Prefect
Master Prefect and learn to orchestrate, schedule, and monitor data workflows with ease—perfect for modern data engineering.Preview Prefect course
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A data ingestion pipeline with retry and logging
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An ETL pipeline integrating with cloud storage and databases
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A workflow to monitor and alert on data anomalies
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Learn to define, run, and monitor tasks and flows in Prefect
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Integrate Prefect with tools like AWS, GCP, Docker, and GitHub Actions
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Gain hands-on experience with Prefect Cloud and Prefect Orion
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Understand how to apply Prefect in production-grade data platforms
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Data engineers and analysts automating workflows
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Python developers working on ETL pipelines
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Cloud architects managing scalable data solutions
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Teams migrating from legacy orchestrators (e.g., Cron, Airflow)
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Anyone interested in operationalizing data pipelines
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Start with the fundamentals
Understand the core concepts of task orchestration before jumping into code. -
Code along
Write flows and tasks alongside the instructor in your local or cloud setup. -
Customize projects
Extend provided templates to solve real business problems. -
Join the Prefect community
Participate in community channels to share ideas and troubleshoot. -
Document your work
Keep notes on parameterization, retries, triggers, and Prefect’s state management system.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Understand the Prefect architecture, including tasks, flows, and state handlers
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Build robust, fault-tolerant data pipelines with retries and logging
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Use Prefect CLI, Prefect Orion UI, and Prefect Cloud
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Schedule workflows using interval, cron, and parameter triggers
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Deploy and monitor flows in production environments
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Integrate Prefect with cloud tools like AWS S3, GCS, and Docker
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Debug workflows using logs, states, and alerts
Course Syllabus
Module 1: Introduction to Prefect
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Why Workflow Orchestration Matters
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Overview of Prefect vs Airflow
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Prefect 2.0 and Orion
Module 2: Getting Started
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Installing Prefect
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Writing Your First Flow
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Understanding Tasks and States
Module 3: Task Management and Retries
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Parameters and Caching
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Handling Failures and Retry Policies
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Logging and Debugging
Module 4: Scheduling Flows
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Time-based and Cron Scheduling
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Using the Prefect CLI
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Parameterizing Flow Runs
Module 5: Working with Prefect Cloud and Orion UI
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Setting Up Prefect Cloud
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Monitoring Flows via the Dashboard
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Alerts and Notifications
Module 6: Integration with External Systems
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Connecting to AWS, GCP, and Databases
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Triggering Flows from GitHub or REST APIs
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Using Docker and Kubernetes Agents
Module 7: Real-World Projects
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ETL Workflow
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Data Quality Checker
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Automated Report Generator
Module 8: Prefect Interview Questions & Answers
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Common Interview Scenarios
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Best Practices and Troubleshooting
Upon successful completion of the course, participants receive an industry-recognized Certificate of Completion from Uplatz. This credential validates your skills in Python-based data orchestration, automation, and production monitoring using Prefect, enhancing your profile for roles in data engineering and automation.
Learning Prefect can open doors to roles such as:
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Data Engineer
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Workflow Orchestration Engineer
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Automation Specialist
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Python Developer (Data)
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Cloud Data Engineer
With organizations modernizing their data infrastructure, Prefect expertise is in growing demand across industries from finance to healthcare.
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What is Prefect and how does it compare to Airflow?
Answer: Prefect is a modern workflow orchestration tool designed to manage, schedule, and monitor data workflows. Unlike Airflow, which uses static DAGs, Prefect offers a more dynamic, Python-native approach using imperative code. Prefect 2.0 (Orion) introduces a flexible, DAG-free model, better observability, and improved local development experience. -
How are tasks and flows defined in Prefect?
Answer: Tasks are individual units of work, and flows are collections of tasks with defined execution order. They are defined using Python decorators like@task
and@flow
. Prefect uses a Directed Acyclic Graph (DAG) model under the hood but allows defining workflows imperatively. -
What are some common triggers in Prefect scheduling?
Answer: Prefect supports several scheduling triggers including interval-based (e.g., every 10 minutes), cron-style (e.g., at midnight), and manual parameter-based triggers. These can be configured via code or through the Prefect Cloud/Orion UI. -
Explain how retries and logging work in Prefect.
Answer: Prefect allows you to specify retry policies for tasks using theretries
andretry_delay_seconds
parameters. Logs are automatically captured for each task and flow run, and can be viewed in the UI or exported for centralized monitoring. -
What is the difference between Prefect Cloud and Orion?
Answer: Prefect Cloud is a managed orchestration environment hosted by Prefect, offering advanced features like team management, cloud storage, and API integration. Orion (now the core of Prefect 2.0) is an open-source orchestration engine that you can run locally or self-hosted. -
How do you manage dependencies between tasks?
Answer: Task dependencies are managed using the order in which tasks are called within the flow. Since Prefect uses Python’s control flow, dependencies are defined naturally through function execution order and data passing. -
Can you deploy a Prefect flow on Docker? How?
Answer: Yes. You can package your Prefect flows in a Docker container by writing a Dockerfile that installs the required dependencies and runs the Prefect agent. The container can be deployed to Kubernetes, ECS, or any container-based environment. -
How would you monitor and debug a failed flow run?
Answer: You can monitor failed runs through the Prefect Orion or Cloud UI. Detailed logs for each task are accessible, and Prefect also provides state tracking and alerting. You can use custom state handlers to trigger notifications or re-runs. -
What are state handlers in Prefect and why are they useful?
Answer: State handlers are functions that run when a task or flow changes state (e.g., from Running to Failed). They’re useful for implementing custom logic such as logging, triggering alerts, or managing retries beyond default behavior. -
Describe a real-world scenario where Prefect adds value over traditional scripting.
Answer: In a scenario where a company runs daily ETL jobs pulling data from APIs and databases, Prefect can provide scheduling, logging, retries, and monitoring—unlike traditional Python scripts which lack built-in observability and fail silently. Prefect ensures these workflows are reliable and maintainable in production.