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AWS Data Engineering - Learnees

AWS Data Engineering

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AWS Data Engineering focuses on building scalable, secure, and efficient data pipelines using Amazon Web Services. The course teaches how to ingest, store, process, and analyze large datasets with tools like AWS Glue, Redshift, Lambda, and S3. Learners gain hands-on experience designing ETL workflows and implementing end-to-end data solutions in the cloud. By the end, they are prepared to support data-driven decision-making using modern AWS infrastructure.

Trainer :

LiveOnline ClassCourse started on:

Upto First 3 session - 100% Refund

1. Introduction to AWS & Data Engineering

Topics:

Explore the AWS global infrastructure
Discover AWS services tailored for Data Engineering
Understand IAM roles policies and permissions
Set up AWS free tier and take an architecture walkthrough

Hands-on:

Skills You Will Learn:

2. Data Storage in AWS

Topics:

Dive into Amazon S3: buckets/ policies and storage classes
Set up a data lake in S3
Learn about versioning/ lifecycle and encryption
Access S3 using Boto3/ AWS CLI and AWS SDK

Hands-on:

Skills You Will Learn:

3. Data Ingestion Techniques

Topics:

Batch ingestion using AWS Glue & S3
Real-time ingestion with Kinesis Data Streams
Utilize Kinesis Firehose to S3 and Redshift
Migrate data with AWS DMS

Hands-on:

Skills You Will Learn:

4. Data Processing with AWS Glue

Topics:

Create Glue ETL jobs with PySpark
Manage job bookmarks/ triggers and workflows
Use Glue Data Catalog and Crawlers
Handle schema evolution and partitioning

Hands-on:

Skills You Will Learn:

5. Streaming with AWS Kinesis

Topics:

Discover Kinesis Data Streams and Firehose architecture
Read data with Kinesis Client Library (KCL)
Understand partition keys and shards
Perform real-time transformation with Lambda

Hands-on:

Skills You Will Learn:

6. Data Transformation with PySpark

Topics:

Learn PySpark basics and GlueContext
Transform data in AWS Glue
Work with dynamic frames
Optimize performance with Glue workers

Hands-on:

Skills You Will Learn:

7. Data Warehousing with Amazon Redshift

Topics:

Explore Redshift clusters and node types
Load data from S3 using the COPY command
Understand distribution styles and sort keys
Utilize Redshift Spectrum and federated queries

Hands-on:

Skills You Will Learn:

8. Data Querying with Athena

Topics:

Set up Athena and integrate with S3
Query both structured and unstructured data
Use partition projection and CTAS
Apply cost optimization techniques

Hands-on:

Skills You Will Learn:

9. Orchestration using Step Functions & EventBridge

Topics:

Build workflows with Step Functions
Create event-driven pipelines using EventBridge
Integrate Lambda and handle errors
Monitor state transitions effectively

Hands-on:

Skills You Will Learn:

10. Security and Compliance

Topics:

Set up IAM roles and policies for data pipelines
Implement data encryption with KMS
Use audit logs with CloudTrail and CloudWatch
Gain an overview of compliance and governance

Hands-on:

Skills You Will Learn:

11. Monitoring and Logging

Topics:

Monitor with CloudWatch metrics and logs
Log activities in Glue and Redshift
Tune Athena query performance
Create Kinesis monitoring dashboards

Hands-on:

Skills You Will Learn:

12. Project & Certifi cation Preparation

Topics:

Develop an end-to-end project using the AWS stack
Combine data pipelines with S3/ Glue Kinesis and Redshift
Prepare for common interview questions and answers
Get tips for AWS certification preparation

Hands-on:

Skills You Will Learn:

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Class Type: On-Demand

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Dur: 10 weeks

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Upto First 3 session - 100% Refund

Why Choose AWS Data Engineering?

Why enroll for AWS Data Engineering Certification?

Unlock your potential in cloud computing with AWS Data Engineering certification

Most Popular Certification

AWS Data Engineering certification is rated as one of the most popular and lucrative cloud certifications in IT globally.

Growing Market

The global AWS Data Engineering market size is expected to grow at a CAGR of 21.2% from 2024 to 2030.

High Salary Potential

The estimated total pay for an AWS Data Engineering is ₹7,09,000 per year, with an average salary of ₹6,09,000 per year - Glassdoor.

