Radical Technologies
Data Engineering
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(2,095 ratings)  50,000+ Student

BIGDATA ON AWS

Big data on AWS (Amazon Web Services) refers to the utilization of AWS services and infrastructure for storing, processing, and analyzing large volumes of data. AWS provides a comprehensive set of tools and services specifically designed to handle big data workloads efficiently and effectively.

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Radical Technologies
50,000+ English 50 hours Weekdays / Weekends Classroom / Online / Corporate
Online / Classroom

BIGDATA ON AWS

IT Training Programme

Duration 50 hours
Batch Type Weekdays / Weekends
Mode of Training Classroom / Online / Corporate
Locations Pune, Bangalore, Kochi
Language English
Certification Globally Recognized
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What you'll learn

Understand core concepts and architecture from the ground up
Get hands-on with the tools used by working professionals
Build real-world projects you can add to your portfolio
Learn industry best practices and coding standards
Practice with real datasets and real-world scenarios
Prepare for certification and technical interviews
Work on collaborative, team-based exercises
Apply performance tuning and optimization techniques
Understand how the technology fits into a larger ecosystem
Complete assignments reviewed by mentors

Programme Overview

11 sections covering the complete curriculum — a single, progressive learning arc.

50 hours
Training Duration
11
Core Modules
52
Total Lessons
4.7
Average Rating
50K+
Students Trained
01

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup
02

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs
03

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies
04

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices
05

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms
06

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a Data Engineering role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

11 sections  •  52 lessons  •  50 hours

01 Module 1 : Introduction to Big Data on AWS
• Overview of big data concepts and challenges
• Introduction to AWS cloud computing services
• Understanding the AWS big data ecosystem
• Architectural considerations for big data on AWS
02 Module 2: Data Storage on AWS
• Overview of AWS storage services (S3, EBS, EFS, Glacier)
• Designing data storage solutions for big data workloads
• Data ingestion and data transfer methods
• Data lifecycle management and versioning
03 Module 3: Data Processing with AWS
• Introduction to AWS compute services (EC2, EMR, Lambda)
• Batch processing with AWS Elastic MapReduce (EMR)
• Real-time processing with AWS Lambda and Kinesis
• Serverless computing for big data workloads
04 Module 4: Data Warehousing and Analytics on AWS
• Introduction to AWS data warehousing services (Redshift, Athena, Glue)
• Designing and optimizing data warehouse architectures
• Querying and analyzing big data with AWS services
• Integration with business intelligence (BI) tools
05 Module 5: Streaming and Real-Time Analytics
• Introduction to AWS streaming services (Kinesis, Kafka)
• Real-time data ingestion and processing pipelines
• Real-time analytics with AWS services (Kinesis Analytics, Amazon Managed Streaming
for Apache Kafka)
• Monitoring and scaling real-time analytics solutions
06 Module 6: Big Data Orchestration and Workflow
• Introduction to AWS orchestration services (Step Functions, Data Pipeline)
• Designing and managing big data workflows on AWS
• Automating data pipelines and ETL processes
• Error handling and fault tolerance in data workflows
07 Module 7: Data Governance and Security
• Understanding data governance challenges in big data
• Data security and compliance considerations on AWS
• Identity and access management (IAM) for big data workloads
• Encryption and data protection mechanisms on AWS
08 Module 8: Data Visualization and Reporting
• Overview of data visualization tools and services
• Integrating AWS big data solutions with visualization tools (QuickSight, Tableau)
• Designing interactive dashboards and reports
• Data storytelling and effective visualization practices
09 Module 9: Big Data Cost Optimization and Performance
• Cost optimization strategies for big data workloads on AWS
• Selecting the right AWS services based on cost and performance requirements
• Monitoring and optimizing resource utilization
• Scalability and performance tuning techniques
10 Module 10: Advanced Topics and Emerging Trends
• Advanced analytics with AWS machine learning services (SageMaker, Comprehend,
Rekognition)
• Big data processing with AWS serverless technologies (Glue, Athena, Lambda)
• Exploring emerging trends in big data and AWS services
• Industry use cases and best practices
11 Big data on AWS (Amazon Web Services)
refers to the utilization of AWS services and infrastructure for storing, processing, and analyzing large volumes of data. AWS provides a comprehensive set of tools and services specifically designed to handle big data workloads efficiently and effectively. Some of the key services offered by AWS for big data include:
Amazon S3 (Simple Storage Service): AWS S3 is a highly scalable object storage service that allows you to store and retrieve large amounts of unstructured data. It is often used as a data lake to store raw data before processing.
Amazon EMR (Elastic MapReduce): EMR is a managed big data processing service that enables you to run distributed frameworks such as Apache Hadoop, Spark, and Presto on AWS. It simplifies the deployment and management of these frameworks and enables processing of large datasets in a scalable manner.
Amazon Redshift: Redshift is a fully managed data warehousing service that provides high-performance analytics for large-scale data sets. It is optimized for online analytical processing (OLAP) workloads and allows you to query and analyze data using SQL.
AWS Glue: Glue is a fully managed extract, transform, and load (ETL) service that helps you prepare and transform your data for analytics. It automatically generates ETL code and provides a serverless environment for data preparation tasks.
AWS Athena: Athena is an interactive query service that allows you to analyze data directly from Amazon S3 using standard SQL queries. It eliminates the need to set up and manage infrastructure and enables ad-hoc querying of large datasets.
Amazon Kinesis: Kinesis is a platform for real-time streaming data processing. It allows you to ingest, process, and analyze streaming data at any scale. Kinesis offers multiple services like Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics for different streaming use cases.
AWS Lambda: Lambda is a serverless computing service that allows you to run code without provisioning or managing servers. It can be used for data processing and integration tasks, such as transforming and enriching data as it flows through various AWS services.
AWS Data Pipeline: Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services and on-premises data sources. It simplifies the creation, scheduling, and management of data workflows.
These are just a few examples of the many AWS services available for handling big data. AWS provides a scalable and flexible platform for storing, processing, and analyzing large datasets, allowing organizations to leverage the power of big data for various purposes, including business intelligence, machine learning, and predictive analytics

