Advanced DataOps and Automated Data Delivery Training Course

Course Introduction

Advanced DataOps and Automated Data Delivery Training Course provides an enterprise-focused framework for engineering reliable, automated, observable, and continuously improving data delivery environments. The programme explores how organizations can apply software engineering, DevOps, automation, testing, governance, observability, and operational reliability principles to modern data pipelines and platforms.

The course examines the complete data delivery lifecycle, from source integration and ingestion through transformation, validation, deployment, orchestration, publishing, monitoring, and consumption. Participants explore engineering patterns for batch, streaming, event-driven, incremental, and real-time data delivery while learning how to reduce manual intervention and establish repeatable delivery processes across complex enterprise data ecosystems.

A major focus is continuous data delivery and automated pipeline operations. Participants learn how to implement version control, CI/CD, infrastructure-as-code, automated testing, data quality gates, schema validation, deployment promotion, rollback procedures, environment management, and workflow automation. The course demonstrates how these capabilities can create predictable and auditable delivery processes while accelerating the movement of trusted data into analytical and operational environments.

The programme also addresses data reliability and operational intelligence. Participants examine observability architectures, service-level objectives, pipeline health monitoring, freshness tracking, anomaly detection, lineage, dependency analysis, incident management, automated remediation, and operational analytics. These capabilities help data teams identify failures and degradation quickly while establishing measurable reliability standards across data products, pipelines, and platform services.

Governance, security, and compliance are integrated into automated data delivery rather than treated as separate control activities. Participants explore policy-as-code, automated access controls, data classification, privacy safeguards, compliance validation, metadata management, audit evidence, and governance checkpoints within CI/CD and DataOps workflows. The course also examines how automation can improve consistency while maintaining appropriate human oversight for sensitive and high-impact data operations.

By the end of the programme, participants will be equipped to design enterprise DataOps operating models and automated data delivery architectures that improve reliability, deployment velocity, quality, governance, and operational efficiency. They will develop practical strategies for modernizing manual delivery processes, implementing continuous data delivery, integrating intelligent automation, and establishing sustainable DataOps capabilities across cloud, hybrid, lakehouse, warehouse, and distributed data environments.

Duration

10 days

Who Should Attend

• Data engineers responsible for developing and delivering enterprise-scale data pipelines and data products.

• DataOps engineers implementing automated data delivery, testing, deployment, and operational workflows.

• Data platform engineers managing shared data infrastructure, orchestration, observability, and automation capabilities.

• DevOps engineers extending continuous integration and delivery practices into data engineering environments.

• Analytics engineers responsible for reliable transformation, analytical modeling, and production data delivery.

• Cloud architects designing automated and scalable data platform operating environments.

• Enterprise architects defining DataOps standards, reference architectures, and transformation strategies.

• Data quality professionals implementing automated validation, testing, reconciliation, and continuous quality controls.

• Data governance professionals integrating policy, compliance, lineage, and access controls into delivery pipelines.

• Site reliability and platform reliability engineers supporting dependable data platform operations.

• Machine learning and MLOps professionals integrating DataOps practices with machine learning data workflows.

• Data product owners responsible for reliable, governed, and continuously delivered data products.

• Technology managers leading data modernization, automation, engineering productivity, and platform transformation initiatives.

• Security and privacy professionals responsible for automated controls across enterprise data delivery environments.

• Consultants and technical leaders designing and implementing enterprise DataOps and automated data delivery programmes.

Course Objectives

• Explain advanced DataOps principles and how they improve data delivery reliability, quality, automation, collaboration, governance, and operational performance.

• Design enterprise DataOps architectures that integrate data ingestion, transformation, testing, orchestration, deployment, observability, and consumption.

• Implement automated CI/CD workflows for data pipelines, transformations, analytical models, data products, infrastructure, and supporting platform components.

• Apply automated testing strategies covering schemas, transformations, data quality, contracts, integration dependencies, performance, and production readiness.

• Design continuous data delivery pipelines capable of supporting batch, streaming, incremental, event-driven, and real-time data workloads.

• Implement infrastructure-as-code and environment automation to create consistent, reproducible, version-controlled data platform deployments.

