Modern Data Pipeline Orchestration with Dagster Training Course  

Course Introduction

Modern Data Pipeline Orchestration with Dagster Training Course provides a comprehensive framework for designing, developing, testing, deploying, monitoring, and governing modern data pipelines using Dagster. The course focuses on asset-oriented orchestration and modern data engineering practices, enabling participants to coordinate data ingestion, transformation, validation, analytics, machine-learning workflows, and data products through reliable, observable, and maintainable pipelines that align technical execution with the logical structure of enterprise data.

Modern data platforms increasingly require orchestration approaches that understand not only when tasks execute but also what data assets are produced, transformed, consumed, validated, and delivered. This course introduces Dagster’s software-defined asset model, jobs, ops, graphs, schedules, sensors, resources, partitions, automation capabilities, metadata, and asset checks. Participants learn how to represent data dependencies explicitly and create pipelines that provide greater visibility into data lineage, freshness, quality, and operational status.

The course develops practical skills in building Dagster projects using Python and organizing workflows into reusable, testable, and production-ready components. Participants learn how to define assets and dependencies, configure resources, manage partitions, implement schedules and sensors, establish asset checks, handle failures, and use Dagster’s development and operational interfaces to monitor pipeline execution. Particular attention is given to designing orchestration code that remains maintainable as data volumes, pipeline complexity, teams, and business requirements grow.

Participants also explore Dagster integration with modern data ecosystems, including cloud storage, relational databases, data warehouses, data lakes, transformation frameworks, APIs, Python applications, containerized workloads, and machine-learning platforms. The course examines how Dagster can coordinate tools such as dbt, Spark, Kubernetes, cloud services, and external data systems while maintaining clear asset dependencies and operational visibility. Emerging practices involving event-driven automation, data contracts, lineage, AI pipelines, real-time data, and software-defined data infrastructure are integrated throughout the program.

A strong practical emphasis enables participants to construct end-to-end asset-driven pipelines and address common operational challenges such as failed materializations, stale assets, partition backfills, changing schemas, late-arriving data, resource constraints, dependency failures, and inconsistent downstream results. Participants learn to use metadata, asset checks, automation, partitioning, retries, observability, and testing to create workflows that can recover safely and provide trustworthy information about pipeline state.

By completing the Modern Data Pipeline Orchestration with Dagster Training Course, participants will be equipped to implement modern, asset-aware orchestration strategies that improve data reliability, lineage, quality, observability, and operational efficiency. The course connects Dagster architecture, software-defined assets, pipeline development, data quality, scheduling, automation, cloud integration, testing, deployment, monitoring, and governance into a practical framework for building scalable enterprise data platforms.

Duration

10 days

Who Should Attend

• Data engineers responsible for developing and operating modern production data pipelines.

• Data platform engineers managing enterprise data orchestration and infrastructure.

• Data architects designing modern data platforms and asset-oriented pipeline architectures.

• Analytics engineers building transformation, modeling, and analytical data workflows.

• Cloud engineers deploying data orchestration environments across cloud and hybrid platforms.

• DevOps engineers responsible for deployment automation, infrastructure, CI/CD, and operational reliability.

• Site reliability engineers supporting data platform observability, availability, and recovery.

• Data scientists coordinating analytical, machine-learning, and feature engineering workflows.

• Machine-learning engineers orchestrating training, validation, feature, and model delivery pipelines.

• Database engineers integrating operational and analytical databases into data workflows.

• BI professionals responsible for dependable analytical data refresh and data product delivery.

• ETL and ELT developers modernizing legacy pipeline automation and scheduling processes.

• Data quality specialists implementing validation and asset-quality controls.

• Data governance professionals interested in lineage, metadata, ownership, and data reliability.

• Enterprise architects evaluating modern orchestration technologies and data platform patterns.

• Technical leads responsible for data pipeline modernization and orchestration transformation.

• Technology managers overseeing enterprise data engineering and platform initiatives.

• Software engineers developing Python-based data applications and data infrastructure components.

Course Objectives

• Develop a comprehensive understanding of Dagster architecture and apply asset-oriented orchestration principles to modern enterprise data engineering environments.

• Design software-defined assets that clearly represent data products, transformations, dependencies, materializations, ownership, and operational expectations.

• Develop maintainable Dagster projects using Python, assets, ops, graphs, jobs, resources, schedules, sensors, partitions, metadata, and reusable components.

• Implement asset dependencies and lineage structures that make relationships between upstream and downstream data products explicit, traceable, and operationally meaningful.

• Configure schedules, sensors, automation conditions, and event-driven workflows to execute data pipelines according to business requirements and data availability.

• Apply partitioning and backfill strategies to process large data sets efficiently while supporting historical reprocessing, incremental updates, and controlled recovery.

• Integrate Dagster with databases, data warehouses, data lakes, cloud storage, APIs, dbt, Spark, Kubernetes, and other modern data processing technologies.

