Course Introduction
Data Engineering CI/CD and DevOps Training Course provides a comprehensive foundation for applying continuous integration, continuous delivery, continuous deployment, automation, collaboration, and operational engineering practices to modern data engineering environments. As data platforms become increasingly complex, organizations need reliable ways to develop, test, deploy, monitor, and maintain data pipelines and infrastructure. This course examines how DevOps principles can be adapted to data engineering workflows to improve delivery speed, reliability, reproducibility, security, and operational consistency.
The course explores the complete lifecycle of data engineering software and platform components, from source code management and collaborative development through automated testing, build processes, deployment, monitoring, and continuous improvement. Participants learn how version control systems, branching strategies, pull requests, code reviews, automated testing, artifact management, and deployment pipelines work together to establish controlled and repeatable data engineering delivery processes. Particular attention is given to managing pipeline code, SQL, configuration files, infrastructure definitions, schemas, data quality rules, and orchestration workflows.
Participants will learn how to build CI/CD pipelines specifically suited to ETL, ELT, batch processing, streaming, data transformation, orchestration, analytics engineering, and data platform infrastructure. The course addresses unit testing, integration testing, data validation, schema testing, pipeline testing, regression testing, deployment validation, and automated quality gates. Participants also examine how to separate development, testing, staging, and production environments while safely promoting data engineering changes between them.
Infrastructure automation is another important component of the course. Participants explore Infrastructure as Code, configuration management, containerization, environment provisioning, secrets management, cloud resource deployment, and automated platform configuration. The course demonstrates how DevOps practices can reduce manual configuration, environment inconsistencies, deployment errors, and operational bottlenecks. It also introduces modern approaches for managing cloud-native data platforms and distributed data infrastructure through declarative and automated processes.
The course further examines operational practices including observability, logging, monitoring, alerting, incident management, rollback strategies, release management, reliability engineering, and performance monitoring. Participants learn how to establish feedback loops between development and operations teams and how to use operational metrics to improve pipeline reliability. Security and governance are integrated through DevSecOps principles, access controls, secrets protection, dependency scanning, policy enforcement, and secure deployment practices.
Advanced topics include GitOps, DataOps, platform engineering, continuous data integration, automated data quality validation, progressive delivery, blue-green and canary deployments, workflow orchestration, ephemeral environments, policy-as-code, and AI-assisted DevOps. Participants also explore emerging practices for managing data products, machine learning pipelines, real-time data systems, and cloud-native data platforms. By the end of the course, participants will understand how to establish scalable CI/CD and DevOps practices that support reliable, secure, and continuously improving data engineering operations.
Duration: 10 days
Who Should Attend
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Data Engineers
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Data Platform Engineers
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Analytics Engineers
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DevOps Engineers
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Data Architects
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Cloud Engineers
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Cloud Data Engineers
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Software Engineers Working With Data Platforms
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Database Administrators
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Data Scientists Working With Production Pipelines
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Site Reliability Engineers
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Platform Engineers
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Machine Learning Engineers
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DataOps Professionals
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IT Operations Professionals
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Technology Managers And Engineering Leaders
Course Objectives
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Explain the principles of DevOps, CI/CD, DataOps, automation, collaboration, and continuous improvement within modern data engineering environments.
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Implement source control and collaborative development practices for data pipelines, SQL, configuration, schemas, infrastructure, and orchestration code.
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Design continuous integration workflows that automatically build, validate, test, and package data engineering components.
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Develop continuous delivery and deployment pipelines for batch, streaming, transformation, orchestration, and cloud data platform workloads.
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Apply automated testing techniques for pipeline logic, SQL transformations, data quality, schemas, integrations, and deployment configurations.
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Manage development, testing, staging, and production environments using controlled promotion, configuration management, and environment-specific deployment practices.
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Apply Infrastructure as Code and automation techniques to provision, configure, update, and maintain data platform infrastructure consistently.
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Integrate security practices including secrets management, dependency scanning, access controls, vulnerability detection, and policy enforcement into CI/CD workflows.
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Implement observability, monitoring, logging, alerting, incident management, and operational feedback mechanisms for data engineering systems.
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Apply deployment strategies such as rollback, blue-green deployment, canary release, progressive delivery, and automated recovery to reduce production risk.
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Integrate GitOps, DataOps, platform engineering, and automation practices into scalable cloud-native data engineering environments.
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Design advanced CI/CD and DevOps operating models that support data products, real-time platforms, AI and machine learning workloads, and continuous data delivery.
Comprehensive Course Outline
Module 1: Foundations Of Data Engineering DevOps And CI/CD
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DevOps principles, culture, collaboration, automation, continuous improvement, and their application to data engineering.
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Continuous integration, continuous delivery, continuous deployment, DataOps, and the modern data engineering lifecycle.
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Differences between traditional software delivery and data engineering delivery involving pipelines, schemas, datasets, and infrastructure.
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Identifying manual processes, deployment risks, operational bottlenecks, and opportunities for automation across data workflows.
Module 2: Version Control And Collaborative Data Engineering
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Git fundamentals, repositories, commits, branches, merges, tags, pull requests, and collaborative development workflows.
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Versioning data pipelines, SQL transformations, schemas, configuration files, infrastructure definitions, and orchestration code.
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Branching strategies, trunk-based development, feature branches, release branches, and controlled production changes.
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Code reviews, merge policies, repository organization, commit standards, and traceability for data engineering projects.
Module 3: Continuous Integration For Data Pipelines
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CI pipeline architecture covering source retrieval, dependency installation, compilation, validation, testing, packaging, and artifact creation.
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Automated validation of SQL, Python, configuration files, schemas, pipeline definitions, and orchestration workflows.
