AWS data engineering Course in PCMC Pune
Learning Format
Online Mode
Total Training Duration
180 Hrs
Duration
4 Month
Certification
Yes
AWS data engineering Course in PCMC Pune
Boost your career in cloud data management with the AWS Data Engineering Course in PCMC Pune at IntelliBI Innovations Technologies. With businesses increasingly moving to the cloud, skilled data engineers are in high demand. This course is designed to equip students and professionals with the practical knowledge needed to design, build, and manage AWS-based data solutions.
Throughout the program, you will gain hands-on experience with essential AWS services, including S3 for storage, Glue for ETL processes, Redshift for data warehousing, Lambda for serverless computing, and EMR for big data processing. Each module is structured to ensure you not only understand the theory but also apply your skills to real-world projects, preparing you to handle actual cloud data challenges confidently.
Whether you are a working professional aiming to upskill or a student looking to enter the data engineering field, this course provides the tools and experience necessary for success. In addition to AWS, IntelliBI also offers insights into other cloud platforms with the GCP Data Engineering Course in PCMC Pune, giving you a competitive edge across multiple cloud environments.
Our instructors are industry experts who bring practical insights to every session, ensuring your learning is aligned with current market demands. By the end of the course, you will have a portfolio of projects demonstrating your ability to manage cloud data pipelines efficiently. Choosing IntelliBI means choosing hands-on learning that bridges the gap between theoretical knowledge and employable skills, making you a sought-after professional in cloud data engineering.
Course Summary
Eligibility
Tech & Non-Tech Working professional, Freshers, Graduate from any domain.
Real time projects
Gain hands-on experience through projects that simulate real-world challenges, preparing you for a career in analytics.
Dedicated Profile Building
Along with learning, we help you with ATS resume building and LinkedIn profile optimization
Hands-on Industry Relevant Casestudies
Complete guidance for 25+ Assignments and case studies to help you gain the hands-on experience.
Live Doubt Solving
Get your queries solved with daily dedicated doubts solving sessions.
800+ Companies
Connect with top employers
Certification
10+ ISO Globally recognized certified
LMS Access for 2 Years
Get course recording access for 2 years after course completion.
Aspirational peer group
10K+ students & alumni across diverse domains. Dedicated Telegram virtual classroom for peer-to-peer support.
Instructor
Experts and trainer for top-tech companies.
Direct CRM Calls
Get shortlisted instantly
GitHub Enhancement
Showcase coding skills
Al Mock
Interviews Practice and improve with Al feedback
Placement Assistance Until Placed
Along with up skilling and preparing you for the job, we also help you for your search & your interview.
Mode of Learning
100% Live Learning Learn directly from experienced instructors in Live sessions, with an emphasis on practical, hands-on learning.
Program Name
LinkedIn Optimization
Program Duration
Eligibility
Real time projects
Dedicated Profile Building
Live Doubt Solving
800+ Companies
Certification
LMS Access for 2 Years
Aspirational peer group
Hands-on Industry Relevant Casestudies
Instructor
Direct CRM Calls
GitHub Enhancement
Al Mock Interviews
Mode of Learning
Placement Assistance Until Placed
Syllabus Summary
A Comprehensive Curriculum Designed for Real-World Success
- Type of Statements, Constraints, Clauses
- Functions, Joins, Subqueries, Set Operators
- Analytical Functions, Pivot/Unpivot, Partitions
- Indexes, Views, Sequences, Synonyms
- Performance Tuning
- Stored Procedure & Stored Function
- Cursors, Exception Handling
- Introduction, Data Types (Tuple, List, Dict, Set)
- Control Statements, User-Defined Functions
- String Operations, File & Error Handling
- NumPy, Pandas
- Visualization: Matplotlib, Seaborn
- Automation & Real-World Cases
- Spark Overview
- Spark Architecture
- Transformations, Actions & Lazy Evaluation
- Series & DataFrame Basics
- Spark SQL
- DataFrame Operations
Filtering & Sorting
Built-in Functions & Case Statements
Aggregations & Grouping
Joins & Window Functions
