Apache Airflow Course in Hyderabad
Schedule, orchestrate and monitor data pipelines with Apache Airflow
The Apache Airflow course teaches you to author, schedule and monitor workflows as Python code. You start with workflow orchestration concepts, Python and Docker, then install Airflow and learn its architecture, the DAG file structure, tasks and operators, the TaskFlow API, scheduling, dependencies, retries and trigger rules. You work with variables, connections and secrets backends, XComs, dynamic task mapping, sensors and deferrable operators, assets for data-aware scheduling, and backfills. The course uses Apache Airflow 3, which introduced the Task SDK, assets, DAG versioning and a rebuilt user interface, and explains the differences from Airflow 2 so you can read older code. Practical modules orchestrate ETL pipelines with PostgreSQL, Spark, dbt and cloud storage, add alerting and monitoring, test DAGs with pytest, and deploy with Docker Compose, Kubernetes or managed services such as Cloud Composer, Amazon MWAA or Astronomer. The course suits data engineers, developers and ETL professionals with basic Python and SQL. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Workflow Orchestration Concepts
- •Cron versus orchestrators; what a DAG is
- •Batch pipeline lifecycle: extract, load, transform and publish
- •Airflow versus alternatives such as Prefect and Dagster, at an overview level
- •Lab: Sketch a pipeline as a dependency graph
- •Outcome: Explain when and why to use Airflow
Python, Docker & Environment Setup
- •Python functions, modules and virtual environments
- •Docker and Docker Compose basics
- •Installing Airflow 3 with Docker Compose; navigating the UI
- •Lab: Run Airflow locally and trigger an example DAG
- •Outcome: A working local Airflow environment
Airflow Architecture
- •Components in Airflow 3: scheduler, DAG processor, API server, triggerer and workers
- •Executors: Local, Celery and Kubernetes overview
- •Metadata database and logs
- •Lab: Trace how a DAG run moves through components
- •Outcome: Understand what runs where
Writing DAGs & Tasks
- •DAG parameters: schedule, start date, catchup and tags
- •Operators, the airflow.sdk namespace and the @dag and @task decorators
- •Task dependencies, groups and trigger rules
- •Lab: Build a three-step ETL DAG with the TaskFlow API
- •Outcome: Author clear, maintainable DAGs
Scheduling, Backfills & Time
- •Data intervals, logical date and cron expressions
- •Timetables and catchup behaviour
- •Backfills managed by the scheduler
- •Lab: Backfill a month of daily runs
- •Outcome: Schedule and rerun history correctly
Passing Data & Configuration
- •XComs for small data and custom backends for larger payloads
- •Variables, connections and secrets backends
- •DAG params and templating with Jinja
- •Lab: Parameterise a DAG and read credentials from a connection
- •Outcome: Configure pipelines without hard-coded values
Dynamic Workflows & Branching
- •Dynamic task mapping with expand and partial
- •Branching, short-circuiting and conditional paths
- •Reusable task groups
- •Lab: Process a variable number of files dynamically
- •Outcome: Build flexible pipelines that adapt to the data
Sensors, Deferrable Operators & Assets
- •Sensors and poke versus reschedule modes
- •Deferrable operators and the triggerer
- •Assets and the @asset decorator; scheduling on asset updates
- •Lab: Trigger a downstream DAG when an asset updates
- •Outcome: Build event-driven and data-aware pipelines
Integrations: Databases, Cloud, Spark & dbt
- •Provider packages and hooks
- •Working with PostgreSQL and cloud storage
- •Triggering Spark jobs and dbt runs
- •Lab: Orchestrate a load, a dbt run and a quality check
- •Outcome: Connect Airflow to the rest of the data stack
Reliability: Retries, Alerts & Monitoring
- •Retries, timeouts and failure callbacks
- •Email and chat notifications; Airflow 3 deadline alerts overview
- •Logging, metrics with StatsD or OpenTelemetry and Grafana overview
- •Lab: Add failure alerts and a dashboard of run durations
- •Outcome: Detect and recover from failures
Testing, CI/CD & Deployment
- •DAG integrity tests and unit tests with pytest
- •CI/CD with GitHub Actions and DAG deployment patterns
- •Deployment: Docker Compose, Helm chart on Kubernetes, Cloud Composer, Amazon MWAA and Astronomer overview
- •Lab: Add a CI pipeline that tests DAG imports
- •Outcome: Ship Airflow code safely
Security, Upgrades & Capstone
- •Roles, permissions and securing connections
- •Migrating from Airflow 2 to Airflow 3: changes and checks
- •Capstone: end-to-end ELT pipeline with sensors, assets, tests and alerts
- •Interview practice for orchestration roles
- •Outcome: A portfolio-ready orchestration project
Tools Covered
Projects Included
- Daily ETL DAG: Extract data from an API, load it into PostgreSQL and run quality checks; deliverable is a scheduled DAG with run history.
- Dynamic File Processor: Process a variable list of files with dynamic task mapping; deliverable is a DAG and execution report.
- Asset-Driven Pipeline: Chain DAGs so that a downstream report runs when upstream data updates; deliverable is two DAGs and a lineage screenshot.
- ELT with dbt and Spark: Orchestrate ingestion, a Spark transform and a dbt run with alerts; deliverable is a repository with CI tests.
- Capstone Orchestration Platform: Deploy Airflow with Docker Compose or Kubernetes, with monitoring and failure alerts; deliverable is a GitHub repository and runbook.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Apache Airflow course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Apache Airflow.
Certificate of Completion
Apache Airflow
This certifies that
[Your Name]
has successfully completed the
Apache Airflow
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Apache Airflow course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best Apache Airflow Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Data Engineer, ETL Developer, Airflow Developer, Data Platform Engineer; support is guidance with the job search, not a guarantee of a job.