Big Data Engineer Masters Program

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Last Update April 27, 2024
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    All Levels
    Duration 200 hours
    199 lectures

    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.

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      Big Data Engineer Masters Program Syllabus

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        Big Data Foundations - Preparatory Course


        Big Data Foundation module offers comprehensive knowledge in Big Data, SQL, NoSQL, Linux, and Git. You’ll learn database management, querying, data manipulation, Linux operations, and version control using Git. This solid foundation primes you for a successful career in the ever-evolving Big Data landscape. 

        Course Content
        • Database Fundamentals
        • SQL Training 
        • NoSQL Fundamentals 
        • Linux Fundamentals
        • Working With Git & GitHub

        Python For Data Engineering


        This course is designed to equip you with the essential skills to excel in data engineering tasks using Python. This course covers Python Basics, OOPs in Python, Data Structures, and essential Python Libraries for Data Engineering. Learn Python fundamentals, OOPs concepts, and data manipulation with Python Libraries like NumPy, Pandas, and Matplotlib, enabling you to build robust data pipelines and solutions.
        Course Content
        • Python Essentials
          • Knowing ABC’s of Python
          • Object-oriented programming and File handling
          • Basic data analysis using Python libraries
          • Connecting to databases and executing SQL Commands.
        • Python for Data Engineering – Foundations
          • Data Wrangling with Pandas including selecting and filtering data, grouping and aggregating data, merging and joining datasets, and handling missing data
          • Data Preparation, Cleaning, Transformation and feature extraction
          • Leveraging Multiprocessing and Multithreading for Improved Performance.
        • Python For Data Engineering – Advanced
          • Advanced data manipulation with Pandas
          • Handling missing and inconsistent data with Pandas
          • Data preparation, cleaning, and transformation techniques
          • Advanced data analysis techniques
          • Grouping and aggregation techniques with Pandas

        Distributed Data Processing


        This course covers Distributed Data Processing using Big Data Hadoop, HDFS, Apache Spark, PySpark, and Hive. Explore fundamentals of Hadoop and HDFS for data management, learn Apache Spark. Become expert in PySpark and efficient data processing. Interact with distributed data using Hive’s HQL queries. Hands-on projects for practical expertise in distributed data processing. Master Hadoop eco-system to tackle big data challenges and drive data-driven insights.

        Course Content
        • Mastering Hadoop and HDFS
          • Introduction to Hadoop and Big Data
          • Hadoop Distributed File System (HDFS) Architecture
          • Hadoop Cluster Setup and Configuration
          • Data Storage and Replication in HDFS
          • Data Ingestion and Processing with Hadoop
          • Hadoop MapReduce Framework
          • Hadoop Ecosystem Overview (Hive, Pig, HBase, etc.)
        • Working with PySpark
          • Spark Architecture and Components
          • Resilient Distributed Datasets (RDDs)
          • Spark DataFrame and SparkSQL
          • Spark Streaming and Real-Time Data Processing
          • Graph Processing with GraphX
        • Working with Hive
          • Hive Architecture and Metastore
          • HiveQL, Hive Data Modeling and Schemas
          • Hive Data Manipulation and Query Optimization
          • Hive UDFs and Custom Functions

        Individual Project -1


        This is an Individual Project designed to equip the learners with Hands-On E

        AWS Certified Data Analytics Specialty - Certification Training


        This comprehensive certification course is designed to transform you into an AWS data analytics expert. Gain proficiency in data collection, storage, and processing using Amazon S3, Redshift, and AWS Glue. Build scalable data pipelines for ETL through hands-on practice. Explore data analysis and visualization with Amazon QuickSight. Dive into machine learning using Amazon SageMaker and real-time data processing with Amazon Kinesis. Prepare for the certification exam and unlock new career possibilities in AWS data analytics.

        Course Content
        • AWS Fundamental Services
        • Data Collection On AWS
        • AWS Storage Services
        • AWS Processing Services 
        • AWS Analytical Services
        • Mastering Data Visualization
        • AWS Security Concepts

        Snowflake Advanced Data Engineer Certification

        SELF PACED

        This comprehensive certification course is designed to equip you with advanced skills in Snowflake data engineering and analytics. Covering data modeling, loading, unloading, and performance optimization, you’ll learn to design efficient data pipelines. Explore Snowflake’s features for data security, sharing, and scaling. Gain hands-on experience with Snowflake’s cloud-based platform, preparing for the SnowPro Advanced Data Engineer Certification. Unlock the full potential of Snowflake for advanced data engineering and analytics in this exciting journey.