Set Operators
GlobalTemp Views
Analytic Functions
Reading File Formats
Parquet, CSV, JSON
Nested DataFrames - SCD Type 1 (Slowly Changing Dimension)
- SCD Type 2
- Data Factory, Databricks, Synapse
- Storage, Data Lake, Blob Storage, Lake Storage
- Cosmos DB, Logic Apps, API Management
- Event Hub, loT Hub
- Data visualization using Power BI
- Data Modelling
- Power Query
- Advanced visualizations
- DAX
- Build data analytics project using Power BI
- Basics & Navigation
- Formulas & Functions
- Data Management
- Charts & Visualization
- Advanced Analysis
- Profile Management with GitHub
Syllabus Summary
A Comprehensive Curriculum Designed for Real-World Success
- Type of Statements, Constraints, Clauses
- Functions, Joins, Subqueries, Set Operators
- Analytical Functions, Pivot/Unpivot, Partitions
- Indexes, Views, Sequences, Synonyms
- Performance Tuning
- Stored Procedure & Stored Function
- Cursors, Exception Handling
- Introduction, Data Types (Tuple, List, Dict, Set)
- Control Statements, User-Defined Functions
- String Operations, File & Error Handling
- NumPy, Pandas
- Visualization: Matplotlib, Seaborn
- Automation & Real-World Cases
- Spark Overview
- Spark Architecture
- Transformations, Actions & Lazy Evaluation
- Series & DataFrame Basics
- Spark SQL
- DataFrame Operations
Filtering & Sorting
Built-in Functions & Case Statements
Aggregations & Grouping
Joins & Window Functions
Set Operators
GlobalTemp Views
Analytic Functions
Reading File Formats
Parquet, CSV, JSON
Nested DataFrames - SCD Type 1 (Slowly Changing Dimension)
- SCD Type 2
- Data Factory, Databricks, Synapse
- Storage, Data Lake, Blob Storage, Lake Storage
- Cosmos DB, Logic Apps, API Management
- Event Hub, loT Hub
- Data visualization using Power BI
- Data Modelling
- Power Query
- Advanced visualizations
- DAX
- Build data analytics project using Power BI
- Basics & Navigation
- Formulas & Functions
- Data Management
- Charts & Visualization
- Advanced Analysis
- Profile Management with GitHub
Top Hiring Industries in India for Data Analytics
Career Support Services
Resume Building
Linkedin Optimaztion
Access To Job Board
Interview Calls
GitHub Portfolio
IntelliBI Azure Data Engineering Certification
Course Highlights
- Course Duration
- 250+ Hours /
- 4 Months
-Pre-Class-
-Pre Career Counseling,
-Technical Evaluation
-Sessions- Technical,
-Softskill, Mentor,
-E-Consultation.
-Technical
-Assignments
-300+ Case Studies
-Industry Real
-World Project 4
-Mock Interviews 5
-Student Internship
-Dashboard
-Resume Preparation,
-Interview Guidance
-Job Readiness,
-Placement support
Our Learners Got Assured Placement. So Can You!
Students reviews
Real stories. Real success.
Real students.
1. Why Learning AWS Data Engineering is a Long-Term Investment:
AWS Data Engineering is one of the easier technologies to learn compared to others, as it involves less coding and often relies on drag-and-drop tools. If you have a solid understanding of SQL, you can quickly relate to many of the tasks involved, making it even easier to adopt. In fact, for a lot of data-related tasks, you don’t need to write complex code but instead can use simple configurations and drag-and-drop interfaces. This makes AWS Data Engineering an accessible skill for both beginners and experienced professionals.
AWS Data Engineering is in high demand. Alongside Azure and Google Cloud Platform (GCP), AWS is one of the leading players in the cloud data engineering space. These three giants dominate the market, offering vast opportunities for data engineers to build careers in cloud-based environments.
The growth of cloud platforms like AWS has been rapid, especially after the COVID-19 pandemic, when many businesses shifted to online models. With this shift, the volume of data generated has increased dramatically. On-premise systems like Informatica and a can no longer handle the volume, speed, and complexity of big data effectively. Managing and maintaining data on-premise has become challenging for many companies, which has driven them to move their data infrastructure to the cloud.
As businesses continue to migrate from on-prem systems to cloud solutions, the demand for professionals skilled in cloud technologies such as AWS, Azure, and GCP is expected to grow. This trend is set to continue as companies aim for more scalable, secure, and cost-effective data management solutions.
