AWS Glue Corporate Training
Learn to design, catalog, and operate serverless ETL pipelines with AWS Glue in live guided labs.
Edstellar, a corporate training company, delivers corporate training that helps data engineering teams master AWS Glue: building the Data Catalog, authoring PySpark ETL jobs, and orchestrating reliable workflows at scale. Your team will learn pipeline design, data quality, and storage optimization through instructor-led sessions and hands-on practice.
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 Glue pipeline team
THE EDSTELLAR DATA ENGINEERING CAPABILITY MODEL
Assess pipeline readiness
Skill checks map your team's data engineering baseline before training begins.
Standardize ETL practice
One shared playbook for catalog design, job authoring, and code review.
Practice in guided labs
Labs cover crawlers, PySpark jobs, bookmarks, and workflow monitoring in Glue.
Measure the lift
Lab reviews benchmark each employee's pipeline skill after training.
Scale across teams
One ETL standard rolls out across data lakes, warehouses, and teams.
Need more than one program?Get a group quote
Trusted as a Leading Corporate Training Provider by
Program Overview: What is AWS Glue?
AWS Glue is Edstellar's instructor-led technical program for data engineers building serverless ETL pipelines on AWS. Participants master the Glue data catalog with crawlers and partitioning, ETL jobs with Glue Studio and PySpark, and workflows with triggers and monitoring. The program covers data quality, governance, and performance tuning.
Training is hands-on from the first module. Participants build pipelines in guided labs: cataloging data with crawlers, writing PySpark ETL jobs, orchestrating workflows, then adding data quality checks. Instructors coach pipeline design with direct feedback on every job run. The program closes with a capstone: an end-to-end lakehouse pipeline the team builds and validates.
Data pipelines break the same way: schemas that drift, jobs that fail silently, and quality nobody checks until the dashboard is wrong. This program teaches pipeline discipline, cataloged data, monitored workflows, and quality gates before the warehouse. Everything is practiced on realistic datasets, so the pipelines survive real data.
The capstone delivers an end-to-end lakehouse pipeline: raw data cataloged, transformed with PySpark, quality-checked, and orchestrated into a governed lakehouse, running on a schedule, monitored, documented. Data engineers return with a pipeline pattern their team can clone for the next data source.
Across six modules, your team moves from console setup and catalog design to production workflows. Guided labs mirror real lakehouse work: crawling landing zones, transforming raw files into curated tables, adding quality gates, and monitoring runs. Every employee practices the ETL patterns their pipelines will use on day one.
What's Included in this Workshop for Your Enterprise Teams
- 1
Skill assessment
Baseline checks align the program to your team's pipeline experience.
- 2
Six guided modules
From catalog setup to a production-style capstone build.
- 3
Hands-on labs
Every module ends with practice on real Glue jobs and workflows.
- 4
Capstone review
Your team ships a lakehouse pipeline and defends it in review.
- 5
Learning materials
Job templates, DQDL samples, and tuning checklists to keep.
- Instructor-led sessions across 24 hours
- Hybrid, onsite, or live virtual delivery
- Guided labs in a shared AWS practice environment
- Small group cohorts for focused coaching
- Scheduling around your team's working hours
- Post-training support and lab references
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
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
AWS Glue Course Topics and Program Outline
Six modules take your team from Glue fundamentals to a production-style lakehouse pipeline, with guided labs in every module.
Built by Edstellar with data engineers
Built by practitioners who run Glue ETL pipelines in production, shaped around your stack and use cases.
Skill checks baseline your team's ETL experience, SQL fluency, and Glue familiarity before training begins.
Live instruction covers Glue architecture, Data Catalog design, crawlers, and serverless ETL patterns.
Guided labs build ETL, orchestration, and monitoring skill across 8 hours of hands-on pipeline work.
Your team ships a production-style lakehouse pipeline in 9.5 hours, reviewed against production criteria.
