Data-Engineering-on-AWS

Data Engineering Masters Program with AWS

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Last Update May 28, 2023
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About This Course

Are you interested in a career as an AWS Data Engineer?

The “Data Engineering Masters Program with AWS” is designed to equip you with the skills necessary to become an expert in data engineering using AWS. This comprehensive program covers the design, development, deployment, and management of data-intensive pipelines and applications using a range of AWS services such as S3, Redshift, DynamoDB, Glue, PySpark, Lambda, and more.

In addition to learning how to build efficient and scalable data engineering pipelines, you will also learn to store and manage large volumes of data and perform data transformations and analytics using AWS services. The course covers the AWS data ecosystem, data warehousing, querying techniques, and real-time data streams.

With real-world projects and personalized feedback from experienced data engineering professionals, you will gain hands-on experience and be able to apply your knowledge and skills to real-world scenarios. This program is suitable for both beginners and experienced developers looking to build a career as an AWS Data Engineer.

Job market overview
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AWS Data Engineers are in high demand in the job market due to the increasing need for data-driven decision making. According to Glassdoor, the national average salary for a Data Engineer is $96,774 in the United States, and the demand for AWS Data Engineers is expected to grow exponentially in the coming years. This course will provide you with the necessary skills to excel in this field and stay ahead of the competition.

What you will learn

By the end of this course, you will have the skills and knowledge necessary to design and implement scalable data engineering pipelines on AWS using a range of services and tools.

Course Format
  • Live classes
  • Hands-on trainings
  • Mini-projects for every module
  • Recorded sessions (available for revising)
  • Collaborative projects with team mates in real-world projects
  • Demonstrations
  • Interactive activities: including labs, quizzes, scenario walk-throughs
What this course includes
  • 200+hrs of live classes
  • Collaborative projects
  • Slide decks, Code snippets
  • Resume preparation from the 2nd week of course commencement
  • 1:1 career/interview preparation
  • Soft skill training
  • On-call project support for up to 3 months
  • Certificate of completion
  • Unlimited free access to our exam engines

Our students work at

Course Outline
Fil The Form

    Prerequisites
    • CS/IT degree or prior IT experience is highly desired
    • Basic programming and cloud computing concepts
    • Database fundamentals

    Why should you take the course with us

    • Project-Ready, Not Just Job-Ready!

    By the time you complete our program, you will be ready to hit the ground running and execute projects with confidence.

    • Authentic Data For Genuine Hands-On Experience

    Our curated datasets sourced from various industries, enable you to develop skills in realistic contexts, tackling challenges in your professional journey.

    • Personalized Career Preparation

    We prepare your entire career, not just your resume. Our personalized guidance helps you excel in interviews and acquire essential expertise for your desired role.

    • Multiple Experts For Each Course

    Multiple experts teach various modules to provide you diverse understanding of the subject matter, and to benefit you with the insights and industrial experiences.

    • On-Call Project Assistance After Landing Your Dream Job

    Get up to 3 months of on-call project assistance from our experts to help you excel in your new role and tackle challenges with confidence.

    • A Vibrant and Active Community

    Get connected with a thriving community of professionals who connect and collaborate through channels like Discord. We regularly host online meet-ups and activities to foster knowledge sharing and networking opportunities.

    FAQs

    What is AWS Data Engineering?

    AWS Data Engineering involves designing, building, and maintaining the data architecture required to support data-driven applications and business decisions. This includes collecting, storing, processing, and analyzing large volumes of data using AWS services and tools.

    What are the prerequisites for this program?

    The prerequisites for this program include basic understanding of programming languages such as Python, SQL, and Java, and a working knowledge of Cloud Computing, Databases and AWS services. Candidate with CS/IT background is highly desirable.

    What will I learn in this masters program?

    This program will cover the following topics:

    • Designing and implementing data pipelines using AWS services such as Glue, Kinesis.
    • Building data warehouses and data lakes using Redshift and S3.
    • Performing data transformation and processing using Lambda and EMR.
    • Implementing security and compliance best practices and much more.

    Kindly read brochure to see the list of topics.

    What is the duration of the AWS Data Engineering Masters Training Program?

    The duration of the live training session is approximately 200+ hours, which includes hands-on labs, interactive quizzes, assignments etc. On top of this, You will be having real-world projects, the completion depends on your pace of work and on your collaboration with other team members to get projects done.

    Is there a certificate of completion available at the end of the program?

    Yes, there is a certificate of completion available at the end of the program for those who successfully complete all the course requirements.