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

52+
Hands-On Lessons
11
Core Modules
50 hours
Training Duration
100%
Practical Training

You don't just learn BIGDATA ON AWS. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

Requirement Analysis

Guided Implementation

Mentor Review

Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief
Step-by-step implementation
Mentor feedback and review
Documented outcome
Stack Core Concepts Best Practices
PROJECT // 02

Applied Practice Project

Scenario Design

Independent Build

Testing & Validation

Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation
Testing and validation
Documentation
Peer/mentor review
Stack Applied Skills Testing
PROJECT // 03

Capstone Project

Planning

End-to-End Build

Review & Refinement

Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution
Presentation-ready documentation
Mentor sign-off
Portfolio-ready deliverable
Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

No upcoming batches scheduled right now. Enquire to get notified.

Why Radical Technologies

Live Online Training
  • Highly practical oriented training
  • Installation support on your system
  • 24/7 Email and Phone support
  • 100% Placement Assistance
  • Global Certification Preparation
  • Trainer-Student Interactive Portal
  • Assignments and Projects by Mentors
Live Classroom Training
  • Weekend / Weekdays / Morning / Evening batches
  • 80:20 Practical and Theory ratio
  • Real-life Case Studies
  • Easy make-up for missed sessions
  • PSI | Kryterion | Redhat Test Centers
  • Lifetime Video Classroom Access (coming soon)
  • Resume Prep and Mock Interviews
Self-Paced Training
  • Learn 300+ courses at your own time
  • 50,000+ Satisfied Learners
  • Course Completion Certificate
  • Practical Labs available
  • Mentor Support available
  • Doubt Clearing Session available
  • 10% Discounted Global Certification

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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Certificate of Completion

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.

