Apache Airflow Corporate Training
Learn to design, schedule, and operate production data pipelines with Apache Airflow in live guided labs.
Edstellar, a corporate training company, delivers corporate training that helps data engineering teams master Apache Airflow: designing DAGs, enforcing data quality, and operating reliable schedules at scale. Your team will learn scheduling, monitoring, and integration patterns through instructor-led, hands-on labs built around real-world use cases.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a production-ready pipeline team
THE EDSTELLAR DATA ENGINEERING CAPABILITY MODEL
Assess pipeline readiness
Skill checks map your team's data engineering baseline before training begins.
Standardize orchestration
One shared playbook for DAG design, naming, schedules, and code review.
Practice in guided labs
Labs cover authoring, validation, backfills, and monitoring in Airflow.
Measure the lift
Lab reviews benchmark each employee's pipeline skill after training.
Scale across teams
One orchestration standard rolls out across pipelines, warehouses, and teams.
Need more than one program?Get a group quote
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Program Overview: What is Apache Airflow?
Apache Airflow is an instructor-led program for data engineering teams adopting Airflow as its orchestration standard. Participants learn to design DAGs with tasks and operators, build dynamic pipelines with task mapping, and add data quality gates that block bad loads. Across 24 hours, the training covers scheduling, scaling, monitoring, and integrations with warehouses and storage.
Every module pairs live instruction with guided labs where learners author, trigger, and debug real pipelines. Instructors coach teams through DAG authoring patterns, idempotent retryable tasks, backfills, SLAs, and failure callbacks, with feedback on each build. Learners practice extraction with sensors, schema validation, executor scaling, and monitoring dashboards. The program closes with a five-hour capstone where each team builds an end-to-end data pipeline and presents it for instructor production review.
Without these skills, teams rely on brittle cron jobs and manual handoffs that fail silently and deliver late data. This program replaces that fragility with orchestrated pipelines that validate data before it moves, retry failed tasks, and alert owners through SLAs. Graduates schedule production loads, run backfills safely, and keep pipelines observable with dashboards.
They operate a pipeline, not just build one, scheduling, backfills, SLAs, alerts. Your data team stops firefighting DAGs at 2 a.m.
Across six modules and 24 hours, employees move from Airflow fundamentals to a production-style capstone pipeline: authoring dynamic DAGs, validating data before it moves, configuring backfills and SLAs, and integrating warehouses, databases, and object storage. Every module pairs live instruction with guided labs, so the team practices the exact workflows it will own, from first DAG to on-call confidence.
What's Included in this Workshop for Your Enterprise Teams
- 1
Airflow foundations
Architecture, DAGs, and a local setup you will use daily.
- 2
Dynamic DAG authoring
Task mapping, templating, and idempotent task design.
- 3
Quality-gated pipelines
Validation checks that stop bad data before it spreads.
- 4
Production operations
Backfills, SLAs, alerting, and scaling practices.
- 5
Capstone build
A full production-style pipeline, presented for review.
- Instructor-led sessions, onsite or live virtual
- Guided Airflow labs on your data stack
- Reusable DAG templates and code samples
- Lab guides and session recordings on your LMS
- Capstone review against production criteria
- Post-training support from your instructor
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
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Apache Airflow Course Topics and Program Outline
A tailored 24-hour, six-module workshop: one learning journey from Airflow setup to a production-style capstone pipeline.
Built by Edstellar with data engineers
Built by practitioners who run Airflow pipelines in production, shaped around your stack and use cases.
Skill checks baseline your team's pipeline experience, SQL fluency, and Airflow familiarity before training begins.
Live instruction covers Airflow architecture, DAG authoring, dynamic task mapping, and reliable pipeline patterns.
Guided labs build scheduling, validation, and monitoring skill across 8 hours of hands-on pipeline work.
Your team ships a production-style pipeline in 9.5 hours, reviewed against production readiness criteria.
From first DAGs to a production pipeline, every technique is practiced in guided labs.
Delivered as
Delivered as a hybrid program of live instruction and guided labs, scheduled around your team's working hours.
