By completing this course, learners will:

• Install and configure Dagster.

• Build data pipelines using software-defined assets.

• Manage jobs, schedules, and sensors.

• Integrate Dagster with modern data stack tools.

• Deploy and scale workflows in production.

• Apply observability and reliability best practices.

Course Syllabus

Module 1: Introduction to Dagster

• What is Dagster?

• Data orchestration vs scheduling

• Installing Dagster and setup

Module 2: Core Concepts

• Ops, jobs, and assets

• Software-defined assets (SDAs)

• Resources and I/O managers

• Configurations and parameters

Module 3: Building Pipelines

• Creating pipelines with assets

• Dependencies between ops

• Running jobs locally

• Unit testing and debugging

Module 4: Scheduling & Monitoring

• Creating schedules and sensors

• Event-based triggers

• Dagit web UI for monitoring

• Logging and debugging pipelines

Module 5: Integration with Data Tools

• Dagster with dbt for analytics

• Spark and Pandas integration

• Cloud data warehouses (BigQuery, Snowflake, Redshift)

• ML workflows with Dagster

Module 6: Deployment

• Running Dagster with Docker

• Kubernetes deployments

• Cloud-native deployments (AWS, GCP, Azure)

• CI/CD integration

Module 7: Observability & Scaling

• Metrics and monitoring with Prometheus + Grafana

• Partitioned and dynamic assets

• Managing large workflows

• Scaling strategies

Module 8: Real-World Projects

• ETL pipeline for e-commerce analytics

• Machine learning workflow orchestration

• Real-time data pipeline with sensors

• BI dashboard pipeline with dbt + Dagster

Module 9: Best Practices & Future Trends

• Designing maintainable pipelines

• Security and compliance considerations

• Comparing Dagster vs Airflow vs Prefect

• The future of orchestrators in the data stack

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in Dagster and modern data orchestration. This certification demonstrates readiness for roles in data engineering, analytics, and machine learning operations.

Dagster skills prepare learners for roles such as:

• Data Engineer (ETL/ELT workflows)

• ML Engineer (pipeline orchestration)

• Analytics Engineer (dbt + BI pipelines)

• DevOps/DataOps Engineer (workflow deployments)

• Platform Engineer (data platforms at scale)

Dagster is increasingly being adopted as a modern alternative to Airflow, making it highly valuable in startups, SaaS platforms, and enterprises modernizing their data stack.

1. What is Dagster?
A modern data orchestrator for building, running, and monitoring pipelines with software-defined assets.

2. How does Dagster differ from Airflow?
Dagster focuses on testing, observability, and assets-first workflows, while Airflow is more task/schedule-driven.

3. What are software-defined assets (SDAs)?
Data products defined in code, allowing better testing, versioning, and observability.

4. What is the role of resources in Dagster?
Resources provide external connections (e.g., database, APIs) that ops/assets can use.

5. What is an I/O manager in Dagster?
It defines how data is stored and retrieved between assets (e.g., writing to S3, database, or local storage).

6. What are schedules and sensors?
Schedules trigger jobs on a fixed timeline, while sensors react to external events (e.g., new file arrival).

7. How does Dagster integrate with dbt?
It orchestrates dbt models as assets, enabling end-to-end analytics workflows.

8. What are the benefits of using Dagster?

• Strong observability and testing

• Assets-first design

• Modern integrations with the data stack

• Scalable for production

9. What are challenges with Dagster?

• Learning curve for new concepts like SDAs

• Smaller ecosystem compared to Airflow

• Requires careful setup for large-scale pipelines

10. Where is Dagster being adopted?
By analytics-driven startups, SaaS companies, and enterprises modernizing ETL, ML, and BI pipelines.