        Course Content
        • Snowflake Core components
          • Introduction to Snowflake and Cloud Data Platform
          • Snowflake Architecture and Components
          • Snowflake Data Warehousing and Data Sharing
          • Snowflake Virtual Warehouses and Clusters
          • Snowflake Data Loading and Unloading
          • Data Modeling and Schema Design in Snowflake
          • Querying and Optimizing Performance in Snowflake
          • Data Security and Access Control in Snowflake
          • Managing Snowflake Objects and Metadata
        • Snowflake Advanced Data Engineer Certification Training
          • Advanced Snowflake Concepts for Data Engineering

        Group Project - 1


        This section consists of One Group Project that covers the Concepts of AWS Data Analytics Specialty certification which helps to gain Real Time Project Experience.

        Dive Into Data Lake Table Format Frameworks


        This comprehensive certification course is designed to provide in-depth knowledge of data lake storage frameworks. Explore Delta Lake and Hudi, powerful technologies for data lake management. Learn about data consistency, reliability, and versioning with Delta Lake. Discover Hudi’s capabilities for stream processing and efficient data ingestion. Work on real-world projects and elevate your expertise in data lake storage solutions.

        Course Content
        • Delta Lake – Open Source Table Format Framework
          • Introduction to Data Lake and Delta Lake
          • Delta Lake Architecture and Components
          • ACID Transactions in Delta Lake
          • Data Versioning and Schema Evolution
          • Data Consistency and Reliability in Delta Lake
          • Data Management and Optimization with Delta Lake
          • Performance Tuning and Query Optimization
          • Integrating Delta Lake with Data Lake Ecosystem
        • Understanding the Apache Hudi
          • Introduction to Hudi (Hadoop Upserts, Deletes, and Incrementals)
          • Hudi Architecture and Core Components
          • Hudi Write Operations and Data Ingestion
          • Stream Processing and Incremental Data Ingestion
          • Upsert and Delete Operations in Hudi
          • Hudi Table Management and Data Compaction
          • Optimizing Performance with Hudi
          • Integrating Hudi with Data Lake and Data Processing Frameworks

        DevOps Foundations


        This comprehensive course is designed to provide a strong foundation in DevOps practices and principles. Participants will gain a deep understanding of DevOps culture, methodologies, and tools, enabling them to improve collaboration and streamline software development and deployment processes.

        Course Content
        • Introduction to DevOps and its Principles
        • DevOps Culture and Collaboration
        • Understanding Continuous Integration and Continuous Deployment (CI/CD)
        • Version Control with Git
        • Automated Build and Deployment using Jenkins
        • Containerization with Docker
        • Infrastructure as Code (IaC) with Terraform
        • Configuration Management with Ansible or Chef
        • Monitoring and Logging in DevOps
        • Security in DevOps
        • DevOps Best Practices and Case Studies

        Group Project - 2


        This section consists of One Group Project that covers all the concepts of the program.

        Tools Covered

        Job market overview
        image 2

        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

        • 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.


        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.

        Course Completion Certification


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              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
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                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


                This is a small preview to the Data Engineering on AWS Masters Program.

                The Introduction

                Build your Database Foundations

                Understanding Data Modelling and Designing

                Python for Data Engineering

                Individual Project – 1

                This section consists of One Individual Project. Learners gain Practical knowledge on the different topics such as Database Fundamentals, Python for Data Engineering.

                Understanding the concepts of Data Engineering

                Exploring Big Data Processing and Open-Source Tools

                Dive into Cloud Computing and AWS

                Mastering Distributed Data Processing using AWS Apache Spark

                Group Project – 1

                This section consists of Group Project. Learners gain Hands-On Experience on the Data Engineering tasks using AWS tools such as Lambda, Glue, PySpark and EMR

                Working with the Tools for Data Engineering Part – 1 (Hands-on)

                Tools for Data Engineering Part – 2 (Hands-on)

                Group Project – 2

                This section consists of Group Project. Learners gain Hands-On Experience on the Data Engineering tasks using AWS tools such as Kinesis, DynamoDB, Athena and Redshift
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