• Establish asset checks and data-quality validation mechanisms that identify incomplete, stale, invalid, inconsistent, or otherwise unsuitable data before downstream consumption.

• Design observability practices using metadata, logs, materialization history, run information, asset status, freshness indicators, and operational monitoring capabilities.

• Apply testing, version control, CI/CD, environment management, and infrastructure automation practices to improve the reliability and maintainability of Dagster deployments.

• Optimize Dagster performance and scalability through appropriate execution models, resource management, partition strategies, concurrency controls, and workload architecture.

• Create enterprise Dagster governance and modernization strategies covering security, lineage, ownership, reliability, automation, deployment, data quality, cloud integration, and continuous improvement.

Comprehensive Course Outline

Module 1: Foundations of Dagster and Modern Data Orchestration

• Examine modern data orchestration principles and understand how Dagster approaches pipelines through assets, dependencies, execution, observability, and software-defined data infrastructure.

• Explore Dagster’s core architecture and concepts including assets, ops, jobs, graphs, resources, schedules, sensors, runs, repositories, metadata, and asset materializations.

• Compare task-oriented workflow orchestration with asset-oriented orchestration and examine how each approach represents data dependencies and operational state.

• Establish production-oriented principles covering modularity, testability, observability, lineage, data quality, reliability, scalability, deployment, and governance.

Module 2: Dagster Project Structure and Python Development

• Create and organize Dagster projects using Python while applying maintainable directory structures, modular code organization, configuration practices, and development standards.

• Develop reusable pipeline components and apply Python programming principles that improve readability, testability, maintainability, and collaboration across data engineering teams.

• Configure project definitions, asset collections, resources, jobs, schedules, sensors, and deployment settings within structured Dagster environments.

• Establish development practices using virtual environments, dependency management, version control, code reviews, documentation, and repeatable local development workflows.

Module 3: Software-Defined Assets and Data Dependencies

• Design software-defined assets that represent tables, files, models, datasets, reports, machine-learning artifacts, and other meaningful enterprise data products.

• Establish explicit asset dependencies that represent upstream and downstream relationships and provide a clear logical model of the organization’s data processing landscape.

• Analyze asset materialization behavior, metadata, ownership, freshness, partitioning, and downstream impact when designing production data pipelines.

• Apply asset-oriented design principles to reduce hidden dependencies, improve lineage visibility, simplify maintenance, and align technical workflows with business data products.

Module 4: Ops, Jobs, Graphs and Execution Models

• Develop Dagster ops and graphs for processing patterns that require explicit task-level orchestration, reusable computational components, or complex execution logic.

• Design jobs that select and execute appropriate assets or operations according to business processes, pipeline requirements, schedules, and operational constraints.

• Compare asset-based and operation-based orchestration patterns and determine appropriate use cases for each within modern data platforms.

• Configure execution behavior, run parameters, tags, selection mechanisms, dependencies, and operational controls for predictable and maintainable workflow execution.

Module 5: Resources, Configuration and External Systems

• Configure Dagster resources for database connections, cloud services, APIs, storage platforms, transformation tools, compute engines, and other external data systems.

• Separate workflow logic from infrastructure configuration to improve portability, testing, security, environment management, and operational flexibility.

• Develop configurable pipelines that support development, testing, staging, production, and other deployment environments without unnecessary code duplication.

• Establish secure resource management practices covering credentials, connections, secrets, configuration parameters, network access, and external system dependencies.

Module 6: Scheduling, Sensors and Event-Driven Automation

• Design schedules that execute assets and workflows according to time-based requirements, processing windows, business calendars, data freshness expectations, and operational constraints.

• Implement sensors that respond to external events such as new files, database changes, upstream workflow completion, API events, or changes in operational conditions.

• Examine automation capabilities for triggering asset materializations based on dependency state, freshness, upstream completion, partitions, or other defined conditions.

• Develop event-driven orchestration patterns that reduce unnecessary polling and improve responsiveness while maintaining predictable workflow behavior and operational control.

Module 7: Partitions, Incremental Processing and Backfills

• Design partitioned assets for large data sets and recurring processing requirements using time-based, static, dynamic, and multi-dimensional partition strategies.

• Implement incremental data processing approaches that limit unnecessary computation and improve pipeline performance as data volumes and historical records increase.

• Develop backfill strategies for correcting historical data, rebuilding assets, responding to upstream changes, and recovering from processing failures without disrupting current workflows.

• Analyze partition dependencies, partition mappings, historical processing requirements, and resource constraints when designing scalable data pipelines.

Module 8: Data Quality, Asset Checks and Reliability

• Implement asset checks that validate data completeness, freshness, schema expectations, uniqueness, business rules, referential integrity, and other critical quality conditions.

• Design quality controls that distinguish between informational observations, warning conditions, and failures requiring pipeline intervention or downstream protection.

