details
Further study
Adjacent topics: beginner (17 shared), review (13 shared), production (13 shared), cost (12 shared), capstone (12 shared), checklist (12 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.
Full resource list
| Resource | Type | Score |
|---|---|---|
| Author your first data contract | Tutorial | 34.15 |
| Compute unit economics for a pipeline | Tutorial | 34.15 |
| Cut Athena bytes scanned with partitions and projection | Tutorial | 34.15 |
| Decide when a Delta table needs OPTIMIZE or Z-ORDER | Tutorial | 34.15 |
| Deduplicate records without rewriting the table | Tutorial | 34.15 |
| Enforce contracts in CI with the schema change check | Tutorial | 34.15 |
| Express a freshness check as a metric and an alert | Tutorial | 34.15 |
| First hands-on — a correct SELECT with a justified join | Tutorial | 34.15 |
| Fix SQL lint with the SQLFluff core rule pack | Tutorial | 34.15 |
| Hold the cloud data stack mental model — storage, catalog, compute, IAM | Tutorial | 34.15 |
| Hold the observability mental model: infrastructure metrics versus data signals | Tutorial | 34.15 |
| Hold the row-versus-column mental model | Tutorial | 34.15 |