Data Engineering on AWS (Redshift, Glue and Lake Formation) Corporate Training
Build analytics platforms on AWS with Redshift, Glue, and Lake Formation, in live guided labs.
Edstellar's instructor-led corporate training takes your team from raw data in S3 to governed analytics in Redshift, with hands-on labs on Glue ETL, Lake Formation security, and dashboard design throughout.
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 data engineering team
The Edstellar DATA ENGINEERING Capability Model
Diagnose data capability
Skill checks map your team's AWS, SQL, and pipeline baseline.
Standardize the lakehouse
Shared patterns for ingestion, storage, and modeling take hold.
Practice in labs
Guided labs on Glue, Redshift, and Lake Formation build muscle memory.
Measure pipeline health
Data quality and performance checks become part of every build.
Scale to production
Governance and monitoring patterns carry the work into live systems.
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Program Overview: What is Data Engineering on AWS (Redshift, Glue and Lake Formation)?
Data Engineering on AWS (Redshift, Glue & Lake Formation) is an instructor-led corporate training program for data teams building analytics platforms on AWS. Across 24 hours, participants move through the full lifecycle: landing data in S3, transforming it with AWS Glue, warehousing it in Amazon Redshift, and securing it with Lake Formation. The program ends with teams publishing dashboards that decision makers can trust.
Each module opens with a focused concept session and moves straight into guided labs on live AWS environments, where teams build real pipelines instead of watching demos. Instructors coach them through Glue job design, Redshift performance tuning, and Lake Formation access policies, reviewing every lab before the cohort advances. Practice builds across three review sessions toward the capstone, an end-to-end analytics pipeline with a published dashboard. All feedback is tuned to the team's own AWS estate and data sources.
Too many data lakes quietly become swamps: raw files nobody trusts, Redshift clusters that crawl at quarter end, and governance applied as an afterthought. This program teaches teams to design the lakehouse properly from the start, with lineage, access control, and performance habits built into every layer. They return running platforms where the data is findable, fast, and safe.
For the capstone, each team designs and builds a complete analytics pipeline on AWS, from S3 ingestion through Glue transformation and Redshift warehousing to a governed dashboard, then presents it to instructors as if to their own leadership. The grading weighs what matters in production: query speed, cost discipline, and whether Lake Formation permissions actually hold. Teams walk out with a tested architecture pattern they can clone onto their next business unit instead of starting from a blank console.
Across six modules your team moves from architecture choices to a working capstone pipeline. Every concept is reinforced in guided hands-on labs using the same services your production platform runs on: S3 for storage, Glue for ETL, Redshift for warehousing, and Lake Formation for governance. By the end, each cohort can design a lakehouse, build reliable pipelines, tune warehouse performance, and enforce the access controls that keep data trustworthy.
What's Included in this Workshop for Your Enterprise Teams
- 1
Lakehouse architecture
Design S3 zones and service patterns for your workloads.
- 2
Glue ETL pipelines
Build, schedule, and monitor production-grade ETL jobs.
- 3
Redshift warehousing
Model, load, and tune a warehouse that answers fast.
- 4
Lake Formation governance
Enforce fine-grained access and audit trails.
- 5
Capstone build
Ship a full pipeline and demo it to stakeholders.
- Live instructor-led sessions, virtual or onsite
- Hands-on guided labs in every module
- Cohort scheduling around your team's calendar
- Pre-training skill assessment and gap review
- Post-training action plan and handover notes
- Group quote and batch planning support
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
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- Hands-On Labs on Live Infrastructure, Led by Industry Experts
Data Engineering on AWS (Redshift, Glue and Lake Formation) Course Topics and Program Outline
Six modules take your team from lakehouse architecture to a governed, production-style analytics pipeline on AWS. Learn phases cover S3 and service design; practice phases build Glue ETL and Redshift warehouses; apply phases enforce Lake Formation governance and ship the capstone dashboard.
Built by Edstellar with data engineering practitioners
Built by engineers who build analytics platforms on AWS in production, shaped around your stack and use cases.
Skill checks baseline your team's AWS, SQL, and pipeline habits before the program begins.
Instructors explain lakehouse patterns, service roles, and governance concepts with live demos.
