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DataOps Pipeline

⬢ LIVELLO 2Tecniche
Alto
Impatto sullo stipendio
2 mesi
Tempo di apprendimento
Medio
Difficoltà
2
Carriere
In sintesi

DataOps applies DevOps principles to data pipelines: version control (pipeline code), testing (unit + integration), CI/CD (auto-deploy on commit), observability (logging, alerting, SLOs). DataOps engineers reduce pipeline failures by 50% and cut debugging time 70%. Senior practitioners earn 15-20% premium because they shift data team culture from 'manual fixes' to 'automated reliability'. Learning: 6-8 weeks.

Cos'è DataOps Pipeline

DataOps is the application of DevOps practices to data pipelines. It includes version control (pipeline code in Git), testing (unit, integration, quality tests), CI/CD (automated testing and deployment), observability (logging, monitoring, alerting), and automation (removing manual fixes). Example: Write dbt SQL model → Commit to Git → GitHub Actions runs tests → Deploy to staging → Manual approve → Prod deployment. If pipeline fails, Datadog alert fires immediately with root cause.

🔧 STRUMENTI ED ECOSISTEMA
Git/GitHubdbt (data transformation)Dagster (orchestration)Airflow (orchestration)Terraform (infrastructure)pytest (testing)Great Expectations (data quality)Datadog (monitoring)Docker/KubernetesCI/CD platforms (GitHub Actions, GitLab CI)

📋 Prima di iniziare

Git Version Control

💰 Stipendio per regione

RegioneLivello baseMidLivello esperto
USA$85k$145k$220k
UK£52k£88k£135k
EU€58k€95k€145k
CANADAC$80kC$140kC$210k

🎯 Carriere che usano DataOps Pipeline

Analytics Engineer
Data Architect

⚖ Confronta con

Devops Ci Cd

❓ Domande frequenti

What's the difference between DataOps and traditional data engineering?
Traditional data engineering builds pipelines. DataOps adds reliability practices: version control, testing, monitoring, CI/CD. Result: pipelines fail less often and are easier to debug when they do.
Should I version control my pipeline code?
Always. All pipeline code (SQL, dbt, Airflow DAGs, transformations) in Git. Code review before merge. CI runs tests. CD deploys to staging, then production.
How do I test data pipelines?
Unit tests: test SQL logic in isolation. Integration tests: test end-to-end (extract → transform → load). Quality tests: use Great Expectations to assert data shape (columns, nulls, ranges).
What are SLOs for data pipelines?
Service-level objectives: 'Pipeline completes by 9am', 'Data freshness <2 hours', 'Query p99 <10s'. Define expectations, monitor, alert if breached.
How do I reduce pipeline debugging time?
Logging + monitoring. Log every step: which records processed, which failed, why. Send logs to Datadog. Alert on errors. When pipeline fails, logs tell you why immediately.

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