• Integrate data-quality validation with asset dependencies so that unreliable or incomplete upstream data does not silently propagate into downstream data products.

• Establish quality monitoring and remediation workflows that document failures, identify root causes, support controlled reprocessing, and improve long-term data reliability.

Module 9: Integration with Modern Data Platforms

• Integrate Dagster with relational databases, cloud warehouses, data lakes, object storage, APIs, transformation frameworks, distributed processing engines, and enterprise applications.

• Coordinate dbt-based transformations and analytical models while maintaining clear asset relationships, execution dependencies, metadata, and operational visibility.

• Integrate Spark, Kubernetes, containerized workloads, cloud-native services, and Python applications into broader asset-oriented orchestration architectures.

• Develop integration patterns that manage external dependencies, connection failures, schema changes, service availability, authentication requirements, and processing constraints.

Module 10: Observability, Metadata and Data Lineage

• Use Dagster’s asset views, materialization history, metadata, logs, run information, and operational interfaces to understand pipeline and data-product behavior.

• Establish observability practices covering asset freshness, execution duration, materialization status, dependency health, failure frequency, and workflow performance.

• Integrate metadata and lineage information into broader enterprise data governance and cataloging practices to improve transparency and data discoverability.

• Explore AI-assisted observability, anomaly detection, predictive pipeline monitoring, intelligent diagnostics, and automated recommendations for increasingly complex data environments.

Module 11: Error Handling, Retries and Recovery

• Configure retries, timeouts, failure policies, run controls, and recovery procedures to manage transient failures and persistent processing problems safely.

• Design idempotent assets and operations so that reruns, retries, backfills, and recovery activities do not introduce unintended duplicate or inconsistent results.

• Analyze common failure conditions involving unavailable databases, cloud services, APIs, storage systems, upstream assets, malformed data, and infrastructure interruptions.

• Develop operational runbooks for diagnosing failed materializations, recovering workflows, validating repaired assets, and restoring normal pipeline execution.

Module 12: Testing, CI/CD and Software Engineering Practices

• Develop unit, integration, asset, resource, workflow, and data-quality tests that identify defects before Dagster pipelines reach production environments.

• Integrate Dagster projects with Git-based development, code review, automated testing, continuous integration, deployment pipelines, and controlled release processes.

• Apply software engineering practices such as modularity, dependency management, documentation, type validation, code quality controls, and reusable components.

• Establish deployment strategies that support consistent promotion of workflows across development, testing, staging, and production environments while controlling configuration differences.

Module 13: Scalability, Performance and Resource Management

• Analyze Dagster execution performance in relation to asset size, partition counts, dependency complexity, task duration, concurrency, infrastructure capacity, and external service limitations.

• Configure concurrency controls, resource requirements, execution settings, and workload boundaries to prevent excessive contention and improve predictable pipeline performance.

• Optimize asset processing through partitioning, incremental computation, efficient queries, parallel execution, caching, data locality, and appropriate compute allocation.

• Develop capacity planning practices that anticipate increasing asset counts, workflow frequency, data volumes, user activity, historical backfills, and processing demands.

Module 14: Cloud, Containers and Production Deployment

• Deploy Dagster in public cloud, hybrid, containerized, and Kubernetes environments while addressing scalability, security, networking, storage, observability, and operational requirements.

• Design production architectures that separate orchestration control components from data-processing workloads and provide appropriate resilience and resource isolation.

• Integrate infrastructure as code, automated provisioning, CI/CD, container registries, cloud storage, managed databases, and platform services into Dagster deployment processes.

• Evaluate managed and self-hosted deployment approaches according to operational ownership, customization, scalability, security, cost, integration, and organizational capabilities.

Module 15: Governance, Security and Enterprise Operations

• Establish Dagster governance frameworks covering asset ownership, workflow standards, naming conventions, resource management, deployment controls, documentation, and operational responsibilities.

• Design security controls covering authentication, authorization, secrets management, network protection, service identities, credential rotation, and privileged pipeline execution.

• Integrate lineage, metadata, data-quality results, operational status, and asset ownership into enterprise data governance and accountability frameworks.

• Develop production operating models covering incident response, service levels, change management, support responsibilities, platform maintenance, upgrades, and disaster recovery.

Module 16: Enterprise Dagster Strategy and Advanced Orchestration

• Develop enterprise Dagster reference architectures integrating software-defined assets, automation, data quality, observability, security, scalability, testing, deployment, and governance.

• Create modernization roadmaps for migrating legacy scripts, cron schedules, traditional workflow systems, and fragmented pipeline automation into asset-oriented Dagster environments.

• Apply advanced orchestration concepts including automation conditions, dynamic partitions, asset checks, data-aware execution, event-driven processing, lineage integration, and machine-learning workflows.

• Prepare Dagster platforms for emerging data engineering requirements including AI pipelines, real-time analytics, data products, data contracts, multi-cloud environments, and increasingly autonomous data operations.

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.