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

1. For Experienced Professionals:
   • AWS Data Engineer
   • Cloud Data Engineer
   • Big Data Engineer
   • Data Analyst
   • Business Intelligence Developer
   • Cloud Solutions Architect
2. 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):

1. 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.
2. 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.
3. 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.
4. 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

Training Duration: 180 Hrs

Duration: 4 Months

Mode of AWS Training: Online

Trainer: Experienced AWS Consultant

• For the past eight years, IntelliBI has been a leader in training for AI and Business Intelligence (BI). Our expertise is in AWS, Azure, GCP, Data Science, and Artificial Intelligence.
• We Are Well-Known: We teach step by step, starting with fundamental concepts and progressing to advanced skills, with a complete focus on practical-based training.
• We have an excellent placement record. On average, 85% to 90% of our students find jobs after completing their training
• Our Key to Success: No Marketing Needed for Our Courses: We don’t spend money on advertising, instead, we grow through referrals from satisfied customers. Our students’ success stories prove the effectiveness of our training.
• We have received many appreciations from many MNC companies for our exceptional corporate training quality and impactful results.
• We have trained and successfully placed over 1,200+ professionals.
• We are partnered with leading MNCs to upskill their workforce.