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Data Engineering

AWS Data Engineering

Build scalable data solutions on AWS. Learn Redshift, Glue, and real-time pipelines to become an industry-ready AWS Data Engineer.

10 Weeks

Duration

AWS Data Engineering Course

About the program

Welcome to the Captivating Realm of Data Engineering with AWS

Are you ready to unlock the power of data and shape the future? In our data-driven world, the ability to derive valuable insights from vast amounts of information is an increasingly sought-after skill. This comprehensive AWS course is designed to equip you with the knowledge and expertise needed to thrive in the ever-evolving field of data engineering.

Why Opt for Data Engineering with AWS?

Data engineering is not just a buzzword, it's a revolutionary field that is transforming industries across the globe. From healthcare and finance to marketing and entertainment, data engineers utilize their skills to solve complex problems, make data-driven decisions, and promote innovation. With an AWS certification, you'll be at the forefront of this evolution, harnessing data to influence the future.

Abundant Career Opportunities:

  • Data Engineer: Design, construct, and sustain data pipelines and infrastructure on AWS.
  • Data Analyst: Collect, cleanse, and analyze data to reveal trends and insights.

This course is your pathway to a fulfilling and impactful career in data engineering with AWS. Let's embark on this thrilling journey together!

Course Curriculum

1

Module 1: Introduction to AWS & Data Engineering

KEY 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

Module 2: Data Storage in AWS

KEY 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
3

Module 3: Data Ingestion Techniques

KEY 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
4

Module 4: Data Processing with AWS Glue

KEY TOPICS :

  • Create Glue ETL jobs with PySpark
  • Manage job bookmarks, triggers, and workflows
  • Use Glue Data Catalog and Crawlers
  • Handle schema evolution and partitioning
5

Module 5: Streaming with AWS Kinesis

KEY 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
6

Module 6: Data Transformation with PySpark

KEY TOPICS :

  • Learn PySpark basics and GlueContext
  • Transform data in AWS Glue
  • Work with dynamic frames
  • Optimize performance with Glue workers
7

Module 7: Data Warehousing with Amazon Redshift

KEY 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
8

Module 8: Data Querying with Athena

KEY TOPICS :

  • Set up Athena and integrate with S3
  • Query both structured and unstructured data
  • Use partition projection and CTAS
  • Apply cost optimization techniques
9

Module 9: Orchestration using Step Functions & EventBridge

KEY TOPICS :

  • Build workflows with Step Functions
  • Create event-driven pipelines using EventBridge
  • Integrate Lambda and handle errors
  • Monitor state transitions effectively
10

Module 10: Security and Compliance

KEY 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
11

Module 11: Monitoring and Logging

KEY TOPICS :

  • Monitor with CloudWatch metrics and logs
  • Log activities in Glue and Redshift
  • Tune Athena query performance
  • Create Kinesis monitoring dashboards
12

Module 12: Project & Certifi cation Preparation

KEY 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

This curriculum offers a structured learning path for mastering Data Engineering on AWS, starting from foundational concepts and progressing to advanced hands-on application. Learners will gain practical experience with essential AWS services such as S3, Glue, Lambda, Redshift, Athena, and EMR. By the end of the program, they will be equipped to design, build, and optimize scalable, cloud-based data pipelines and analytics solutions for real-world use cases.

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