DataOps Training — Data Pipeline Automation, Quality & Governance

Who Should Attend

This program is for data engineers, analytics engineers, and DevOps practitioners supporting data teams. If your data pipelines fail silently, data quality issues are discovered by downstream consumers, schema changes break dashboards, or your data team has no CI/CD — this course applies DevOps principles to data engineering.

Learning Outcomes

• Version-control and deploy dbt models through CI/CD with automated testing
• Orchestrate data pipelines with Airflow — DAG design, scheduling, monitoring, alerting
• Implement data quality monitoring with Great Expectations and Soda at every pipeline stage
• Build data infrastructure as code with Terraform for data services
• Implement schema governance with registries and compatibility enforcement
• Monitor data freshness, volume, and quality with automated alerting

Course Modules

1. DataOps Fundamentals — DevOps for data. Data pipeline lifecycle. Data reliability engineering.
2. Data Transformation with dbt — Models, tests, snapshots, macros. dbt Cloud vs. Core. Materializations.
3. Data Orchestration with Airflow — DAGs, operators, sensors, XComs. Scheduling. Monitoring. Error handling.
4. CI/CD for Data Pipelines — dbt in CI/CD. Testing data transformations. Environment promotion. Safe deployment.
5. Data Quality Automation — Great Expectations suites. Soda checks. Data freshness, volume, schema, and distribution monitoring.
6. Data Infrastructure as Code — Terraform for Snowflake, BigQuery, Databricks, Redshift. Managing data infrastructure alongside application infrastructure.
7. Schema Governance — Schema registries. Compatibility enforcement. Schema evolution. Breaking change detection.
8. Data Catalog & Lineage — DataHub, Amundsen. Column-level lineage. Data discovery. Ownership metadata.
9. Data Observability — Monitoring data pipelines. Data downtime. SLAs for data. Anomaly detection on data characteristics.
10. Capstone: DataOps Pipeline — Build a complete DataOps pipeline with dbt CI/CD, data quality checks, Airflow orchestration, and monitoring.

Hands-on Labs (18 total)

Labs include: “Set up a dbt project with CI/CD in GitHub Actions that tests models before merge,” “Configure a Great Expectations suite that validates data freshness, volume, and schema on every pipeline run,” “Build an Airflow DAG that orchestrates dbt transformations with data quality gates.”

Frequently Asked Questions

How is this different from a data engineering course? Data engineering courses teach you to build data pipelines. DataOps teaches you to operate them reliably — CI/CD, testing, monitoring, governance. It’s the DevOps layer on top of data engineering. You’ll leave knowing how to deploy dbt models through pipelines, not manually.

Do I need a specific data warehouse? No. The course works with Snowflake, BigQuery, Redshift, and Databricks. Labs use Snowflake by default, but we provide instructions for other platforms. The DataOps principles are platform-agnostic.