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Build triggers, pull request validation, quality gates, dependency management, and automated failure handling.
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Designing fast, reliable, repeatable CI workflows for large-scale data engineering repositories.
Module 4: Automated Testing And Data Quality
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Unit, integration, regression, functional, end-to-end, and deployment testing for data engineering systems.
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Automated data quality checks covering completeness, accuracy, consistency, uniqueness, validity, timeliness, and schema conformity.
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Testing transformations, SQL models, pipeline dependencies, APIs, database connections, and data contracts.
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Quality gates, test environments, test datasets, synthetic data, and automated validation before production deployment.
Module 5: Continuous Delivery And Deployment
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Continuous delivery architecture and automated promotion of data engineering changes across environments.
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Development, testing, staging, and production environment management and deployment controls.
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Deployment approvals, release management, artifact promotion, environment configuration, and production safeguards.
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Rollback strategies, deployment verification, release traceability, and managing failed data platform deployments.
Module 6: Infrastructure As Code For Data Platforms
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Infrastructure as Code principles and declarative management of data platform resources and environments.
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Automating cloud databases, storage, compute resources, networking, orchestration services, and data platform components.
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Infrastructure state management, reusable modules, environment configurations, dependency management, and controlled infrastructure changes.
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Infrastructure validation, automated provisioning, drift detection, and recovery through reproducible platform definitions.
Module 7: Containers And Environment Standardization
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Containerization concepts and their application to data pipelines, transformation workloads, testing environments, and platform services.
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Building reproducible development and deployment environments using containers and standardized dependencies.
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Container image management, versioning, security scanning, registries, and controlled image promotion.
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Container orchestration considerations for scalable data processing, workflow execution, and cloud-native data engineering.
Module 8: CI/CD For Data Orchestration And Pipelines
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Integrating CI/CD practices with workflow orchestration, scheduling, dependency management, and pipeline execution.
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Automated deployment of batch, streaming, ETL, ELT, transformation, and ingestion workflows.
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Managing pipeline configuration, environment variables, schedules, dependencies, credentials, and runtime parameters.
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Deployment validation, pipeline health checks, failure recovery, retries, rollback, and operational automation.
Module 9: Cloud-Native Data Engineering DevOps
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DevOps practices for cloud data warehouses, data lakes, lakehouses, managed databases, and serverless data services.
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Cloud deployment automation, resource provisioning, configuration management, identity controls, and environment consistency.
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Managing cloud-native CI/CD pipelines across development, staging, production, and multiple regions.
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Cost, scalability, availability, performance, and operational considerations in cloud data engineering delivery.
Module 10: DevSecOps And Secure Data Engineering Delivery
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Integrating security controls into CI/CD pipelines through automated scanning, validation, and policy enforcement.
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Secrets management for credentials, API keys, certificates, connection strings, and deployment automation.
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Dependency, container, infrastructure, and code security scanning throughout the development lifecycle.
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Security gates, least privilege, compliance validation, auditability, and secure production deployment practices.
Module 11: Data Platform Observability And Reliability Engineering
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Monitoring pipeline execution, data freshness, processing latency, resource utilization, failures, and service availability.
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Centralized logging, metrics, traces, dashboards, alerts, and operational visibility across data engineering environments.
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Service-level indicators, service-level objectives, reliability targets, error budgets, and operational performance measurement.
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Incident response, root cause analysis, post-incident reviews, and continuous reliability improvement for data systems.
Module 12: Deployment Strategies And Release Management
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Blue-green deployments, canary releases, rolling deployments, feature flags, progressive delivery, and controlled production changes.
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Managing database schema changes, pipeline version changes, data model migrations, and backward compatibility.
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Release orchestration, deployment sequencing, approvals, validation, rollback, and recovery procedures.
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Minimizing downtime and data disruption during complex data platform releases and infrastructure changes.
Module 13: GitOps, DataOps And Platform Engineering
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GitOps principles for declarative infrastructure, configuration management, environment synchronization, and automated deployments.
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DataOps practices for improving collaboration, quality, observability, automation, and delivery across data teams.
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Platform engineering approaches for building reusable internal data engineering platforms and developer self-service capabilities.
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Standardized templates, golden paths, reusable components, automated environments, and governance controls for engineering teams.
Module 14: CI/CD For Streaming, Real-Time And Advanced Data Workloads
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Applying CI/CD practices to streaming pipelines, event-driven systems, message processing, and real-time analytics.
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Testing streaming applications, event schemas, message compatibility, state management, and processing reliability.
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Continuous deployment strategies for machine learning pipelines, feature pipelines, analytical models, and AI data workflows.
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Managing version compatibility, model dependencies, real-time releases, and continuous delivery for advanced data workloads.
Module 15: Advanced Automation And Intelligent DevOps
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Policy-as-code, automated governance, compliance validation, environment provisioning, and continuous control enforcement.
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Ephemeral environments, automated test infrastructure, self-service deployment platforms, and environment lifecycle automation.
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AI-assisted code analysis, pipeline optimization, anomaly detection, incident support, and intelligent operational automation.
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Continuous improvement using engineering metrics, deployment frequency, lead time, failure rates, recovery performance, and operational feedback.
Module 16: Advanced Data Engineering DevOps And Future Directions
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Designing enterprise-scale CI/CD architectures for distributed data platforms, data products, and multi-team engineering environments.
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Managing multi-cloud, hybrid, distributed, and highly regulated data engineering delivery pipelines.
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Emerging approaches including autonomous DevOps, intelligent DataOps, continuous data contracts, platform engineering, and AI-assisted delivery.
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Developing a sustainable DevOps operating model that combines automation, security, reliability, governance, collaboration, and continuous improvement.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
