Requirements
- Basic programming understanding (Python preferred)
- Basic SQL (SELECT, JOIN, GROUP BY)
- Comfort with files (CSV/JSON/Parquet basics)
- Basic Linux/CLI helpful (not mandatory)
- Laptop + stable internet (for labs)
- AWS account for practice (free-tier guidance provided)
- Anyone preparing for AWS data engineering interviews and projects
Features
- Live Project-Based Training
- Expert-Led Sessions
- Flexible Learning Options
- Interactive Learning
- One-on-One Mentorship
Target audiences
- College students and freshers aiming for Data Engineer roles
- Software developers moving into data engineering
- Data analysts who want to build pipelines (not just dashboards)
- ETL/BI professionals shifting to AWS cloud
- Working professionals upskilling for better projects and hikes
If you’re aiming for a data engineering role, AWS Data Engineering Training should go beyond theory and straight into building pipelines that actually run. At Ascents Learning, we focus on hands-on work: designing a data lake on S3, transforming data with Glue, streaming events with Kinesis, and serving analytics with Redshift and Athena. You’ll practice the same patterns teams use daily—batch + streaming, orchestration, monitoring, and cost control.
This AWS Data Engineering Training is structured around weekly assignments and a capstone so you don’t just “learn services,” you learn how they connect. Example: ingest CSV/JSON to S3 → catalog with Glue Data Catalog → transform with Glue jobs → load to Redshift → query in Athena → schedule using Step Functions or MWAA. Ascents Learning also supports resume + project portfolio guidance so you can clearly present your work.
Whether you’re switching from testing/dev, starting after graduation, or upskilling at work, Ascents Learning keeps the sessions practical, with mentor feedback and interview-style discussions. If you want AWS Data Engineering Training that feels like real project work—not a slide show—this is built for that. Learn job-ready AWS data engineering with Ascents Learning.
Curriculum
- 15 Sections
- 71 Lessons
- 10 Weeks
- Module 1: Data Engineering Foundations6
- 1.1What a Data Engineer does (daily tasks, typical project flow)
- 1.2Data pipeline basics: ingest → store → process → serve
- 1.3Batch vs streaming (where each fits, examples)
- 1.4ETL vs ELT (and what modern teams prefer)
- 1.5Data quality basics: duplicates, nulls, schema drift
- 1.6Intro to lake/lakehouse/warehouse (simple comparison)
- Module 2: AWS Core for Data Engineers (Must-Know)5
- Module 3: Amazon S3 Deep Dive (Data Lake Storage)5
- Module 4: Data Formats + Performance Basics5
- Module 5: AWS Glue Basics (Catalog + Crawlers)5
- Module 6: Glue ETL with PySpark (Real Transformations)5
- Module 7: Lake Formation (Governance + Permissions)5
- Module 8: Amazon Athena (Querying S3 Like a Pro)5
- Module 9: Amazon Redshift (Warehouse Layer)5
- Module 10: Data Ingestion Patterns (DMS + Common Sources)5
- Module 11: Streaming with Kinesis (Real-Time Data)5
- Module 12: Orchestration with Step Functions + EventBridge5
- Module 13: Apache Airflow on AWS (MWAA)5
- Module 14: Monitoring, Logging, Reliability5
- Module 15: Capstone Project (Interview-Ready)0