From first crawlers 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.
Provision an S3 data lake layout, configure IAM roles for Glue, and run a first crawler to register a sample table.
learna)Glue architecture: Data Catalog, crawlers, ETL engine, and dev endpoints
b)Console navigation, IAM roles, and security setup for data teams
c)S3 data lake zones: landing, raw, and curated storage patterns
d)First guided lab: configure the environment and load sample data
Run a crawler against multi-format S3 data, tune partition keys, and validate the catalog with Athena queries.
learna)Catalog databases, tables, and schema versioning concepts
b)Crawler configuration: classifiers, schedules, and incremental crawls
c)Partitioning strategies for faster, cheaper queries
d)Guided lab: crawl, partition, and query data with Athena
Author a PySpark ETL job in Glue Studio that joins two sources, applies transforms, and loads incrementally with bookmarks.
practicea)Glue Studio visual jobs and the script editor workflow
b)PySpark scripts: ApplyMapping, Filter, Join, and DropNullFields
c)DynamicFrames versus DataFrames for schema flexibility
d)Job bookmarks for incremental processing
e)Guided lab: build a multi-source ETL job with transforms
Chain ETL jobs into a workflow with event triggers, then add CloudWatch alarms and retry policies for failed runs.
practicea)Glue workflows: graphs, properties, and run propagation
b)Schedule, on-demand, and event-based triggers
c)CloudWatch metrics, logs, and job run states
d)Error handling, retries, and notification patterns
e)Guided lab: orchestrate a workflow and add monitoring
Define a DQDL ruleset on a curated table and retune an ETL job, cutting run time through better partitioning and worker sizing.
applya)Glue Data Quality: rulesets, DQDL, and evaluation schedules
b)Lake Formation permissions on catalog tables
c)Encryption, VPC endpoints, and access control for Glue
d)DPU allocation, worker types, and pushdown predicates
e)Guided lab: attach quality checks and tune a slow job
Ship a complete landing-to-curated pipeline with crawlers, ETL jobs, data quality checks, and a monitored workflow, reviewed against production criteria.
applya)Capstone brief: a retail orders pipeline on a fixed timeline
b)Crawler and catalog design for the landing zone
c)ETL build: transforms, bookmarks, and quality gates
d)Workflow orchestration and monitoring review
e)Instructor review: production-readiness scorecard
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 serverless ETL architectures
Choose Glue components, S3 zones, and compute sizing for pipeline workloads.
Build and govern the Data Catalog
Configure crawlers, tables, and partitions teams can discover and trust.
Author PySpark jobs in Glue Studio
Write transforms, manage schema drift, and load incrementally with bookmarks.
Orchestrate workflows and triggers
Chain jobs into reliable workflows with schedules and event triggers.
Enforce data quality with Glue DQ
Author DQDL rulesets and gate pipeline runs on quality results.
Tune performance and control cost
Right-size DPUs, push down predicates, and partition for speed and savings.
Monitor and troubleshoot pipelines
Read CloudWatch signals, set alarms, and recover failed runs confidently.
Teams that Benefit from this AWS Glue Group Training
Built for data teams moving ETL workloads onto AWS Glue. Content adapts to role and experience, so each cohort practices the workflows it owns.
Ship dependable Glue pipelines on schedule
Your data engineers will standardize how ETL jobs are authored, orchestrated, and monitored in Glue.
Workflow friction
- Ad-hoc scripts with no shared standards
- Fragile schedules and silent failures
- Slow onboarding to pipeline tooling
How the program helps
- Consistent PySpark job authoring standards
- Reliable workflow orchestration patterns
- Faster troubleshooting of failed runs
Practice use cases
- ETL job templates
- workflow design
- run monitoring
Turn raw landings into trusted curated tables
Your analytics engineers will build the catalog and transform layers downstream teams query.