Guided labs on Glue, Redshift, and Lake Formation turn concepts into working pipelines.
Your team ships a capstone pipeline and dashboard reviewed by the instructor.
From lakehouse design to a capstone analytics pipeline, every skill is practiced in guided labs.
Delivered as
Delivered as a hybrid program of live instruction and guided labs, scheduled around your team's calendar in flexible batches.
Map your team's data estate to an AWS lakehouse design in a guided workshop.
learna)AWS data service map: S3, Glue, Redshift, Athena, Lake Formation
b)Lakehouse architecture patterns and data zoning
c)Raw, curated, and consumption layers
d)Cost, performance, and scalability trade-offs
Create an S3 data lake layout with partitions, encryption, and lifecycle rules.
learna)S3 buckets, prefixes, and partitioning strategies
b)Batch and streaming ingestion options
c)Storage classes, lifecycle rules, and encryption
d)Data quality checks on landing
Build a Glue ETL job that cleanses, transforms, and catalogs a raw dataset.
practicea)Glue Data Catalog and crawlers
b)Visual and script-based ETL jobs in Glue Studio
c)Schema evolution and bookmark management
d)Job monitoring, bookmarks, and failure recovery
e)Scheduling workflows with Glue triggers
Load a warehouse, tune a slow query with sort keys and distribution styles.
practicea)Redshift architecture: clusters, nodes, and slices
b)Star schema modeling and COPY loading
c)Distribution styles and sort keys
d)Query tuning with EXPLAIN and WLM
e)Concurrency scaling and maintenance
Grant column-level access in Lake Formation and verify it with audit logs.
applya)Lake Formation permissions model
b)Fine-grained access: column and row level
c)Tag-based access control (LF-Tags)
d)Auditing access with CloudTrail logs
e)Cross-account data sharing
Ship a full lake-to-dashboard pipeline and present it for instructor review.
applya)Solution design and service selection
b)Building the full pipeline end to end
c)Governance and access validation
d)Dashboard design and stakeholder demo
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
Practical, pipeline-tested skills your team applies to your own AWS data platform from week one.
Lakehouse architecture design
Choose the right mix of S3, Glue, and Redshift for each workload.
S3 data lake engineering
Partition, encrypt, and lifecycle-manage raw and curated datasets.
Glue ETL development
Author, schedule, and monitor reliable ETL jobs in Glue Studio.
Redshift modeling and tuning
Build star schemas and tune queries for fast analytics.
Lake Formation governance
Enforce column and row level access with full audit trails.
Pipeline monitoring
Track job health, data quality, and failures end to end.
Dashboard publishing
Deliver governed datasets to decision-ready dashboards.
Teams that Benefit from this Data Engineering on AWS (Redshift, Glue and Lake Formation) Group Training
Built for data engineers, BI developers, analysts, and platform teams who own analytics on AWS. Cohorts can come from one team or across functions, and the labs adapt to each group's tools and experience so every cohort practices the workflows it owns.
Pipelines your team can trust
Your engineers learn to build, schedule, and monitor Glue ETL that data consumers rely on.
Workflow friction
- Brittle manual pipelines
- Slow incident response
- Rework across projects
How the program helps
- Reliable ETL job patterns
- Faster failure recovery
- Reusable pipeline templates
Practice use cases
- Glue job authoring
- Bookmark management
- Workflow scheduling
Warehouses that answer fast
Analysts and BI developers learn to model and tune Redshift for the queries your business runs.
Workflow friction
- Slow report performance
- Inconsistent metrics
- Stale warehouse data
How the program helps
- Faster dashboard queries
- Cleaner semantic models
- Self-serve ready datasets
Practice use cases
- Star schema design
- Query tuning
- Dashboard delivery
Governance that scales
Platform teams learn Lake Formation controls that protect data without slowing delivery.
Workflow friction
- Sprawling S3 access
- Unclear data ownership
- Audit pressure
How the program helps
- Centralized access control
- Audit-ready permissions
- Safe data sharing
Practice use cases
- LF-Tag policies
- Cross-account sharing
- Access auditing
Cohorts train as a cross-functional workforce, with each small group aligned on shared data and quality standards.
