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Master Certificate in Data Engineering

Develop scalable, secure, and cost-optimized data solutions in AWS, Azure, and Google Cloud
Build strong foundations in Python, SQL, and NoSQL for modern data engineering
Process large-scale datasets with Hadoop, Spark, and real-time streaming tools
Implement advanced data modeling, ETL pipelines, and orchestration workflows
Work with cutting-edge platforms like Snowflake and integrate AI-driven analytics

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Start Date

Start Anytime

Start Date

Start Anytime

Start Date

Start Anytime

Program Duration

6 months

Mon & Wed (18:00 - 21:00 hrs)

Program Duration

6 months

Mon & Wed (18:00 - 21:00 hrs)

Program Duration

6 months

Mon & Wed (18:00 - 21:00 hrs)

Learning Format

Intructor-Led (Live Project-Based Learning)

Individuals who want structure & accountability

Evenings, 2x per week (6-9 PM)

Live classes, group projects, custom labs, 1:1 support

Mentor-Guided (Self-Paced + Projects)

Motivated learners who want flexible learning with support

Flexible + weekly mentor
check-in

Guided labs, weekly mentor sessions, TA support, real projects

Intructor-Led (Live Project-Based Learning)

Individuals who want structure & accountability

Evenings, 2x per week (6-9 PM)

Live classes, group projects, custom labs, 1:1 support

Mentor-Guided (Self-Paced + Projects)

Motivated learners who want flexible learning with support

Flexible + weekly mentor check-in

Guided labs, weekly mentor sessions, TA support, real projects

Program Overview

Designed for professionals ready to advance their careers, this program covers essential skills in Python, SQL, NoSQL, Big Data, and cloud platforms including AWS, Azure, GCP, and Snowflake. Gain practical, hands-on experience through immersive labs and a capstone project that helps you build a strong portfolio. Prepare for key industry certifications to boost your credentials and open doors to high-demand roles in data engineering and analytics.

Course Outline

Fast-track your career as a data engineering professional with our comprehensive training program. Master Certificate in Data Engineering covers essential data engineering and big data concepts, the Hadoop ecosystem, Apache tools, Python for data engineering, and hands-on experience with AWS services such as EMR, Quicksight, and SageMaker, along with core Azure data services.

Download Outline

Python Basics for Data Engineering

Database Management using SQL

NoSQL Mastery with MongoDB

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SKILLS GAINED

……………………..

Database Management using SQL

Gain strong SQL skills to design, query, and manage relational databases. Learn advanced commands, joins, indexing, stored procedures, and user access controls for secure and scalable database operations.

Concepts Learned:

SQL statements, filtering, ordering, grouping, and joins
Aggregate, mathematical, and string functions
Subqueries, views, and stored procedures
Transactions, indexing, and query optimization
User access control and security functions
Backup and recovery strategies

SKILLS GAINED

……………………..

AWS Certified Data Engineer – Associate

Azure Data Engineer (DP-203)

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SKILLS GAINED

……………………..

NoSQL Mastery with MongoDB

Master the concepts of NoSQL and MongoDB to manage semi-structured and unstructured data. Gain hands-on skills in schema design, indexing, performance optimization, and integration into modern data pipelines.

Concepts Learned:

NoSQL database fundamentals and MongoDB architecture
Document structures and schema design
CRUD operations, querying, and aggregation
Indexing strategies for performance
Security, authentication, and access control
Scalability, replication, and sharding
Integration of MongoDB with data engineering workflows

SKILLS GAINED

……………………..

Big Data with Hadoop and Spark

Learn the Hadoop ecosystem and Spark framework to process, analyze, and manage massive datasets. Build data ingestion pipelines, perform distributed computing, and apply machine learning with Spark ML.

Concepts Learned:

Hadoop architecture (HDFS, YARN, MapReduce)
Hive, Pig, HBase, Sqoop, and Flume for data management
Data ingestion with Sqoop, Flume, and Kafka
Apache Spark RDDs, DataFrames, and SQL operations
Real-time data streaming with Spark Structured Streaming
Spark GraphX and GraphFrames for graph data processing
Machine Learning with Spark ML

SKILLS GAINED

……………………..