Configure a local Airflow development environment, author a three-task DAG, trigger it in the UI, and inspect task logs and rendered templates.
learna)Airflow architecture: scheduler, executor, web server, metadata database
b)DAGs, tasks, operators, and task dependencies in the web UI
c)Local development setup and configuration for daily practice
d)Authoring, testing, and triggering a first pipeline
Author a DAG that uses dynamic task mapping to process multiple datasets in parallel, add branching logic, and verify idempotent retries in the UI.
learna)DAG authoring patterns and scheduling configuration
b)Python operators, branching, and templated fields
c)Dynamic task mapping for dataset-parallel pipelines
d)Best practices for idempotent, retryable tasks
Build an ingestion pipeline with sensor waits, add validation tasks that block downstream loads on bad data, and test failure and recovery paths.
practicea)Extraction and loading patterns with operators and sensors
b)Data validation and schema checks inside pipelines
c)Quality gates that block downstream tasks on failure
d)Handling late, missing, and malformed data gracefully
Configure a production-style schedule, run a historical backfill, tune retries and SLAs, and set up alerting and log review for a running deployment.
practicea)Schedules, catchup, backfills, and reruns in production
b)Retries, SLAs, alerting, and failure callbacks
c)Executor and worker scaling strategies for growing teams
d)Monitoring dashboards and log management for pipelines
Wire Airflow to a warehouse and object storage, build an ELT pipeline with staged loads, and orchestrate transformation steps from the DAG.
applya)Connections, hooks, and secret management for data stores
b)Orchestrating ELT: loading warehouses and object storage
c)Integrating transformation tools into Airflow pipelines
d)File-based workflows and partitioned data handling
Design and build a complete end-to-end pipeline with quality gates, a production schedule, and monitoring, then present it for instructor review.
applya)Designing a pipeline from requirements to DAG plan
b)Building ingestion, validation, and load tasks end to end
c)Applying scheduling, backfills, and alerting
d)Presenting the build for instructor production review
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Seven job-ready outcomes your team will demonstrate by the end of the program.
Design reliable DAGs
Author DAGs with clear task dependencies, safe schedules, and retryable tasks.
Use dynamic task mapping
Scale one DAG across hundreds of datasets with dynamic task mapping and templating.
Build quality gates
Add validation and schema checks so bad data never reaches downstream consumers.
Schedule production loads
Configure catchup, backfills, SLAs, and alerting for reliable production schedules.
Integrate data stores
Connect warehouses, databases, and object storage with managed connections.
Orchestrate transformations
Coordinate ELT and transformation steps from Airflow with clean lineage.
Monitor and operate
Watch pipelines with dashboards, logs, and callbacks, and recover from failures fast.
Teams that Benefit from this Apache Airflow Group Training
This group training is built for data and platform teams that want reliable, observable pipelines. Content adapts to each role and experience level, so every cohort practices the workflows it owns.
Standardize orchestration with Airflow patterns your team owns.
Give data engineers a shared Airflow standard for DAG design, scheduling, and monitoring that replaces fragile scripts with reviewed, testable pipelines.
Workflow friction
- Cron scripts nobody dares to touch anymore
- Silent pipeline failures discovered too late
- Inconsistent schedules across teams
How the program helps
- Replace brittle cron jobs with reviewed, testable DAGs
- Ship ingestion and ELT pipelines with confidence
- Standardize schedules, retries, and alerting
Practice use cases
- Daily ELT orchestration
- Backfill automation
- Ingestion pipelines
Orchestrate transformations with reliable Airflow DAGs.
Help analytics engineers schedule and monitor transformation workloads in Airflow, with data quality checks that protect the datasets downstream teams use.
Workflow friction
- Manual refreshes and ad-hoc job triggers
- Broken downstream data from bad upstream loads
- No clear owner for failed scheduled runs
How the program helps
- Schedule transformation runs with clean lineage
- Add quality gates before downstream delivery
- Coordinate models, marts, and refreshes reliably
Practice use cases
- Transformation scheduling
- Dataset refresh SLAs
- Quality-gated delivery
Run Airflow as a shared, reliable platform service.
Enable platform teams to deploy, scale, and monitor Airflow for multiple consumers, with connection management, secrets, and observability built in.
Workflow friction
- Ad-hoc Airflow installs across teams
- Scaling pain as DAG counts grow
- Inconsistent security and secrets handling
How the program helps
- Serve one reliable Airflow platform to all teams
- Manage connections, secrets, and access centrally
- Scale workers and monitor platform health
Practice use cases
- Shared Airflow platform
- Worker autoscaling
- Connection governance
Keep production pipelines healthy around the clock.
Support data operations teams with scheduling discipline, backfill procedures, and monitoring habits that keep SLAs green and incidents short.
Workflow friction
- Missed SLAs with no early warning
- Manual backfills that take days to plan
- Alert fatigue from noisy failure channels
How the program helps
- Cut incident response time on failed pipelines
- Run clean backfills without manual heroics
- Keep schedules and SLAs visible to everyone
Practice use cases
- SLA monitoring
- Incident response drills
- Scheduled backfills
Cohorts train as a cross-functional workforce, with each small group aligned on the pipelines it operates.