• Establish data quality gates, automated validation, reconciliation, anomaly detection, and release controls throughout the data delivery lifecycle.

• Design comprehensive data observability frameworks for monitoring pipeline health, freshness, completeness, failures, dependencies, and service performance.

• Apply automated incident management, remediation, rollback, recovery, and escalation mechanisms to improve data platform reliability.

• Integrate security, privacy, governance, lineage, metadata, and compliance controls directly into automated DataOps workflows.

• Apply intelligent automation and emerging AI capabilities to improve pipeline optimization, operational diagnostics, quality management, and data delivery efficiency.

• Develop enterprise DataOps implementation roadmaps, operating models, engineering standards, and continuous improvement frameworks for sustainable adoption.

Comprehensive Course Outline

Module 1: Foundations of Advanced DataOps

• Core principles of DataOps and their relationship to data engineering, DevOps, platform engineering, and data product delivery.

• Evolution from manually managed data pipelines toward automated, continuously tested, observable, and reliable data delivery environments.

• DataOps operating principles covering collaboration, automation, reproducibility, quality, governance, monitoring, and continuous improvement.

• Enterprise considerations for establishing DataOps capabilities across distributed teams, platforms, domains, and data delivery lifecycles.

Module 2: Enterprise DataOps Architecture

• Designing reference architectures integrating ingestion, transformation, orchestration, testing, deployment, monitoring, governance, and data consumption.

• Establishing reusable DataOps platform services that support multiple engineering teams, data products, workloads, and technology environments.

• Designing centralized and federated DataOps operating models for enterprise organizations with distributed data ownership.

• Evaluating architectural trade-offs involving standardization, flexibility, scalability, platform complexity, interoperability, and operational ownership.

Module 3: Automated Data Pipeline Engineering

• Engineering reusable and parameterized data pipelines for batch, incremental, streaming, event-driven, and scheduled data delivery.

• Applying modular pipeline design, dependency management, configuration management, error handling, retries, and recovery patterns.

• Automating source integration, transformation, validation, publishing, and downstream delivery across enterprise data environments.

• Designing pipeline frameworks that support repeatability, portability, scalability, maintainability, and controlled operational execution.

Module 4: Continuous Integration and Continuous Delivery for Data

• Applying CI/CD principles to data transformation code, analytical models, pipeline definitions, schemas, quality rules, and platform configuration.

• Designing automated build, test, validation, packaging, deployment, promotion, rollback, and release workflows for data assets.

• Managing development, testing, staging, and production environments through controlled promotion and configuration management.

• Establishing release governance, approval mechanisms, change tracking, and automated deployment policies for enterprise data platforms.

Module 5: Automated Data Testing and Quality Engineering

• Designing automated testing frameworks for schema integrity, transformations, business rules, referential relationships, and analytical correctness.

• Implementing data quality gates that prevent unreliable datasets and pipeline changes from reaching downstream production environments.

• Applying reconciliation, statistical validation, anomaly detection, freshness checks, completeness testing, and regression analysis.

• Integrating quality testing with CI/CD, orchestration, observability, incident management, and continuous data delivery processes.

Module 6: Data Contracts, Schemas and Automated Compatibility

• Designing data contracts that define schemas, quality expectations, ownership, service levels, compatibility requirements, and consumption rules.

• Implementing automated schema validation, compatibility testing, versioning, registration, and controlled schema evolution.

• Managing breaking and non-breaking changes across distributed pipelines, data products, APIs, events, and analytical consumers.

• Integrating contracts with CI/CD, governance, metadata, lineage, testing, and automated delivery controls.

Module 7: Workflow Orchestration and Automated Delivery

• Designing sophisticated workflow orchestration for dependencies, scheduling, event triggers, parallel processing, retries, and failure recovery.

• Applying dynamic workflows, reusable operators, parameterization, backfills, conditional execution, and automated pipeline management.

• Integrating orchestration platforms with source control, testing frameworks, data quality services, metadata, and observability systems.

• Engineering workflow architectures that support reliable execution across distributed, cloud-native, hybrid, and multi-platform environments.

Module 8: Data Observability and Reliability Engineering

• Designing observability architectures for pipeline execution, data freshness, volume, schema changes, quality signals, infrastructure, and dependencies.