Workflow friction
- Unreliable source data shapes
- Manual rework after schema changes
- Unclear table ownership
How the program helps
- Clean catalog tables for analytics
- Reusable transform patterns
- Bookmark-driven incremental loads
Practice use cases
- curated table builds
- incremental loads
- catalog governance
Run Glue as a governed platform service
Your platform team will operate Glue with the guardrails, IAM roles, and cost controls pipelines need.
Workflow friction
- Overprovisioned Glue job spend
- Inconsistent access controls
- Limited pipeline observability
How the program helps
- Standard IAM and VPC patterns
- Cost-aware DPU and worker sizing
- Lake Formation permission models
Practice use cases
- platform guardrails
- cost optimization
- access management
Feed dashboards from quality-checked data
Your BI teams will depend on curated Glue tables with quality gates upstream of every dashboard.
Workflow friction
- Dashboard breaks from bad data
- Stale refreshes with no alerts
- No visibility into pipeline health
How the program helps
- Trusted data for every report
- Faster refresh through tuned jobs
- Clear data lineage from source
Practice use cases
- curated marts
- refresh SLAs
- quality gates
Cohorts train as a cross-functional workforce, with each small group aligned on the pipelines it operates.
Prerequisites: Best suited to data engineers, analytics engineers, and BI developers with working SQL and basic Python. No prior Glue experience is needed; AWS console familiarity helps but is not required. The program adapts to each cohort's data stack and pipeline context.
Data analyst to data engineer
Each skill moves from guided foundations to independent production practice across the six modules.
One program that grows with data engineering careers, from catalog basics to confident production 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
Blend live virtual sessions with onsite lab days so distributed teams learn together without travel overhead.
- Same curriculum across virtual and onsite days
- Labs run in a shared AWS practice environment
- Instructor support during every hands-on session
- Schedule tuned to your team's working hours
Onsite Training
The instructor delivers the full program at your office, with labs tailored to your data stack and pipeline goals.
- Full program delivered at your location
- Examples mapped to your S3 zones and sources
- Direct coaching during every lab
- Team workshops aligned to real backlogs
Live Virtual Training
Live virtual sessions with real-time instruction, screen-shared demos, and guided labs in cloud environments.
- Live instruction, not recorded videos
- Breakout coaching during hands-on labs
- Shared AWS practice environment access
- Recordings and materials for revision
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 and timelines through the request form and you will receive a tailored proposal with batch options and pricing.
Yes. A short discovery call aligns the program with your data stack, pipeline goals, and cohort experience levels.
Yes. Live virtual sessions run instructor-led with guided labs in a shared AWS practice environment.
Yes. Onsite delivery is available and labs can be mapped to your S3 zones and data sources.
The program runs 24 hours of instructor-led sessions, typically scheduled as 3 to 4 days or spread across weeks.
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.
AWS Glue jobs exist to feed analytical stores, so warehouse basics come first. Your team then builds Glue jobs, loads the results into Redshift, and schedules the full flow in Airflow so it runs reliably without manual steps.
Why Choose Edstellar as Your AWS Glue Corporate Training Provider
Edstellar pairs instructor-led delivery with hands-on Glue 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, S3 zones, and Glue workloads.
Proven Methodology
A learn, practice, apply sequence moves teams from catalog basics to capstone delivery with steady coaching.
Practice-Based Learning
Hands-on labs on crawlers, PySpark jobs, and workflows build muscle memory your team keeps after training.
Expert Trainers
Practitioners who run Glue pipelines in production teach the tuning and recovery patterns that matter.
Global Scale
One program standard rolls out across regions and time zones for consistent ETL capability worldwide.
Measurable Outcomes
Skill checks, lab reviews, and a production-style capstone show exactly what your team can now deliver.
Also from Edstellar
Talent assessments
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?
AWS Glue 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 AWS Glue Training for Your Team
Share your team size, data stack, pipeline goals, and timelines to receive a tailored proposal for this AWS Glue program.