Prerequisites: This program suits teams with basic SQL and some exposure to AWS. You do not need prior Glue or Redshift experience; the labs introduce each service from setup through production patterns. Cohorts benefit from knowing their own data sources, but no prior data engineering background is required.
Data engineer to analytics lead
The progression tracks how each skill deepens through the program, from foundations in the learn phase to production-style practice in the capstone.
A program that grows with data careers, from cloud basics to confident pipeline practice.
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's schedule. Every mode includes the same live instruction and guided labs.
Hybrid Training
Part of your team joins in the room and part joins live online, with the instructor guiding both through every lab.
- Live instructor for room and remote learners
- Guided labs with shared troubleshooting
- Flexible batch scheduling
- Same capstone for every participant
Onsite Training
The instructor trains your team at your office, working through labs on your network and data patterns.
- Instructor at your location
- Labs tuned to your data estate
- Team workshops and whiteboard sessions
- Minimal disruption to work hours
Live Virtual Training
Your team joins live online sessions with real-time instructor support through every guided lab.
- Live online instructor support
- Breakout rooms for group labs
- Screen-share troubleshooting
- Recorded demos for review
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 per-group quote with batch options.
Yes. A short discovery call aligns the program with your data stack, team roles, and business goals before scheduling.
Yes. Live virtual sessions run with the same instructor, labs, and capstone as onsite and hybrid modes.
Yes. Onsite training brings the instructor to your location with labs tuned to your data estate and tools.
The program runs 24 instructor-led hours, typically delivered across three to four days in flexible batches.
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.
Data engineering on AWS goes faster when your team already knows the AWS basics. This path covers those, then Redshift, Glue and Lake Formation, then orchestration with Airflow, and ends with the practices that keep pipelines reliable in production.
Why Choose Edstellar as Your Data Engineering on AWS (Redshift, Glue and Lake Formation) Training Provider
Edstellar delivers corporate training that fits how your data team actually works, with labs on the services you run.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Labs, datasets, and the capstone are tuned to your AWS setup, data sources, and analytics workloads.
Proven Methodology
A learn, practice, apply arc moves your team from service basics to production-ready pipeline skills.
Practice-Based Learning
Six guided labs plus a capstone mean your team builds real pipelines, not just slideware.
Expert Trainers
Instructors are working data engineers with production AWS analytics experience.
Global Scale
Consistent delivery across regions and time zones for distributed data teams.
Measurable Outcomes
Skill checks, lab reviews, and the capstone demo show exactly what your team can now build.
Connect AWS Data Services Into Governed Lake-to-Dashboard Workflows
Edstellar develops AWS data engineering through S3 layout, Glue transformations, Redshift performance, and Lake Formation controls. Participants connect these services in a representative analytics pipeline before presenting its outputs. Review focuses on transformation accuracy, query evidence, access verification, and the rationale linking architectural decisions with the dashboard's data needs.
- Step 01
Consult
We review SQL and AWS familiarity to identify gaps in lakehouse layout, ETL development, warehouse performance, and fine-grained data access.
- Step 02
Plan
We establish AWS lakehouse foundations before S3 ingestion and Glue jobs, then Redshift tuning and Lake Formation governance within the integrated analytics pipeline.
- Step 03
Align
We align AWS data instruction with the cohort's source systems and SQL readiness, confirming suitable practice resources for service setup, tuning, and access verification.
- Step 04
Customize
We tailor raw datasets, analytical workloads, and column-access requirements to representative business needs while keeping permissions and lifecycle assumptions explicit in the sandbox.
- Step 05
Execute
Participants build Glue transformations, tune Redshift queries, and verify access logs, receiving feedback on data consistency, storage design, performance evidence, and governance choices.
- Step 06
Evaluate
We assess the lake-to-dashboard pipeline for correct transformations, justified warehouse tuning, verified access controls, and a clear explanation of the end-to-end architecture.
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?
Data Engineering on AWS (Redshift, Glue & Lake Formation) Training 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 Data Engineering on AWS (Redshift, Glue and Lake Formation) Training for Your Team
Tell us about your team size, timelines, and AWS setup. We will design a training plan with the right batches, labs, and capstone focus for your data platform.