Big Data Engineer Masters Program
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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.
Big Data Engineer Masters Program Syllabus
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Big Data Foundations - Preparatory Course
LIVE TRAINING
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 TrainingÂ
- NoSQL FundamentalsÂ
- Linux Fundamentals
- Working With Git & GitHub
Python For Data Engineering
LIVE TRAINING
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
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Distributed Data Processing
LIVE TRAINING
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
LIVE TRAINING
This is an Individual Project designed to equip the learners with Hands-On E
AWS Certified Data Analytics Specialty - Certification Training
LIVE 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 ServicesÂ
- 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
HANDS-ON
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
LIVE TRAINING
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
LIVE TRAINING
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
HANDS-ON
This section consists of One Group Project that covers all the concepts of the program.
Tools Covered
Job market overview
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
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
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.
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.
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.
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.
Yes, there is a certificate of completion available at the end of the program for those who successfully complete all the course requirements.
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.
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.
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.
Yes, we include a complete package of hands-on training and exercises to help you apply what you learn to a real-world setting.
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.
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.
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
Upfront Payment
32% off
Pay upfront and save 32% on tuition fee
INR 85,000
INR 58,000
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Monthly Payment
20% off
Pay monthly and save 20% on tuition fee
INR 10,000
Total up to 60,000
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Scholarship
70% off
Avail upto 70% Scholarship
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Learning Objectives
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
Fill out a form, and get PDF curriculum delivered straight to your inbox. Accelerate your learning journey on our platform
Introduction to Data Engineering
AWS Fundamentals
Data Storage and Management
Data Integration and Transformation
Overview of Data Integration and Transformation
Database Fundamentals
Introduction to Databases
Relational Database Management Systems
Structured Query Language (SQL)
Database Security and Administration
Understanding Database Transactions
OLAP and OLTP
ACID and BASE
SQL Database Fundamentals
Data types
Table Creation
Table Creation
Data Manipulation
Joins
Aggregate Functions
NoSQL Database Fundamentals
Document-oriented databases
Key-value stores
Graph databases
Column-family stores
Key-value stores
Data modeling and Database Designs
Conceptual Data Modeling
Logical Data Modeling
Physical Data Modeling
Normalization
Indexing
Query Optimization
Data Integrity
Python (Fundamentals)
Essential Syntax and Data Structures
Functions
Object-Oriented Programming
File Handling
Basic Data Analysis
Connecting to Databases
Executing Basic SQL Commands
Python (Intermediate)
Data Wrangling with Pandas
Data Preparation, Cleaning, and Transformation
Leveraging Multiprocessing and Multithreading for Improved Performance
Python (Advanced)
Advanced Data Manipulation with Pandas
Advanced Data Analysis
Grouping and Aggregation Techniques
Introduction to Data Engineering:
Overview of Data Engineering
Data Storage and Retrieval
Data Processing and Transformation
Data Pipelines and Workflows
Data Quality and Governance
Data Integration and ETL (Extract, Transform, Load)
ETL (Extract, Transform, Load) processes
Extracting Insights from Raw Data
Transforming Data for Efficient Processing
Loading Data with Confidence
Data Profiling for Better Decision Making
Cleaning Data for Actionable Insights
Integrating Data for a Holistic View
Error Handling for Seamless Data Management
Data Extraction
Identifying the data sources
Selecting the appropriate data extraction tools and techniques
Defining the data extraction requirements
Validating the extracted data
Data Integration and Transformation:
Overview of Data Integration and Transformation
Data Integration Techniques and Best Practices
Semantic Transformation in Data Integration
Common Challenges and Pitfalls of Data Integration and Transformation
Data Transformation Techniques and Tools
Data Warehouse Storage and Management
Data Loading
Initial load
Incremental load
Verifying the referential integrity between the dimensions and the fact tables
Big Data Processing and open source tools:
Overview of Big Data Processing and Open Source Tools
Hadoop: A Framework for Storing and Processing Data
Apache Spark: An Analytics Engine for Large Scale Data
Cassandra and MongoDB: NoSQL Databases for Big Data
HPCC and Apache Storm: Distributed Computing Platforms for Big Data
KNIME Analytics Platform, RapidMiner, and RStudio: Open Source Tools for Big Data Analytics
Distributed data processing using AWS Apache spark
Overview of Distributed Data Processing using AWS Apache Spark
Apache Spark: A Distributed Processing Framework for Big Data
Amazon EMR: A Managed Service for Running Apache Spark on AWS
Spark Libraries for Machine Learning, Stream Processing, and Graph Analytics
Data Processing with Apache Spark on Amazon SageMaker
AWS Fundamentals
Overview of Cloud Computing and AWS
AWS Core Services
Storage and Management
AWS Security and Compliance
AWS Pricing and Support
AWS Architecture and Design
AWS Operations and Management
Securing AWS resources using IAM