Prerequisites: This program benefits data engineers, analytics engineers, and platform teams who build or run data pipelines. Basic Python and SQL familiarity helps, but is not required: labs start with fundamentals and adapt to each cohort's tools and context. No prior Airflow experience is needed.
Data Analyst to Data Platform Lead
Start with Airflow setup and DAG authoring, then advance through dynamic pipelines, quality gates, and production operations to full pipeline ownership.
A program that grows with data engineering careers, from first DAG to confident pipeline ownership.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Choose the format that fits your team: hybrid, onsite, or live virtual. Every format includes the same instructor-led sessions and guided labs.
Hybrid Training
Instructor-led sessions mix onsite and virtual delivery, with guided labs running against your data stack throughout.
- Same instructor across onsite and virtual sessions
- Guided labs in every module, wherever learners join
- Recordings and lab guides shared on your LMS
- Scheduling flexes around your team's working hours
Onsite Training
Instructor-led training at your offices, with labs set up on your data stack and pipelines.
- Whiteboard sessions with your actual pipeline diagrams
- Labs on your tools, data, and warehouse
- Direct coaching from a practicing data engineer
- Team builds together in one room, one standard
Live Virtual Training
Live virtual sessions on Zoom or Teams with screen-shared pairing and breakout labs.
- Live instructor-led training on Zoom or Teams
- Breakout rooms for paired lab work
- Screen-shared debugging with your instructor
- Same labs and capstone as the onsite program
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share your team size, timelines, and delivery preference, and we will send a detailed quote for this instructor-led program.
Yes. Book a discovery call and we will map your pipelines, Airflow usage, and goals to the curriculum.
Yes. Live virtual sessions run on Zoom or Teams with the same guided labs and instructor support.
Yes. The instructor runs the full program at your offices, with labs set up on your tools and data.
The program runs 24 hours of instructor-led training, usually across three to six days.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Airflow DAGs are written in Python, so Python comes first. Once your team can orchestrate pipelines, dbt handles the transformation layer the DAGs trigger, and DataOps practice keeps the whole system reliable in production.
Why Choose Edstellar as Your Apache Airflow Corporate Training Provider
Edstellar pairs instructor-led delivery with hands-on Airflow labs so your data engineering team practices the pipelines it owns.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Every cohort's labs and examples are tailored to your data stack, pipelines, and Airflow setup.
Proven Methodology
A field-tested methodology moves your team from first DAGs to production-ready pipelines.
Practice-Based Learning
Guided labs in every module turn Airflow concepts into practiced skill on real pipelines.
Expert Trainers
Learn from data engineers who run Airflow pipelines in production every day.
Global Scale
One consistent program delivered across your regions, time zones, and engineering teams.
Measurable Outcomes
Lab checkpoints and capstone reviews show exactly what each employee can build and operate.
Build Airflow Pipelines With Quality Gates and Observable Recovery
Edstellar helps data teams connect orchestration logic with the operational behavior of a pipeline. Participants author DAGs, map tasks dynamically, validate data, and practice scheduling and backfills before integrating storage and transformation steps. A complete pipeline is reviewed through execution evidence, quality controls, monitoring, and recovery from failures.
- Step 01
Consult
We review pipeline responsibilities and Python or SQL familiarity to identify needs in DAG design, retries, data-quality gates, scheduling, and deployment monitoring.
- Step 02
Plan
We connect Airflow setup and task authoring with dynamic mapping, validation, scheduling, and storage integration before completing an end-to-end orchestration build.
- Step 03
Align
We align instruction with data and platform engineers’ environments, using local development and practice deployments to make task state and operational ownership visible.
- Step 04
Customize
We tailor sample datasets, sensor waits, and backfill scenarios to familiar ingestion patterns while preserving idempotency, downstream blocking, and clear failure-recovery expectations.
- Step 05
Execute
Participants build DAGs, test quality gates, and inspect retry and backfill behavior, receiving feedback on dependencies, logs, alerting, and the reliability of recovery paths.
- Step 06
Evaluate
We assess the pipeline for correct task dependencies, effective data-quality controls, reproducible scheduling, and monitoring evidence that explains both successful loads and failures.
Also from Edstellar
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Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Apache Airflow Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request Apache Airflow Training for Your Team
Share your team size, data stack, pipeline goals, and timelines to receive a tailored proposal for this Airflow program.