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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.

⏱️ 40 Hours
📦 7 Modules
🔬 15+ Labs
🗂️ 5 Projects
🌐 Live Online
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No prior experience needed
7-day money-back guarantee
Placement support included
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₹25,000
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🎥 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
40
Training Hours
7
Modules
15+
Hands-on Labs
5
Real Projects
1200+
Students Placed
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
📅 Upcoming Batches
Morning Batch 1
6:00 – 7:30 AM IST
Mon–Fri
Morning Batch 2
7:30 – 9:00 AM IST
Mon–Fri
Evening Batch
7:30 – 9:00 PM IST
Filling Fast
Weekend Batch
Sat & Sun
Starting Apr 20
Talk to Trainer Venu
Free demo — no commitment needed
💬 WhatsApp Now 📞 +91-8500002025 📞 +91-8500002025
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
▾
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
▾
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
▾
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
📊 Course Summary
Duration40 Hours
Modules7 Modules
Labs15+ Labs
Projects5 Projects
ModeLive + Recorded
LanguageEnglish / Telugu
Still Deciding?
Attend a free demo — no commitment
💬 WhatsApp Trainer Venu 📞 +91-8500002025
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
View All Placement Stories →
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.
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