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Radical Learning Eco-System

Exam Simulator

Cloud SandBox

Hands-on Cloud Lab

Developer Coding Ground

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Frequently Asked Questions

15 questions about the BIGDATA ON AWS course.

01 What is Big Data on AWS, and why is it important?
Big Data on AWS refers to the use of Amazon Web Services’ tools and infrastructure for storing, processing, and analyzing vast amounts of data. It’s important because AWS provides scalable, cost-effective, and secure solutions that empower businesses to gain insights and make data-driven decisions.
02 Which AWS services are essential for Big Data processing?
Key AWS services for big data processing include Amazon EMR for Hadoop and Spark-based processing, Amazon Redshift for data warehousing, Amazon S3 for storage, AWS Glue for ETL processes, Amazon Kinesis for real-time streaming, and Amazon SageMaker for machine learning.
03 What are the benefits of using Amazon S3 for big data storage?
Amazon S3 offers durable, scalable, and secure storage for large datasets. With virtually unlimited storage capacity, S3 integrates seamlessly with other AWS services, supports data lifecycle management, and provides cost-effective options like S3 Glacier for archiving.
04 How does Amazon Kinesis support real-time data analytics?
Amazon Kinesis enables real-time data streaming, allowing businesses to collect, process, and analyze data as it’s generated. This is crucial for applications like clickstream analytics, IoT data processing, and log monitoring, where immediate insights are needed.
05 What is Amazon EMR, and how is it used in Big Data applications?
Amazon EMR (Elastic MapReduce) is a cloud-based big data platform that simplifies running Hadoop, Spark, and other distributed data processing frameworks. It’s used to process and analyze large datasets, supporting tasks like data transformation, ETL, and advanced analytics.
06 How does Amazon Redshift handle large-scale data warehousing?
Amazon Redshift is a fast, scalable data warehousing service that allows users to run complex queries on petabyte-scale data. It uses columnar storage and advanced query optimization techniques, making it ideal for big data analytics and business intelligence.
07 What role does AWS Glue play in Big Data on AWS?
AWS Glue is a fully managed ETL (Extract, Transform, Load) service that simplifies data preparation for analytics and machine learning. It automates data discovery, cleaning, and cataloging, making it easier to transform raw data into valuable insights.
08 Can AWS support machine learning on big data?
Yes, AWS offers Amazon SageMaker, a fully managed machine learning service that enables data scientists to build, train, and deploy machine learning models on large datasets. SageMaker integrates well with other AWS services, enabling scalable and efficient machine learning on big data.
09 What is a data lake, and how does AWS support it?
A data lake is a centralized repository that stores structured and unstructured data at any scale. AWS supports data lakes with services like Amazon S3 for storage, AWS Lake Formation for lake setup and management, and AWS Glue for data cataloging and processing.
10 How can Amazon QuickSight be used for data visualization in Big Data projects?
Amazon QuickSight is a business intelligence service that allows users to create interactive dashboards and visualizations for big data. It connects to multiple data sources, including Amazon Redshift and S3, providing quick insights through customizable dashboards.
11 What are the best practices for securing big data on AWS?
Best practices include encrypting data at rest and in transit, implementing AWS Identity and Access Management (IAM) for access control, using Amazon S3 bucket policies, enabling logging and monitoring with AWS CloudTrail, and regularly auditing for compliance.
12 How does AWS support ETL processes for Big Data?
AWS supports ETL processes with AWS Glue, a managed ETL service that automates data extraction, transformation, and loading. Other tools, such as Amazon EMR and AWS Data Pipeline, can also be used for more complex or customized ETL workflows.
13 What are some common Big Data use cases on AWS?
Common use cases include real-time customer analytics, predictive maintenance, fraud detection, personalized recommendations, genomics research, financial risk assessment, supply chain optimization, and climate modeling. AWS’s big data services support diverse industry applications.
14 Can AWS handle unstructured data, and if so, how?
Yes, AWS can handle unstructured data. Services like Amazon S3 and Amazon DynamoDB are optimized for storing and querying unstructured data. With AWS Glue and Amazon EMR, unstructured data can be processed and transformed into usable insights.
15 How does AWS pricing work for Big Data solutions?
AWS offers a pay-as-you-go pricing model, meaning you pay only for the resources you use. For big data solutions, AWS also offers cost-optimization options, such as Amazon S3 storage classes and Reserved Instances, allowing you to manage costs efficiently based on your data needs.