    Will I have access to the materials after the program has ended?

    Yes, you will have access to the course materials even after the program has ended. You can refer to the course materials anytime to revise and refresh your knowledge.

    What kind of support will I receive during the program?

    You will have access to a dedicated instructor, as well as a support team to help you with any questions or issues you may have during the course. On top, you get to communicate with your personal Learning Manager. Your support is ensured from day 1 and before.

    Do you have any hands-on activities or projects?

    Yes, there are several hands-on activities and projects during the course to help you apply the concepts learned in real-world scenarios. These activities and projects are designed to give you practical experience in building data engineering solutions on AWS.

    Will there be opportunities for hands-on learning?

    Yes, we include a complete package of hands-on training and exercises to help you apply what you learn to a real-world setting.

    What kind of career opportunities can I expect after completing this program?

    After completing this program, you can expect to pursue career opportunities such as AWS Data Engineer, Big Data Engineer, Cloud Data Engineer, Data Architect, and Cloud Solutions Architect. The demand for professionals with skills in data engineering on AWS is increasing rapidly, and this program can help you capitalize on this demand.

    How much time should I dedicate to the course each week?

    The amount of time required will vary depending on the schedule you have opted for. You should check the course description for an estimated time commitment, and plan to dedicate enough time to complete the course within the allotted time frame.

    Can I interact with other students taking the program?

    Yes, we provide a friendly platform for students to interact with each other, such as a discussion forum or chat room. We also provide an engaging learning experience through collaborative projects.

    Tools Covered

    Course Completion Certification

    Data Engineering Sample Certificate

    Learning Objectives

    Understand the AWS data ecosystem and how to use various services and tools to build data engineering pipelines
    Write Python and SQL queries to perform data transformations and analytics
    Setting up local development environment for AWS on windows/mac
    Learn how to store and manage large volumes of data using AWS S3
    To secure your AWS resources using IAM
    Use PySpark for Big Data Analysis
    Data ingestion using Lambda Functions
    Populating DynamoDB table with data
    Perform ETL operations on large datasets using AWS Glue and Lambda
    Build scalable and efficient data processing workflows using PySpark and EMR
    Understand and utilize various data warehousing and data querying techniques using Redshift and Athena
    Learn how to ingest real-time data streaming pipelines using Kinesis

    Target Audience

    • Computer Science or IT Students is highly desirable
    • or other graduates with passion to get into IT
    • Data Warehouse, Database, SQL, ETL developers who want to transition to Data Engineering roles

    Curriculum

    156 Lessons200h

    Introduction to Data Engineering

    AWS Fundamentals

    Data Storage and Management

    Data Integration and Transformation

    Database Fundamentals

    SQL Database Fundamentals

    NoSQL Database Fundamentals

    Key-value stores

    Data modeling and Database Designs

    Python (Fundamentals)

    Python (Intermediate)

    Python (Advanced)

    Introduction to Data Engineering:

    ETL (Extract, Transform, Load) processes

    Data Extraction

    Data Integration and Transformation:

    Data Loading

    Big Data Processing and open source tools:

    Distributed data processing using AWS Apache spark

    AWS Fundamentals

    Securing AWS resources using IAM

    Accessing AWS via Command line interface

    AWS Storage (S3 and Glacier) Storage

    Setting up Local Development Environment

    Setting up environment for practice using Cloud9

    Working with EC2 Instances

    Advanced EC2 Instance Management

    Data ingestion using Lambda Functions Introduction to Serverless Computing and AWS Lambda

    Development Lifecycle for PySpark

    Developing Your First ETL Job with AWS Glue

    Spark History server for glue jobs

    Mastering AWS Glue Catalog

    Programmatically Interacting with AWS Glue Using API

    Incremental Data Processing with AWS Glue Job Bookmark

    Getting started with AWS EMR

    Deploying Spark applications using AWS EMR

    Optimizing Data on EMR

    Building a Streaming Pipeline using Kinesis

    Setting up Kinesis Delivery Stream for s3

    Getting most out /of Amazon Athena

    Amazon Athena using AWS CLI

    Amazon Athena using Python boto3

    Getting started with Amazon Redshift

    Copy data from S3 into Redshift tables

    Develop applications using Redshift cluster

    Redshift Tables with Distkeys and Sortkeys

    Redshift Federated Queries and spectrum

    US$1,300.00US$1,500.00

    13% off
    Level
    All Levels
    Duration 200 hours
    Lectures
    156 lectures
    Language
    English
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