• Establishing service-level indicators and objectives for data products, pipelines, analytical datasets, and platform services.

• Implementing automated anomaly detection, dependency monitoring, lineage-aware impact analysis, and proactive operational alerting.

• Applying reliability engineering practices including failure isolation, recovery automation, error budgets, incident learning, and continuous resilience improvement.

Module 9: Automated Data Governance and Security

• Integrating data governance controls into automated delivery pipelines through machine-readable policies and policy-as-code approaches.

• Implementing automated classification, access validation, privacy checks, retention rules, encryption requirements, and sensitive-data controls.

• Establishing governance checkpoints that validate data products and pipeline changes before controlled production deployment.

• Creating auditable automated compliance workflows that connect policies, metadata, lineage, approvals, evidence, and operational events.

Module 10: DataOps for Cloud, Lakehouse and Distributed Platforms

• Designing DataOps architectures across cloud data warehouses, data lakes, lakehouses, distributed processing platforms, and analytical databases.

• Automating deployments and operational workflows across managed cloud services, containers, serverless platforms, and infrastructure-as-code environments.

• Managing portability, environment consistency, configuration, dependencies, networking, and platform-specific operational requirements.

• Establishing cross-platform DataOps standards for hybrid and multi-cloud data engineering environments.

Module 11: Streaming and Real-Time DataOps

• Applying DataOps practices to continuous streaming pipelines, event-driven architectures, message systems, and real-time analytical platforms.

• Automating testing, deployment, schema validation, monitoring, recovery, and quality controls for continuously running data workloads.

• Managing event ordering, delivery guarantees, replay, state, checkpointing, backpressure, and stream-processing reliability.

• Establishing operational controls for real-time data products requiring low latency, continuous availability, and rapid incident response.

Module 12: Infrastructure Automation and Platform Engineering

• Applying infrastructure-as-code to provision and manage data platforms, pipelines, orchestration services, networking, security, and supporting resources.

• Designing reusable platform templates and automated environment provisioning for consistent data engineering development and production operations.

• Integrating secrets management, configuration management, policy validation, resource controls, and automated lifecycle management.

• Establishing internal data platform capabilities that provide standardized tooling, automation, observability, and developer self-service.

Module 13: DataOps for Data Products and Analytics

• Designing automated delivery models for governed data products, analytical datasets, semantic models, metrics, and reusable enterprise data services.

• Establishing product-level quality, reliability, documentation, ownership, metadata, service levels, and release management practices.

• Integrating DataOps with analytics engineering, business intelligence, self-service analytics, and enterprise reporting environments.

• Creating automated publication and consumption workflows that maintain trusted analytical assets throughout their operational lifecycle.

Module 14: Intelligent Automation and AI-Assisted DataOps

• Applying machine learning and generative AI capabilities to pipeline diagnostics, anomaly investigation, documentation, testing, and operational assistance.

• Designing intelligent systems that identify recurring failures, recommend remediation actions, and support automated data engineering workflows.

• Applying AI-assisted metadata enrichment, quality analysis, dependency reasoning, code generation, and pipeline optimization under governed controls.

• Establishing human oversight, validation, auditability, security, and operational boundaries for AI-assisted DataOps automation.

Module 15: Performance, Cost and Operational Optimization

• Optimizing data delivery performance through parallelism, partitioning, caching, workload management, efficient processing, and resource allocation.

• Establishing operational metrics for pipeline throughput, latency, failure rates, deployment frequency, recovery time, and data freshness.

• Applying cloud cost management and FinOps practices to automated data workloads, infrastructure, storage, processing, and analytical services.

• Designing continuous optimization mechanisms that balance reliability, performance, delivery speed, engineering productivity, and operational expenditure.

Module 16: Enterprise DataOps Strategy and Implementation

• Developing enterprise DataOps strategies aligned with data platform modernization, engineering transformation, governance, and business delivery requirements.

• Creating target-state architectures, capability maturity models, implementation roadmaps, platform standards, and organizational operating models.

• Establishing measurable DataOps practices using delivery, reliability, quality, automation, governance, and operational performance indicators.

• Designing continuous improvement programmes that scale DataOps across enterprise domains while adapting to new technologies, workloads, and organizational requirements.

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.