AWS Identity and Access Management (IAM) Introduction
AWS IAM Policies and Permissions
Managing AWS IAM Roles, Users and Groups
Accessing AWS via Command line interface
Configure and Validate AWS CLI
AWS Storage (S3 and Glacier) Storage
Getting started with S3
Storage: Deep dive into S3
Setting up Local Development Environment
Setting up local development environment for AWS on windows
Setting up local development environment for AWS on Mac
Setting up environment for practice using Cloud9
Cloud9 features you must know
Connecting and Working with Cloud9
Working with EC2 Instances
Launching and connecting to EC2
Managing EC2 instances
Securing EC2 connections and resources
Advanced EC2 Instance Management
Metadata, Querying, Filtering, and Bootstrapping
Creating and Validating AMIs
Data ingestion using Lambda Functions Introduction to Serverless Computing and AWS Lambda
Developing and Deploying lambda Functions for Data Ingestion
Automating data processing with AWS Lambda and Event-Driven Architectures
Optimization of AWS Lambda functions
Development Lifecycle for PySpark
Introduction to PySpark and Spark Session
Data Ingestion with PySpark
Data Processing with PySpark APIs
Data Export with PySpark
Productionizing PySpark Code
Developing Your First ETL Job with AWS Glue
AWS Glue Job and Basic Configuration
Creating and Running Your First AWS Glue ETL Job
Spark History server for glue jobs
Setting up Spark History server for glue jobs
Running AWS Glue Jobs with Spark UI Container
Mastering AWS Glue Catalog
Creating and Managing Glue Catalog Tables
Managing Glue Catalog Programmatically
Programmatically Interacting with AWS Glue Using API
Updating IAM Role and Creating a Baseline Glue Job
Partitioning Data with Glue Script
Incremental Data Processing with AWS Glue Job Bookmark
Overview of Glue Job Bookmark
Running jobs with Glue Job Bookmarks
Incremental Data Processing with Glue Job Bookmarks
Getting started with AWS EMR
AWS EMR Cluster Fundamentals
EMR Cluster Configuration and Management
Deploying Spark applications using AWS EMR
Deploying Applications using AWS EMR
Running Spark Applications on AWS EMR Cluster
Managing Applications on AWS EMR Clusters
Optimizing Data on EMR
Security and Networking
Optimization and Performance Tuning
Managing data in EMR
Troubleshooting, debugging and Best Practice
Building a Streaming Pipeline using Kinesis
Streaming Data Processing with AWS Kinesis
Setting up the Streaming Pipeline
Using Kinesis Firehose for Data Delivery
Setting up Kinesis Delivery Stream for s3
Accessing and Reading S3 Objects with Kinesis and Boto3
Setting Up Access and reading S3 objects with Kinesis and Boto
Working with DynamoDB using Boto3
Getting most out /of Amazon Athena
Amazon Athena and Glue Catalog
Creating Tables and Populating Data in Athena
Partitioning Data in Athena
Amazon Athena using AWS CLI
Utilizing Amazon Athena using AWS CLI
Managing Athena with AWS CLI
Running Athena Queries with AWS CLI
Amazon Athena using Python boto3
Amazon Athena using Python boto3
Managing Amazon Athena using Python boto3
Run Amazon Athena Queries using Python boto3
Getting started with Amazon Redshift
Setting up and Managing Redshift Cluster
Querying Redshift Tables
Redshift Tables Management
Copy data from S3 into Redshift tables
Introduction and Overview of Redshift Copy Command
Setting up and Running the Redshift Copy Command
Copying Data using IAM Role and JSON Dataset
Develop applications using Redshift cluster
Setting up Redshift Cluster and Access
Working with Redshift Databases and Tables
Interacting with Redshift using Python
Redshift Tables with Distkeys and Sortkeys
Redshift Architecture and Cluster Creation
Redshift Tables and Distribution Strategies
Maintenance and Troubleshooting
Redshift Federated Queries and spectrum
Setting up RDS and Redshift Integration
Data Processing with Redshift Federated Queries
Running Queries with Redshift Spectrum
Clean up and Maintenance
Preview
The Introduction
Introduction to Course
Introduction to Data Engineering
Cloud Fundamentals
Quick Review of AWS
Build your Database Foundations
Concepts of DBMS
SQL Fundamentals
NoSQL Fundamentals
Understanding Data Modelling and Designing
Introduction to Data Modelling and Database Design
Conceptual data modeling and Entity-Relationship diagrams
Types of Data Modelling, Normalization and Denormalization
Indexing and Query Optimization Techniques
Constraints and Triggers for Data Integrity
Choosing the Appropriate Design for given scenario
Best Practices for Data Modelling and Database Design
Python for Data Engineering
Python Essentials
Python for Data Engineering – Foundations
Python for Data Engineering – Advanced
Individual Project – 1
Understanding the concepts of Data Engineering
Introduction to Data Engineering
Understanding ETL Processes
Data Extraction ,Integration, Transformation and Data Loading
Explore in-depth concepts of Data Engineering
Exploring Big Data Processing and Open-Source Tools
Introduction to Big Data Processing and Open-Source Tools
Hadoop Framework and Apache Spark Analytics Engine
Cassandra and MongoDB: NoSQL databases for Big Data storage and retrieval
Distributed computing platforms – HPCC and Apache Storm
Working with KNIME Analytics Platform, RapidMiner, and RStudio
Dive into Cloud Computing and AWS
Concepts of Cloud Computing
AWS Fundamentals
Working with AWS
Mastering Distributed Data Processing using AWS Apache Spark
Overview of distributed data processing using AWS Apache Spark
Apache Spark as a distributed processing framework for big data
Data processing with Apache Spark on Amazon Sage Maker
Amazon EMR for running Spark on AWS
Building a Spark application on AWS
Best practices for distributed data processing using AWS Apache Spark
Group Project – 1
Working with the Tools for Data Engineering Part – 1 (Hands-on)
Working with AWS Lambda
Development Lifecycle for AWS PySpark
Working with AWS Glue
Mastering AWS EMR
Tools for Data Engineering Part – 2 (Hands-on)
Building a Streaming Pipeline using Kinesis
Working with DynamoDB using Boto3
Getting most out of Amazon Athena
Getting started with Amazon Redshift
Group Project – 2
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