Understanding CICD Pipelines in Data Engineering | IABAC

CI/CD pipelines in data engineering automate integration, testing, and deployment of ETL/ELT workflows, big data processing, and transformations, ensuring faster, reliable, and accurate data delivery to warehouses, data lakes, and analytics systems.

1. Understanding CI/CD
Pipelines in Data
Engineering iabac.org‌

2. What is CI/CD in Data Engineering?
Continuous Integration (CI): ‌Regularly integrate changes in‌
ETL/ELT pipelines‌
Continuous Deployment (CD): ‌Automatically deploy tested
pipelines to production‌
Ensures reliable, fast, and accurate data delivery‌
notes:-‌ Explain CI/CD as a conveyor belt for data workflows, not just
software.
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3. Continuous Integration (CI)
Commit small, frequent changes in ETL/ELT pipelines‌
Automated validation of scripts, SQL, and transformations‌
Unit tests, schema validation, and data quality checks‌
Instant feedback to engineers‌
notes:- ‌Emphasize catching errors early to maintain data quality
and prevent production issues.‌
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4. Continuous Deployment / Delivery (CD)
Artifact creation: ‌Scripts, SQL, or containerized pipelines‌
Staging deployment with test datasets‌
Automated testing: ‌regression, performance, data validation‌
Production deployment to data warehouses or data lakes‌
notes:- ‌Highlight benefits: faster updates, fewer errors, reliable
production data.‌
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5. Version control: ‌Git, GitHub, GitLab‌
CI/CD platforms: ‌Jenkins, GitHub Actions, GitLab CI, CircleCI‌
Orchestration: ‌Airflow, Prefect, Dagster‌
Testing: ‌Great Expectations, dbt, SQL/Python scripts‌
Big data support: ‌Spark, Hadoop‌
Deployment: ‌Docker, Kubernetes, Terraform‌
Tools & Workflow
notes:-‌ Show a simple workflow diagram if possible: commit → test →
staging → production.‌
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6. Benefits & Best Practices
Faster data delivery
Improved data quality‌
Reduced risk of broken pipelines‌
Enhanced collaboration among engineers‌
Best Practices:‌ small frequent commits, automated testing,
monitoring, version control, clear documentation‌
notes:-‌ Wrap up with key takeaways and encourage adopting CI/CD in
data engineering projects.‌
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7. Thank you
Visit: www.iabac.org
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