AWS Tech Essentials

Gain foundational knowledge of AWS cloud services and learn how to deploy, secure, and manage workloads. Build hands-on expertise in compute, storage, networking, and serverless services.

Concepts Learned:

AWS cloud concepts, services, and console navigation
Compute with EC2, Lambda, and autoscaling
Storage with S3, EBS, and content delivery
Identity and Access Management (IAM)
Networking and Virtual Private Clouds (VPC)
Managed databases and pricing models

SKILLS GAINED

……………………..

AWS Certified Data Engineer – Associate

Prepare for the AWS Certified Data Engineer – Associate exam by mastering data pipelines, cloud storage, and advanced data processing with AWS services. Gain the expertise needed for real-world data engineering in AWS.

Concepts Learned:

Data engineering fundamentals and AWS ecosystem
Data modeling and quality assurance
Compute services (EC2, Lambda, Batch, ECS, EKS)
Databases: DynamoDB, RDS, Redshift
Data lakes and analytics: Athena, Glue, Spark, Kinesis
Migration, security, and compliance in AWS
Developer tools (CLI, CDK) and governance frameworks

SKILLS GAINED

……………………..

Azure Fundamentals

Learn the essentials of Microsoft Azure and cloud computing. Understand services, deployment models, security, and compliance while building and managing applications on the Azure platform.

Concepts Learned:

Core Azure services (VMs, storage, web apps, databases)
Cloud models: Public, private, and hybrid
Azure AD and identity management
Pricing, support, and compliance
Deploying databases and applications on Azure

SKILLS GAINED

……………………..

Azure Data Engineer (DP-203)

Specialize in Azure data engineering by learning to design, build, and optimize batch and streaming data solutions. Gain expertise in provisioning storage, implementing security, and integrating data services.

Concepts Learned:

Azure data engineering fundamentals
Storage solutions with SQL Database, Synapse, Data Lake
Data ingestion with Azure Data Factory and Event Hubs
Azure Databricks for big data processing
Batch processing and real-time streaming
Security, data masking, and retention policies
Optimization and monitoring of Azure data solutions

SKILLS GAINED

……………………..

Data Engineering Capstone Project

The data engineering capstone project will allow you to implement the skills you learned throughout this program. Through dedicated mentoring sessions, you’ll learn how to solve real-world, industry-aligned data engineering challenges, from setting up configuration, ETL, data streaming, and data analysis to data visualization. This project is the final step in the learning path and will enable you to showcase your expertise in data engineering to future employers. To make your practice more relevant, you can choose to work on projects that cover the most relevant domains.

SKILLS GAINED

……………………..

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SKILLS GAINED

……………………..

Python Basics for Data Engineering

Develop the Python foundation required to build, automate, and manage modern data engineering workflows. Learn procedural and object-oriented programming, control structures, and multi-threading to efficiently handle large-scale data operations.

Concepts Learned:

Fundamentals of Python programming and syntax
Functions, variables, and control flow (loops, conditionals)
Object-Oriented Programming (OOP) principles
Data types, operators, and string manipulation
Multi-threading for performance optimization
Effective use of Jupyter Notebook for data engineering tasks

SKILLS GAINED

……………………..

Python Basics for Data Engineering

Big Data with Hadoop and Spark

AWS Tech Essentials

Azure Fundamentals

Data Engineering Capstone Project

NoSQL Mastery with MongoDB

Concepts Learned:

NoSQL database fundamentals and MongoDB architecture
Document structures and schema design
CRUD operations, querying, and aggregation
Indexing strategies for performance
Security, authentication, and access control
Scalability, replication, and sharding
Integration of MongoDB with data engineering workflows

SKILLS GAINED

……………………..

Big Data with Hadoop and Spark

Concepts Learned:

Hadoop architecture (HDFS, YARN, MapReduce)
Hive, Pig, HBase, Sqoop, and Flume for data management
Data ingestion with Sqoop, Flume, and Kafka
Apache Spark RDDs, DataFrames, and SQL operations
Real-time data streaming with Spark Structured Streaming
Spark GraphX and GraphFrames for graph data processing
Machine Learning with Spark ML

SKILLS GAINED

……………………..

AWS Tech Essentials

Gain foundational knowledge of AWS cloud services and learn how to deploy, secure, and manage workloads. Build hands-on expertise in compute, storage, networking, and serverless services.