2. Pre-requisites:
- SQL & PL/SQL (Stored Functions, Stored Procedures, Indexes)
- Python
- Spark (PySpark, Spark SQL)
3. AWS Services: A Must for Data Engineers:
- Amazon Kinesis Data Streams
- AWS Transfer for SFTP
- Amazon RDS
- Amazon S3
- NoSQL Databases (DynamoDB)
- AWS Lambda
- AWS Glue
- Amazon EMR
- Amazon Athena
- Amazon Redshift
- AWS CLI & CloudShell
- AWS Lake Formation
- AWS Database Migration Service (DMS)
- CI/CD Pipelines (AWS CodePipeline, CodeBuild, CodeDeploy)
- AWS IAM (Identity and Access Management)
- Data Encryption
- Apache Airflow
- AWS SageMaker (Machine Learning Integration)
- AWS CloudFormation
- Terraform (Infrastructure as Code)
- Performance Optimization
Project 1: End-to-End Complete Data Ingestion Pipeline with Amazon Redshift and RDS
Project 2: End-to-End Full Data Ingestion Pipeline with Parquet Files and Athena Views
4. Comparing Coding Complexity: AWS Data Engineers vs Java Developers
AWS Data Engineers: Coding complexity is around 20% compared to Java Developers, where the complexity is considered 100%.
5. Career Opportunities
- For Experienced Professionals:
- AWS Data Engineer
- Cloud Data Engineer
- Big Data Engineer
- Data Analyst
- Business Intelligence Developer
- Cloud Solutions Architect
- For Freshers:
- AWS Data Engineer
- Cloud Data Engineer
- Data Analyst
- SQL Developer
- Python Developer
- Big Data Engineer
6. Who Can Learn AWS Data Engineering Skills:
IT Professionals: If you are working on prem ETL tools. The demand for cloud skills, especially in AWS Data Engineering, is skyrocketing, and companies are offering competitive pay for skilled professionals. Whether you are looking for easier-to-learn technologies or a chance to boost your career in a growing field, AWS Data Engineering is a perfect choice. With cloud computing becoming essential for businesses everywhere, this skillset is in high demand, making it a smart move for anyone looking to future-proof their career.
Non-IT Professionals: If you have a background in finance, or any other non-technical field, and you’re interested in moving into the world of cloud computing and data engineering, this course is for you. AWS Data Engineering is easy to learn and doesn’t require a technical background. The tools used are simple and user-friendly, so you don’t need to worry about complex coding. This makes it a great option for people looking to switch careers and enter a high-demand, well-paying field with strong growth opportunities. Whether you want to learn new skills or shift your career, this course can help you take the first step into the tech world.
7. Annual CTC for AWS Data Engineers (Based on Experience):
- 0-1 Year Experience (Entry-Level):
- India: ₹4–5 LPA
- US: $60,000–$80,000
- Job Role: Support data tasks, learn cloud tech, assist with data pipelines and cloud infrastructure setup.
- 1-4 Years Experience (Junior/Mid-Level):
- India: ₹6–10 LPA
- US: $80,000–$110,000
- Job Role: Build and manage data pipelines, handle data integration, optimize cloud environments, and automate processes.
- 4-8 Years Experience (Mid-Level/Senior):
- India: ₹10–20 LPA
- US: $110,000–$140,000
- Job Role: Lead data architecture, develop ETL pipelines, mentor junior engineers, and handle large-scale data projects.
- 8+ Years Experience (Senior/Lead):
- India: ₹20–30+ LPA
- US: $140,000–$160,000+
- Job Role: Oversee cloud data solutions, manage teams, lead strategic data projects, ensure system scalability, and optimize data flow.
Pre-requisites:
- SQL & PL/SQL (Stored Functions, Stored Procedures, Indexes)
- Python
- Spark (PySpark, Spark SQL)
AWS Services: A Must for Data Engineers:
- Amazon Kinesis Data Streams
- AWS Transfer for SFTP
- Amazon RDS
- Amazon S3
- NoSQL Databases (DynamoDB)
- AWS Lambda
- AWS Glue
- Amazon EMR
- Amazon Athena
- Amazon Redshift
- AWS CLI & CloudShell
- AWS Lake Formation
- AWS Database Migration Service (DMS)
- CI/CD Pipelines (AWS CodePipeline, CodeBuild, CodeDeploy)
- AWS IAM (Identity and Access Management)
- Data Encryption
- Apache Airflow
- AWS SageMaker (Machine Learning Integration)
- AWS CloudFormation
- Terraform (Infrastructure as Code)
- Performance Optimization
Project 1: End-to-End Complete Data Ingestion Pipeline with Amazon Redshift and RDS
Project 2: End-to-End Full Data Ingestion Pipeline with Parquet Files and Athena Views