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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.

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Understanding CICD Pipelines in Data Engineering | IABAC

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  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. iabac.org

  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. iabac.org

  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. iabac.org

  5. Tools & Workflow 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 notes:- Show a simple workflow diagram if possible: commit → test → staging → production. iabac.org

  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. iabac.org

  7. Thank you Visit: www.iabac.org iabac.org

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