Learn how to build, automate, and manage data pipelines on the AWS Cloud using AWS Glue — a fully managed ETL (Extract, Transform, Load) service.
At Vistasparks Solutions, our AWS Glue Training helps you gain practical skills in data cataloging, job orchestration, Python (PySpark) scripts, and integration with AWS Analytics tools like Athena, Redshift, and S3.
Whether you’re an Individual learner 👩💻 or part of a Corporate team 🏢, this course helps you transform into a Cloud Data Engineer capable of building scalable and automated data pipelines.
AWS Glue is a serverless ETL service from Amazon Web Services that helps automate data preparation, integration, and transformation for analytics and machine learning.
It eliminates manual scripting and infrastructure management — perfect for modern data lake and data warehouse solutions.
✅ Build serverless ETL jobs using PySpark
✅ Automate data movement between S3, Redshift, and RDS
✅ Manage metadata with the AWS Glue Data Catalog
✅ Integrate with AWS Lake Formation and Athena
✅ Learn scalable data integration using AWS Glue Studio
✅ Prepare for AWS Data Engineer or Data Analytics certification
🧩 Module 1: Introduction to AWS Glue
Overview of AWS Glue and Its Architecture
Key Components: Data Catalog, Crawlers, Jobs, Triggers, and Endpoints
AWS Glue Studio & Visual ETL
🧠 Module 2: AWS Glue Data Catalog
Creating Databases and Tables
Understanding Crawlers and Classifiers
Integrating Catalog with Athena and Redshift Spectrum
⚙️ Module 3: ETL Jobs and Scripts
Building and Configuring ETL Jobs
Using AWS Glue Studio for Job Creation
Writing ETL Scripts in Python (PySpark)
DynamicFrame and DataFrame Conversions
☁️ Module 4: AWS Glue with S3, RDS, and Redshift
Extracting Data from Multiple Sources
Transforming and Loading into AWS Redshift
Data Movement between On-prem and Cloud
🔄 Module 5: Triggers, Workflows, and Job Scheduling
Creating and Managing Job Triggers
Building Multi-Step Workflows
Monitoring and Debugging Jobs
🧩 Module 6: AWS Glue DataBrew
Introduction to AWS Glue DataBrew
Visual Data Preparation and Cleaning
Integration with S3 and Redshift
🧱 Module 7: AWS Glue Integration with Analytics
Connecting Glue to AWS Lake Formation
Querying Data using Athena
Integrating with Amazon QuickSight for Visualization
🧪 Module 8: Hands-On Project
Building an End-to-End ETL Pipeline
Automating Data Flow between S3, Glue, and Redshift
Real-Time Data Integration Case Study
🎓 Module 9: Certification & Career Prep
AWS Certified Data Analytics Exam Guidance
Resume Building and Interview Preparation
🎯 1️⃣ Personalized Coaching: 1-on-1 guidance with flexible schedules.
💻 2️⃣ Labs: Practice real AWS Glue pipelines on live AWS environments.
🧠 3️⃣ Expert Mentorship: Learn directly from certified AWS professionals.
📅 4️⃣ Flexible Learning Options: Weekend, weekday, and fast-track batches.
🎓 5️⃣ Certification Support: Prepare for AWS Data Engineer or Analytics exams.
📚 6️⃣ Lifetime Access: Recordings, code templates, and notes for future use.
🚀 7️⃣ Career Boost: Job-ready skills for Data Engineer & Cloud Integration roles.
🤝 8️⃣ Post-Training Mentorship: Continued learning and placement support.
🏭 1️⃣ Custom Learning Path: Designed to match your company’s data workflows.
🌍 2️⃣ Global Delivery: virtual, or hybrid sessions.
🔧 3️⃣ Real-World Case Studies: Enterprise-grade AWS Glue and ETL examples.
📈 4️⃣ Improve Data Efficiency: Automate complex pipelines, reduce manual effort.
🔒 5️⃣ Secure Cloud Integration: Learn IAM roles, data encryption, and access control.
🤝 6️⃣ Post-Training Support: Technical consultation for team projects.
🏆 7️⃣ Corporate Certification: Validate your team’s AWS Glue expertise.
💼 8️⃣ ROI-Driven Results: Boost productivity and cloud adoption.
📞 Get in Touch
📌 Call / WhatsApp: +91-8626099654
📌 Email: contact@vistasparks.com
📌 Website: vistasparks.com
Related Services
AWS Glue is a serverless ETL tool for extracting, transforming, and loading data across AWS services.
Data Engineers, Cloud Developers, and Analytics Professionals.
Basic knowledge of AWS, SQL, or Python.
Basic Python or PySpark scripting knowledge helps but isn’t mandatory.
Yes, learners get AWS account guidance for hands-on labs.
AWS Glue, S3, Athena, Redshift, DataBrew, and Lake Formation.
30–40 hours of theory and practice.
✅ Yes, no prior data engineering experience required.
🎓 Yes, completion certificate from Vistasparks Solutions.
Building an automated data pipeline using AWS Glue and Redshift.
Glue is serverless ETL; Data Pipeline is for scheduled data movement.
Yes, via JDBC connections.
A centralized metadata repository for datasets.
Some components are free; others are pay-per-use.
A visual interface for building and managing ETL workflows.
PySpark scripts define transformation logic for data jobs.
✅ Yes, with customized content and enterprise use cases.
Yes, live instructor sessions with Q&A.
A visual data prep tool for cleaning and profiling data.
Yes, integrated with AWS Glue for data governance.
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