AWS Data Engineering Course in Hyderabad
Build data lakes, pipelines and warehouses on AWS with Glue, Kinesis and Redshift
The AWS Data Engineering course teaches you to design, build and operate data platforms on Amazon Web Services. It follows the four domains of the AWS Certified Data Engineer – Associate (DEA-C01) exam guide: data ingestion and transformation, data store management, data operations and support, and data security and governance. You start with SQL, Python and AWS fundamentals, then build data lakes on Amazon S3, ingest batch and streaming data with AWS DMS, Amazon Kinesis and Amazon MSK, transform it with AWS Glue, Amazon EMR and Apache Spark, query it with Amazon Athena, and serve it from Amazon Redshift. You orchestrate pipelines with AWS Step Functions and Amazon MWAA, govern data with Lake Formation and the Glue Data Catalog, secure it with IAM and KMS, and monitor, test and optimise it for cost. The course suits graduates and professionals with basic SQL; Python is taught from the basics. You finish with a capstone lakehouse pipeline. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Data Engineering & AWS Foundations
- •Data engineer role and modern data architectures
- •AWS accounts, IAM roles, Regions and the AWS CLI
- •Cost controls: budgets, tagging and clean-up
- •Lab: Set up a secure account and a project budget
- •Outcome: A safe, low-cost AWS workspace
SQL & Python for Data Engineers
- •SQL joins, aggregations, CTEs and window functions
- •Python and pandas for transformation; the boto3 SDK
- •File formats: CSV, JSON, Avro, Parquet and ORC
- •Lab: Transform a dataset and write it to S3 with Python
- •Outcome: Write the SQL and Python used in pipelines
Data Lakes on Amazon S3
- •S3 features: versioning, lifecycle, replication and storage classes
- •Raw, curated and consumption zones; partitioning and compaction
- •Encryption and access control for lakes
- •Lab: Build a lake layout and load sample data
- •Outcome: Organise and secure a lake on S3
Batch Ingestion: DMS, Glue & AppFlow
- •AWS Database Migration Service: full load and change data capture
- •Glue connectors, AppFlow and Transfer Family overview
- •Choosing ingestion tools by source and latency
- •Lab: Replicate a database into S3 with DMS
- •Outcome: Ingest operational data into the lake
AWS Glue: ETL, Catalog & Crawlers
- •Glue Data Catalog and crawlers
- •Glue ETL jobs with PySpark, job bookmarks and worker types
- •Glue Studio visual ETL, Glue DataBrew and Glue Data Quality
- •Lab: Build a Glue job that cleans and partitions data
- •Outcome: Transform data at scale with Glue
Streaming with Kinesis & MSK
- •Kinesis Data Streams, shards and consumers
- •Amazon Data Firehose delivery to S3 and Redshift
- •Amazon MSK and Amazon Managed Service for Apache Flink overview
- •Lab: Stream events into S3 with Firehose and query them
- •Outcome: Build near real-time ingestion pipelines
Querying & Lakehouse: Athena, EMR & Iceberg
- •Amazon Athena queries, cost control and partition projection
- •Amazon EMR and EMR Serverless for Spark
- •Open table formats: Apache Iceberg on S3
- •Lab: Convert a dataset to Iceberg and query it with Athena
- •Outcome: Query and manage lakehouse tables
Data Warehousing with Amazon Redshift
- •Redshift architecture, node types and Serverless
- •Distribution styles, sort keys and compression
- •COPY and UNLOAD; Redshift Spectrum and data sharing
- •Lab: Load data into Redshift and tune a slow query
- •Outcome: Serve analytics from a warehouse
Operational & NoSQL Stores
- •Choosing between RDS, Aurora, DynamoDB and OpenSearch Service
- •DynamoDB keys, indexes and streams for data pipelines
- •Data modelling for access patterns
- •Lab: Capture DynamoDB changes into the lake
- •Outcome: Match stores to workload needs
Orchestration: Step Functions, MWAA & EventBridge
- •AWS Step Functions workflows and error handling
- •Amazon Managed Workflows for Apache Airflow
- •EventBridge schedules and triggers; Lambda in pipelines
- •Lab: Orchestrate an ingest, Glue transform and Redshift load
- •Outcome: Run pipelines on dependable schedules
Security, Governance & Quality
- •Lake Formation permissions, LF-tags and cross-account sharing
- •Encryption with KMS, VPC endpoints and secrets management
- •Amazon Macie for sensitive data, audit with CloudTrail
- •Data quality rules with Glue Data Quality
- •Lab: Restrict columns by role with Lake Formation
- •Outcome: Build governed, auditable data platforms
Monitoring, Cost Optimisation & CI/CD
- •CloudWatch metrics, logs and alarms for pipelines
- •Infrastructure as code with CloudFormation or Terraform; CI/CD with CodePipeline or GitHub Actions
- •Cost optimisation: file sizes, partitions, Spot and right-sizing
- •Lab: Add alarms and a deployment pipeline to the capstone
- •Outcome: Operate and improve pipelines continuously
Capstone & Exam Readiness
- •Capstone: ingest, transform, govern, orchestrate and serve a lakehouse on AWS
- •Review of the four DEA-C01 domains with scenario questions
- •Portfolio write-up and interview practice
- •Outcome: A portfolio project and a study plan for the vendor exam
Tools Covered
Projects Included
- S3 Data Lake with Glue Catalog: Land raw files in zones, crawl them and expose curated Parquet tables to Athena; deliverable is a documented lake and query set.
- Database Replication Pipeline: Use DMS with change data capture to load operational data into the lake; deliverable is a replication task and a reconciliation report.
- Streaming Click Events: Ingest events with Kinesis and Firehose and aggregate near real time; deliverable is a working stream and dashboard query.
- Redshift Analytics Mart: Load a star schema, tune distribution and sort keys and benchmark queries; deliverable is a mart and timing report.
- Capstone Lakehouse Platform: Orchestrate ingestion, Glue transforms, Lake Formation security and Redshift serving with alarms; deliverable is a GitHub repository and architecture diagram.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the AWS Data Engineering course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on AWS Data Engineering.
Certificate of Completion
AWS Data Engineering
This certifies that
[Your Name]
has successfully completed the
AWS Data Engineering
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured AWS Data Engineering course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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How is this different from the general Data Engineering course?
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Best AWS Data Engineering Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include AWS Data Engineer, Data Engineer, ETL Developer, Big Data Engineer; support is guidance with the job search, not a guarantee of a job.