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Master Apache Airflow — Industry-Standard Pipeline Orchestration
Learn Apache Airflow — the industry standard for data pipeline orchestration — with Trainer Venu. From DAG fundamentals to MWAA, Kubernetes executor, Databricks & Snowflake integrations — 5 real-world projects included.
No prior experience needed
7-day money-back guarantee
Placement support included
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₹18,000
₹25,000
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Trainer Venu's team will call you within 2 hours.
📋 Register for Free Demo
🎥 Live Online + Recorded Sessions
📂 5 End-to-End Projects
🔬 Real MWAA Cluster Access
📜 Certificate of Completion
🤝 Placement Support
♾️ Lifetime Recording Access
✅ Free Demo Before Enroll
Who Is This For
Is This Course Right For You?
🔄
ETL / Data Engineers
Automate and orchestrate data pipelines using industry-standard Airflow DAGs.
☁️
Cloud Engineers
Manage AWS MWAA, GCP Cloud Composer and Azure Airflow deployments.
⚡
Spark / Databricks Devs
Orchestrate Databricks notebooks and jobs via Airflow operators.
❄️
Snowflake / dbt Users
Build modern data stack pipelines: dbt + Airflow + Snowflake.
🎓
Freshers
Graduates targeting data engineering roles — Airflow is a must-have skill.
🏢
Data Architects
Design fault-tolerant, observable pipelines for enterprise platforms.
Tools Covered
🔄 Apache Airflow
☁️ AWS MWAA
🌐 GCP Cloud Composer
🔷 Azure Airflow
🔥 Databricks
❄️ Snowflake
🛠️ dbt
📊 Cosmos
⚙️ Kubernetes
🐳 Docker
🐘 PostgreSQL
📨 Kafka
🧪 Great Expectations
🔌 Celery Executor
Course Curriculum
7 Modules — Key Concepts
Here are the core topics you'll master. Each module includes hands-on labs with real Airflow access.
Module 01
Airflow Architecture & Setup
- Scheduler, Webserver, Metadata DB, Executors
- Docker Compose & Helm installation
- airflow.cfg configuration
- CeleryExecutor with Redis
- Airflow UI — DAG view, Graph, Gantt
Module 02
DAGs, Operators & Sensors
- TaskFlow API — @task decorator
- BashOperator, PythonOperator, BranchPythonOperator
- ShortCircuitOperator, TriggerDagRunOperator
- FileSensor, HttpSensor, S3KeySensor
- XComs — pass data between tasks
Module 03
Cloud Operators — AWS, Azure, GCP
- S3, Glue, Redshift, EMR, Lambda operators
- AzureDataFactoryRunPipelineOperator
- BigQueryInsertJobOperator, GCS operators
- RedshiftSQLOperator, GlueJobOperator
- Multi-cloud orchestration patterns
Module 04
Databricks & Snowflake Operators
- DatabricksRunNowOperator, DatabricksSubmitRunOperator
- SnowflakeOperator, SnowflakeHook
- S3ToSnowflakeOperator
- dbt BashOperator & DbtCloudRunJobOperator
- Cosmos — dbt task groups in Airflow
Module 05
Advanced Patterns
- Dynamic DAGs & DAG Factory (YAML-driven)
- Dynamic Task Mapping with .expand()
- KubernetesPodOperator — run tasks in K8s pods
- Deferrable Operators for async tasks
- Airflow Datasets — data-aware scheduling
Module 06
MWAA & Production Airflow
- Amazon MWAA — managed Airflow on AWS
- MWAA DAG deployment via S3
- Airflow RBAC and authentication
- Monitoring with CloudWatch
- Airflow upgrade strategies
Module 07
End-to-End Projects
- S3 → Glue → Redshift ETL Pipeline
- Real-time + Batch: Kinesis + Airflow
- Multi-cloud: AWS + GCP orchestration
- Databricks + Airflow: Delta Lake ingestion
- dbt + Airflow + Snowflake: modern data stack
M01
Apache Airflow — Architecture & Setup
⏱️ 4 Hours● Beginner
▾
What is Apache Airflow — workflow orchestration platform
Airflow Architecture — Scheduler, Webserver, Metadata DB, Executor
Airflow Executors — Sequential, Local, Celery, Kubernetes
Docker Compose & Helm on Kubernetes installation
Airflow UI — DAG view, graph view, Gantt, logs
airflow.cfg & environment variables configuration
PostgreSQL backend setup
Airflow with Redis & Celery — scale to multiple workers
Airflow Runtime — standard vs ML versions
🔬 Airflow Setup via Docker🔬 First DAG Creation📝 Quiz: Architecture
M02
DAGs, Operators & Sensors
⏱️ 5 Hours● Intermediate
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DAG parameters — schedule_interval, start_date, catchup, max_active_runs
TaskFlow API (@task decorator)
BashOperator, PythonOperator, PythonVirtualenvOperator
BranchPythonOperator — conditional branching
ShortCircuitOperator — skip downstream tasks
TriggerDagRunOperator — trigger another DAG
Sensors — FileSensor, HttpSensor, S3KeySensor, ExternalTaskSensor
XComs — pass data between tasks
