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How to Use GitScrum for Data Engineering Projects?
How to use GitScrum for data engineering projects?
Manage data engineering in GitScrum with labels for pipeline types, track ETL development through standard workflow, and document data models in NoteVault. Coordinate with analysts on requirements, track data quality issues, and manage infrastructure work. Organized data teams deliver 40% more reliable pipelines [Source: Data Engineering Research 2024].
Data engineering workflow:
- Requirements - From analysts/stakeholders
- Design - Data model, pipeline design
- Develop - Build ETL/pipeline
- Test - Data validation
- Stage - Pre-production testing
- Deploy - Production release
- Monitor - Ongoing quality
Data engineering labels
| Label | Purpose |
|---|---|
| type-etl | ETL pipelines |
| type-streaming | Real-time pipelines |
| type-analytics | Analytics models |
| type-infrastructure | Data infrastructure |
| data-quality | Quality issues |
| source-[name] | Data source |
| destination-[name] | Data destination |
Pipeline task template
## Pipeline: [name]
### Details
- Source: [system/table]
- Destination: [warehouse/table]
- Schedule: [cron/trigger]
- SLA: [freshness requirement]
### Checklist
- [ ] Schema design
- [ ] Transform logic
- [ ] Data validation
- [ ] Performance test
- [ ] Deploy to staging
- [ ] Stakeholder review
- [ ] Deploy to production
- [ ] Monitor setup
NoteVault data documentation
| Document | Content |
|---|---|
| Data catalog | Tables, columns, types |
| Data lineage | Source to destination |
| Pipeline inventory | All pipelines |
| Data dictionary | Business definitions |
| SLAs | Freshness requirements |
Columns for data projects
| Column | Purpose |
|---|---|
| Backlog | All work |
| Design | Schema, logic design |
| Development | Building |
| Testing | Data validation |
| Staging | Pre-production |
| Production | Deployed |
Data quality tracking
| Issue Type | Label |
|---|---|
| Missing data | dq-completeness |
| Wrong data | dq-accuracy |
| Late data | dq-timeliness |
| Duplicate data | dq-uniqueness |
| Format issues | dq-consistency |
Stakeholder coordination
| Stakeholder | Coordination |
|---|---|
| Analysts | Requirements, validation |
| Business | SLA definition |
| Engineering | Source system access |
| Security | Data access controls |
Pipeline dependencies
| Dependency | Handling |
|---|---|
| Source changes | Linked tasks |
| Upstream pipelines | Run order |
| Schema changes | Migration tasks |
| Infrastructure | Platform tasks |
Data testing checklist
| Test | Verify |
|---|---|
| Row counts | Expected volume |
| Null checks | Required fields |
| Range checks | Valid values |
| Referential | Key relationships |
| Freshness | SLA compliance |
Common data engineering issues
| Issue | Solution |
|---|---|
| Undocumented pipelines | NoteVault requirement |
| Quality issues | Monitoring tasks |
| Long runs | Performance tasks |
| Failed jobs | Incident workflow |
Data team metrics
| Metric | Track |
|---|---|
| Pipeline reliability | % successful runs |
| Data freshness | SLA compliance |
| Quality issues | Open DQ tasks |
| Delivery time | Task cycle time |