Concepts Learned:

AWS cloud concepts, services, and console navigation
Compute with EC2, Lambda, and autoscaling
Storage with S3, EBS, and content delivery
Identity and Access Management (IAM)
Networking and Virtual Private Clouds (VPC)
Managed databases and pricing models

SKILLS GAINED

……………………..

AWS Certified Data Engineer – Associate

Concepts Learned:

Data engineering fundamentals and AWS ecosystem
Data modeling and quality assurance
Compute services (EC2, Lambda, Batch, ECS, EKS)
Databases: DynamoDB, RDS, Redshift
Data lakes and analytics: Athena, Glue, Spark, Kinesis
Migration, security, and compliance in AWS
Developer tools (CLI, CDK) and governance frameworks

SKILLS GAINED

……………………..

Azure Fundamentals

Learn the essentials of Microsoft Azure and cloud computing. Understand services, deployment models, security, and compliance while building and managing applications on the Azure platform.

Concepts Learned:

Core Azure services (VMs, storage, web apps, databases)
Cloud models: Public, private, and hybrid
Azure AD and identity management
Pricing, support, and compliance
Deploying databases and applications on Azure

SKILLS GAINED

……………………..

Azure Data Engineer (DP-203)

Concepts Learned:

Azure data engineering fundamentals
Storage solutions with SQL Database, Synapse, Data Lake
Data ingestion with Azure Data Factory and Event Hubs
Azure Databricks for big data processing
Batch processing and real-time streaming
Security, data masking, and retention policies
Optimization and monitoring of Azure data solutions

SKILLS GAINED

……………………..

Data Engineering Capstone Project

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

Python Basics for Data Engineering

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

Database Management using SQL

Python Basics for Data Engineering

NoSQL Mastery with MongoDB

Big Data with Hadoop and Spark

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

AWS Tech Essentials

AWS Certified Data Engineer – Associate

Azure Fundamentals

Azure Data Engineer (DP-203)

Data Engineering Capstone Project

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

NoSQL Mastery with MongoDB

Concepts Learned:

NoSQL database fundamentals and MongoDB architecture
Document structures and schema design
CRUD operations, querying, and aggregation
Indexing strategies for performance
Security, authentication, and access control
Scalability, replication, and sharding
Integration of MongoDB with data engineering workflows

SKILLS GAINED

……………………..

Big Data with Hadoop and Spark

Concepts Learned:

Hadoop architecture (HDFS, YARN, MapReduce)
Hive, Pig, HBase, Sqoop, and Flume for data management
Data ingestion with Sqoop, Flume, and Kafka
Apache Spark RDDs, DataFrames, and SQL operations
Real-time data streaming with Spark Structured Streaming
Spark GraphX and GraphFrames for graph data processing
Machine Learning with Spark ML

SKILLS GAINED

……………………..

AWS Tech Essentials

Gain foundational knowledge of AWS cloud services and learn how to deploy, secure, and manage workloads. Build hands-on expertise in compute, storage, networking, and serverless services.

Concepts Learned:

AWS cloud concepts, services, and console navigation
Compute with EC2, Lambda, and autoscaling
Storage with S3, EBS, and content delivery
Identity and Access Management (IAM)
Networking and Virtual Private Clouds (VPC)
Managed databases and pricing models

SKILLS GAINED

……………………..

AWS Certified Data Engineer – Associate

Concepts Learned:

Data engineering fundamentals and AWS ecosystem
Data modeling and quality assurance
Compute services (EC2, Lambda, Batch, ECS, EKS)
Databases: DynamoDB, RDS, Redshift
Data lakes and analytics: Athena, Glue, Spark, Kinesis
Migration, security, and compliance in AWS
Developer tools (CLI, CDK) and governance frameworks

SKILLS GAINED

……………………..

Azure Fundamentals

Learn the essentials of Microsoft Azure and cloud computing. Understand services, deployment models, security, and compliance while building and managing applications on the Azure platform.

Concepts Learned:

Core Azure services (VMs, storage, web apps, databases)
Cloud models: Public, private, and hybrid
Azure AD and identity management
Pricing, support, and compliance
Deploying databases and applications on Azure

SKILLS GAINED

……………………..

Data Engineering Capstone Project

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

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