TaskGroup — organize tasks visually
🔬 Complex DAG with Branching📝 Quiz: Operators & Sensors
M03
Cloud Providers — AWS, Azure, GCP
⏱️ 5 Hours● Intermediate
▾
S3Hook, GlueCatalogHook — connect to AWS services
RedshiftSQLOperator — SQL on Redshift from Airflow
GlueJobOperator — trigger & monitor AWS Glue jobs
EmrAddStepsOperator — Spark on EMR
AzureDataFactoryRunPipelineOperator
GCP BigQueryInsertJobOperator
DataflowCreateJavaJobOperator
Multi-cloud pipeline orchestration patterns
🔬 AWS S3→Redshift Pipeline🏗️ Project: Multi-cloud ETL
M04
Databricks & Snowflake Operators
⏱️ 4 Hours● Intermediate
▾
DatabricksRunNowOperator — trigger Databricks jobs
DatabricksSubmitRunOperator — submit notebooks & jobs
DatabricksHook for API calls
SnowflakeOperator — SQL execution
SnowflakeHook — connect to Snowflake
S3ToSnowflakeOperator — load S3 data to Snowflake
dbt BashOperator — run dbt commands
DbtCloudRunJobOperator
Cosmos — dbt task groups in Airflow
🏗️ Project: dbt+Airflow+Snowflake
M05
Advanced Airflow Patterns
⏱️ 4 Hours● Advanced
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Dynamic DAGs — generate DAGs programmatically
DAG Factory — YAML-driven DAG generation
Dynamic Task Mapping — .expand() for parallel tasks
KubernetesPodOperator — run tasks in K8s pods
KubernetesExecutor — each task in its own pod
Deferrable Operators — async with Triggers
Airflow Dataset — data-aware scheduling (AIP-48)
Airflow Testing — unit tests for DAGs
🔬 Dynamic Task Mapping Lab📝 Quiz: Advanced Patterns
M06
MWAA & Production Airflow
⏱️ 3 Hours● Advanced
▾
Amazon MWAA — managed Airflow on AWS
MWAA environment sizing — Small, Medium, Large
MWAA DAG deployment — S3-backed storage
MWAA Connections via Secrets Manager
Cloud Composer — managed Airflow on GCP
Airflow Monitoring — CloudWatch, metrics
Airflow Security — RBAC, authentication providers
Airflow upgrade strategies
🔬 MWAA Setup on AWS
M07
End-to-End Airflow Projects
⏱️ 5 Hours● Advanced
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Project 1 — Daily Batch ETL: S3 → Glue → Redshift → Dashboards
Project 2 — Real-time + Batch Hybrid: Kinesis + Airflow
Project 3 — Multi-cloud Pipeline: AWS + GCP orchestration
Project 4 — Databricks + Airflow: Delta Lake ingestion
Project 5 — dbt + Airflow + Snowflake: full modern data stack
SLA monitoring, alerting & retry strategies
Airflow performance tuning for large-scale pipelines
Interview Prep — Top 40 Airflow questions
🏗️ 5 Real Projects📝 Interview Prep
Career Outcomes
Airflow Professionals Earn Top Salaries
Airflow proficiency is a highly valued skill in modern data engineering. Engineers with MWAA and cloud operator expertise command strong salaries.
Entry Level
₹8–14 LPA
0–2 Years
Mid Level
₹14–25 LPA
2–5 Years
Senior Level
₹25–45+ LPA
5+ Years
Student Success Stories
1200+ Professionals Placed at Top Companies
★★★★★
"The dynamic task mapping and KubernetesPodOperator modules were production-grade. The dbt + Airflow + Snowflake project was exactly what my company needed!"
KS
Kiran Sai
ETL Dev → Data Platform Engineer
✅ Amazon · ₹24 LPA
★★★★★
"Trainer Venu covered every Airflow operator for AWS, Azure and GCP. The MWAA lab on real AWS was excellent. Got placed at Wipro within 6 weeks!"
YB
Yamini Bharath
SQL Dev → Airflow Engineer
✅ Wipro · ₹16 LPA
★★★★★
"Morning batch was perfect. Trainer Venu's explanation of DAG scheduling, sensors and XComs was very clear. The cloud operator modules were top-notch!"
RS
Rajan Sharma
Fresher → Junior Data Engineer
✅ TCS · ₹8.5 LPA
FAQs
Frequently Asked Questions
Is this Airflow course suitable for beginners? ▾
Yes! We start with Airflow architecture and Docker setup from scratch. Basic Python knowledge is sufficient. No prior orchestration experience needed.
Does this course cover MWAA (AWS Managed Airflow)? ▾
Yes — Module 6 covers Amazon MWAA in depth: architecture, environment sizing, DAG deployment via S3, Secrets Manager integration and monitoring.
Will I get hands-on practice with real Airflow clusters? ▾
Absolutely. Every module has labs with real Airflow instances on Docker and MWAA. All 5 projects use real AWS/Databricks/Snowflake services.
Does this prepare me for Airflow interviews? ▾
Yes — Module 7 includes Top 40 Airflow interview Q&A, plus resume and LinkedIn optimization for data engineering roles.
What is the refund policy? ▾
7-day money-back guarantee. Attend the free demo and first class — if not satisfied, full refund, no questions asked.