pipeline-engineering
Skill0 starsChanged 44 days agoMITView on GitHub
Debbie's data engineering playbook, covering incremental, idempotent, audited pipelines on Spark and SQL platforms. Includes watermark and CDC loading, Delta/Iceberg MERGE, deduplication, schema drift handling, audit logging, a notebook template, a code-review checklist of anti-patterns, and platform notes for Databricks, Azure Data Factory, AWS Glue/EMR, GCP Dataproc/Composer and Snowflake/dbt. Use when building, reviewing or debugging data pipeline code.
In this repository4 other files it gives its agents.
- data-qualitySkill
- ml-engineeringSkill
- setupSkill
- squadSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 26
Failure and re-run: if this fails halfway and re-runs, is the result identical?
- Distinctive: line 29
Write the answers as a comment block at the top of the notebook or job.
- Very specific: line 42
Batch vs streaming: start with incremental batch (availableNow trigger or a scheduled job).
- Distinctive: line 44
Python vs SQL: use whatever the repo already uses for that layer.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Pipeline Engineering Playbook
Standards for anything that moves or transforms data. The platform-specific details are in references/. Open only what you need.
| Need | Open |
| Incremental loads, MERGE, CDC, dedupe, SCD2, schema drift | references/incremental-patterns.md |
| Starting a new notebook or job | references/notebook-template.md |
| Reviewing existing code | references/review-checklist.md |
| Databricks (Workflows, Asset Bundles, Unity Catalog, Auto Loader, Lakeflow) | references/platforms/databricks.md |
| Azure Data Factory / Synapse / Fabric pipelines | references/platforms/azure-data-factory.md |
| AWS Glue, EMR, Step Functions, MWAA | references/platforms/aws.md |
| GCP Dataproc, BigQuery, Cloud Composer | references/platforms/gcp.md |
| Snowflake and dbt | references/platforms/snowflake-dbt.md |
The five questions before writing any pipeline
- Grain and key: what does one row mean, and what is the business key that makes it unique?
- Change detection: how do we know what's new? A watermark column, change data feed, file arrival, or a CDC log.
- Late and corrected data: how late can records arrive, and can they be updated or deleted at the source? The answers set the lookback window and whether MERGE handles deletes.
- Failure and re-run: if this fails halfway and re-runs, is the result identical? (It must be.)
- Consumers and SLA: who reads the output, how fresh does it need to be, and what's the cost budget?
Write the answers as a comment block at the top of the notebook or job. That's the design record.
Layer contracts (medallion — rename to conventions.layers)
| Layer | Contract | Write mode |
| Bronze / raw | Source-faithful and append-only, plus ingestion audit columns. Strings are OK. | Append (Auto Loader / COPY INTO / file-based incremental) |
| Silver / standardised | Typed, deduplicated, conformed names, one row per business key (or SCD2), DQ-gated | MERGE on the business key |
| Gold / curated | Business-level aggregates or features, stable schema, documented | MERGE, or replaceWhere on the affected partitions only |
Decision rules Debbie applies
- Overwrite vs MERGE: MERGE by default. Replace partitions (
replaceWhere/ dynamic partition overwrite) only when a whole partition is always recomputed from source. A full-table overwrite needs a yes from the user. - Batch vs streaming: start with incremental batch (
availableNowtrigger or a scheduled job). Go to continuous streaming only when the SLA is under about 15 minutes. - Partitioning: partition only on low-cardinality columns with more than ~1 GB per partition. Otherwise use liquid clustering / Z-order on filter columns. Over-partitioning is the top cause of small files.
- Python vs SQL: use whatever the repo already uses for that layer. Consistency beats preference.
- Per-tenant processing: one distributed job that partitions by tenant, not N sequential jobs. When isolation is required, use orchestrator fan-out (ForEach / dynamic task mapping / for-each task) with a bounded parallelism setting.
Definition of done
- Re-run of the same window produces identical output (tested or reasoned in writing)
- Watermark / checkpoint advances only after a successful write
- Audit row written for start and success/failure with counts
- Vera's gate called before publishing to the next layer
- No secrets, no hardcoded environment values
- The orchestrator wiring (job, pipeline or DAG) is updated or listed as a follow-up
- Hand-off block given
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