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How to Use GitScrum for Data Engineering Teams?
How to use GitScrum for data engineering teams?
Manage data engineering in GitScrum with pipeline tracking, data project coordination, and documentation in NoteVault. Track ETL development, monitor data quality, coordinate with analytics. Data teams with structured workflow reduce pipeline incidents by 45% [Source: Data Engineering Research 2024].
Data engineering workflow:
- Request - Data need identified
- Design - Schema and pipeline
- Develop - Build pipeline
- Test - Data quality checks
- Deploy - Production rollout
- Monitor - Track metrics
- Maintain - Ongoing support
Data engineering labels
| Label | Purpose |
|---|---|
| type-data-pipeline | Pipeline work |
| type-data-model | Data modeling |
| data-ingestion | Ingestion pipeline |
| data-transformation | ETL work |
| data-quality | Quality issues |
| domain-[name] | Data domain |
Data engineering columns
| Column | Purpose |
|---|---|
| Backlog | Planned work |
| Design | Schema design |
| Development | Building |
| Testing | Validation |
| Production | Deployed |
NoteVault data docs
| Document | Content |
|---|---|
| Data catalog | All datasets |
| Pipeline inventory | All pipelines |
| Schema registry | Data schemas |
| Data contracts | SLAs and agreements |
| Runbooks | Operations guide |
Pipeline task template
## Pipeline: [name]
### Purpose
[What this pipeline does]
### Source
- System: [source system]
- Format: [format]
- Frequency: [schedule]
### Target
- System: [target system]
- Schema: [schema name]
- SLA: [freshness requirement]
### Transformations
1. [Transform 1]
2. [Transform 2]
### Data Quality Checks
- [ ] Schema validation
- [ ] Null checks
- [ ] Referential integrity
- [ ] Business rules
### Dependencies
- Upstream: [pipelines]
- Downstream: [pipelines]
### Monitoring
- Alerts: [list]
- Dashboard: [link]
Data pipeline types
| Type | Purpose |
|---|---|
| Ingestion | Source to lake |
| Transformation | Raw to clean |
| Aggregation | Summaries |
| Export | Lake to downstream |
| Streaming | Real-time |
Data quality dimensions
| Dimension | Checks |
|---|---|
| Completeness | No missing data |
| Accuracy | Correct values |
| Timeliness | Fresh data |
| Consistency | Matches source |
| Uniqueness | No duplicates |
Data contract template
## Data Contract: [dataset]
### Owner
- Team: [team]
- Contact: [@person]
### Schema
| Field | Type | Description |
|-------|------|-------------|
| [field] | [type] | [description] |
### SLA
- Freshness: [requirement]
- Availability: [requirement]
- Quality: [requirements]
### Change Policy
[How changes are communicated]
Pipeline monitoring
| Metric | Track |
|---|---|
| Run status | Success/fail |
| Run duration | Time trend |
| Data volume | Row counts |
| Freshness | Last update |
Common data challenges
| Challenge | Solution |
|---|---|
| Late data | Monitoring, alerts |
| Bad data | Quality checks |
| Schema drift | Schema registry |
| Pipeline failures | Retry, alerts |
Data team coordination
| Consumer | Coordination |
|---|---|
| Analytics | Dashboard data |
| ML | Training data |
| Product | Feature data |
| Finance | Report data |
Data incident management
| Severity | Response |
|---|---|
| Data outage | Immediate |
| Data quality | Same day |
| Performance | Within sprint |
Data metrics
| Metric | Track |
|---|---|
| Pipeline success rate | % successful |
| Data freshness | SLA compliance |
| Quality score | By dimension |
| Incidents | Per period |
Best practices
| Practice | Implementation |
|---|---|
| Idempotent pipelines | Rerun safely |
| Data lineage | Track flow |
| Version schemas | Handle changes |
| Test data | Quality validation |