Python

SQL

AWS S3

AWS Lambda

AWS Glue

Amazon Redshift

ETL development

Curriculum Designed by Experts

1. Introduction to AWS & Data Engineering

Topics:

Explore the AWS global infrastructure
Discover AWS services tailored for Data Engineering
Understand IAM roles policies and permissions
Set up AWS free tier and take an architecture walkthrough

2. Data Storage in AWS

3. Data Ingestion Techniques

4. Data Processing with AWS Glue

5. Streaming with AWS Kinesis

6. Data Transformation with PySpark

7. Data Warehousing with Amazon Redshift

8. Data Querying with Athena

9. Orchestration using Step Functions & EventBridge

10. Security and Compliance

11. Monitoring and Logging

12. Project & Certifi cation Preparation

Free Career Counselling

We are happy to help you 24/7

1. Introduction to AWS & Data Engineering

Topics:

Explore the AWS global infrastructure
Discover AWS services tailored for Data Engineering
Understand IAM roles policies and permissions
Set up AWS free tier and take an architecture walkthrough

Hands-on:

Skills You Will Learn:

2. Data Storage in AWS

Topics:

Dive into Amazon S3: buckets/ policies and storage classes
Set up a data lake in S3
Learn about versioning/ lifecycle and encryption
Access S3 using Boto3/ AWS CLI and AWS SDK

Hands-on:

Skills You Will Learn:

3. Data Ingestion Techniques

Topics:

Batch ingestion using AWS Glue & S3
Real-time ingestion with Kinesis Data Streams
Utilize Kinesis Firehose to S3 and Redshift
Migrate data with AWS DMS

Hands-on:

Skills You Will Learn:

4. Data Processing with AWS Glue

Topics:

Create Glue ETL jobs with PySpark
Manage job bookmarks/ triggers and workflows
Use Glue Data Catalog and Crawlers
Handle schema evolution and partitioning

Hands-on:

Skills You Will Learn:

5. Streaming with AWS Kinesis

Topics:

Discover Kinesis Data Streams and Firehose architecture
Read data with Kinesis Client Library (KCL)
Understand partition keys and shards
Perform real-time transformation with Lambda

Hands-on:

Skills You Will Learn:

6. Data Transformation with PySpark

Topics:

Learn PySpark basics and GlueContext
Transform data in AWS Glue
Work with dynamic frames
Optimize performance with Glue workers

Hands-on:

Skills You Will Learn:

7. Data Warehousing with Amazon Redshift

Topics:

Explore Redshift clusters and node types
Load data from S3 using the COPY command
Understand distribution styles and sort keys
Utilize Redshift Spectrum and federated queries

Hands-on:

Skills You Will Learn:

8. Data Querying with Athena

Topics:

Set up Athena and integrate with S3
Query both structured and unstructured data
Use partition projection and CTAS
Apply cost optimization techniques

Hands-on:

Skills You Will Learn:

9. Orchestration using Step Functions & EventBridge

Topics:

Build workflows with Step Functions
Create event-driven pipelines using EventBridge
Integrate Lambda and handle errors
Monitor state transitions effectively

Hands-on:

Skills You Will Learn:

10. Security and Compliance

Topics:

Set up IAM roles and policies for data pipelines
Implement data encryption with KMS
Use audit logs with CloudTrail and CloudWatch
Gain an overview of compliance and governance

Hands-on:

Skills You Will Learn:

11. Monitoring and Logging

Topics:

Monitor with CloudWatch metrics and logs
Log activities in Glue and Redshift
Tune Athena query performance
Create Kinesis monitoring dashboards

Hands-on:

Skills You Will Learn:

12. Project & Certifi cation Preparation

Topics:

Develop an end-to-end project using the AWS stack
Combine data pipelines with S3/ Glue Kinesis and Redshift
Prepare for common interview questions and answers
Get tips for AWS certification preparation

Hands-on:

Skills You Will Learn:

Career Opportunities

Job roles are being prepared for this course. Please check back later.

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Graded Performance Certificate
Certificate of Completion

Course Overview

Everything you need to know about AWS Data Engineering

The AWS Data Engineering course aims to equip learners with the skills to design, build, and maintain scalable data pipelines using core AWS services. It focuses on ingesting, storing, processing, and transforming large volumes of data for analytics and machine learning. Participants will learn to implement secure, automated, and cost-optimized data architectures. By the end of the course, they will be able to leverage AWS tools to deliver reliable, high-performance data solutions for real-world business needs.