BIGDATA ON AWS Interview Questions

10 questions commonly asked in BIGDATA ON AWS interviews.

  1. 01 Can you explain the key differences between Amazon Redshift and Amazon EMR, and in what scenarios you would choose one over the other for big data processing?
  2. 02 What are the best practices for securing big data stored in Amazon S3, and how do you implement them to ensure data compliance and protection?
  3. 03 Can you discuss a time when you used Amazon SageMaker to build and deploy a machine learning model on a big data dataset? What challenges did you encounter and how did you overcome them?
  4. 04 How would you design a scalable data pipeline on AWS to handle real-time data ingestion, processing, and storage? Please include the AWS services you would utilize.
  5. 05 Describe your experience with Amazon Kinesis. How have you used it to manage and analyze streaming data in a big data environment?
  6. 06 Describe your approach to monitoring and troubleshooting big data applications on AWS. Which AWS tools and services do you use to ensure system reliability and performance?
  7. 07 How do you implement data lake architecture on AWS using services like Amazon S3, AWS Lake Formation, and AWS Glue? What are the key considerations for managing and querying data within the lake?
  8. 08 What role does AWS Lambda play in a serverless big data architecture, and how can it be integrated with other AWS services to enhance data processing workflows?
  9. 09 Explain how you would optimize query performance in Amazon Redshift for large-scale data analytics. What strategies and AWS features would you employ?
  10. 10 How does AWS Glue facilitate ETL (Extract, Transform, Load) processes in big data projects, and what are its advantages compared to traditional ETL tools?

Big Data on AWS Course Certification with Training in Pune

Welcome to Radical Technologies, the leading institute in Bangalore for Big Data on AWS Training. With a reputation for excellence and an industry-oriented approach, we specialize in delivering comprehensive AWS Big Data Training designed to equip professionals with the skills and knowledge needed to excel in today’s data-driven world. Our courses cover the entire spectrum of Big Data on AWS concepts, ensuring you gain hands-on experience with real-world applications.

At Radical Technologies, we offer a range of courses tailored to meet the diverse needs of our students and corporate clients. Our flagship programs include Big Data on AWS Course in Bangalore, AWS Big Data Certification Training, and Amazon Big Data Certification courses. We also provide flexible learning formats, including Big Data on AWS Online Classes in Bangalore and Big Data AWS Certification Online options, allowing students to learn at their own pace and convenience.

Our training modules are led by industry experts with years of experience in AWS with Big Data solutions, offering practical insights and hands-on guidance. Whether you’re looking for Big Data on AWS Corporate Training in Bangalore for your organization or aiming for an AWS Certified Big Data Certification to advance your career, Radical Technologies provides the expert guidance and comprehensive resources you need.

Join us at Radical Technologies to gain in-depth expertise through our Big Data on AWS Classes in Bangalore, backed by a curriculum aligned with the latest industry standards. With our dedication to quality education, Radical Technologies is the ideal destination for those pursuing Big Data AWS Certification and career growth in the field of Big Data on AWS.

For more details about our Big Data on AWS Training in Bangalore or to enroll in our Big Data AWS Course, contact us today and start your journey toward becoming an AWS-certified expert in Big Data!

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