Professional Certificate Program in Data Engineering (In collaboration with Purdue University, AWS, and Microsoft Azure)

Professional Certificate Program in Data Engineering

In collaboration with Purdue University, AWS, and Microsoft Azure

• 7 months length program (Live class & eLearning)
• Live classroom (5-8hrs / Week weekend classes)
• Ask us for the next cohort and schedule details!

Accelerate your career with Purdue University Online’s Professional Certificate Program in Data Engineering. This program blends theory, case studies, and extensive hands-on practice to deliver a comprehensive data engineering education.

Aligned with DP-203 Azure Data Engineer Associate, AWS Certified Data Engineer – Associate, and SnowPro® Core Certification, it equips you to pursue these industry-recognized credentials.

You will master skills such as distributed processing with Hadoop, large-scale data processing using Spark, building data pipelines with Kafka, and managing databases across on-premise, AWS, and Azure infrastructures.

Designed for experienced professionals, the program combines self-paced learning, live virtual classes, hands-on projects, and integrated labs, ensuring a highly engaging and practice-driven experience.

Key Features

• Course and material are in English
• in collaboration with Purdue University Online
• Beginner to advanced level
• 7 months of live classroom by Industry experts (5-8 hours/week weekend classes) 
• 150+ hours of live classes and mentor-led project support
• 20+ hours eLearning video content
• 250+ hours of study time and practice recommended
• Flexible learning with session recordings and 24/7 access
• 20+ tools to gain practical experience
• Aligned with Microsoft DP-203, AWS Certified Data Engineer – Associate, and SnowPro® Core.
• Capstone from 3 domains and 14+ projects with Industry datasets from YouTube, Glassdoor, Facebook, etc.
• Networking benefits via Purdue’s Alumni Association
• Program completion certificate from Purdue University Online.
• Official badge/certificate hosted on the Microsoft Learn portal.

Engaging Learning Experience

• Peer Interaction
  Enjoy a true classroom-like environment by connecting with fellow learners and engaging with mentors in real time through Slack.
• Flexible Learning
  Never fall behind—access recorded sessions anytime to catch up and stay aligned with your cohort.
• Mentorship Sessions
  Receive expert support from mentors to resolve doubts, get project guidance, and enhance your learning journey.
• Dedicated Support
  Benefit from a Cohort Manager who provides personalized assistance and ensures you stay on track toward success.

About Purdue University

Purdue University is a leading public research university known for creating practical solutions to some of today’s most pressing problems. Recognized by U.S. News & World Report as one of the top 10 Most Innovative Universities in the U.S. for four consecutive years, Purdue is at the forefront of groundbreaking research and innovation.

What added value does Purdue University contribute to the program?

The program curriculum is designed and reviewed with the assistance of the university, which gives the program quality legitimacy and a co-branded certificate of completion. Please be aware that the live classes are not held by actual University faculty staff but by many experienced Industry experts who are suitable for each topic.

Learning Objective

• Gather, process, and store data from multiple sources on cloud and on-premises platforms.
• Ingest, transform, and orchestrate data pipelines using AWS services while ensuring data quality.
• Process big data efficiently to improve business productivity across volume, velocity, variety, and veracity.
• Use Amazon Kinesis for real-time big data processing and QuickSight for visualization and queries.
• Gain foundational knowledge of Scala and its development tooling for big data applications.
• Master the Hadoop ecosystem (Yarn, MapReduce, Pig, Hive, HBase, ZooKeeper, Oozie, Sqoop, Flume).
• Implement hands-on data solutions across diverse business applications.
• Design and manage Azure data storage and processing solutions using Cosmos DB, SQL Database, Synapse Analytics, Data Lake, Data Factory, Stream Analytics, Databricks, and Blob Storage.
• Build secure, scalable Snowflake solutions to drive business objectives.
• Apply program knowledge to create industry-ready data engineering products using AWS EMR and other tools.

12+ Skills Covered

• Real-Time Data Processing
• Data Pipelining
• Big Data Analytics
• Data Visualization
• Provisioning Data Storage Services
• Apache Hadoop
• Ingesting Streaming and Batch Data
• Transforming Data
• Implementing Security Requirements
• Data Protection & Encryption Techniques
• Data Governance & Compliance Controls
• Scaling AI Initiatives
• Conversational AI
• AI Risk Assessment
• Predictive Analytics
• Workflow Automation
• Ethical AI and Compliance
• Chatbot Implementation
• AI Economics
• ROI Analysis
• Model Evaluation and Optimization
• LLMOps and AI Deployment
• Sentiment Analysis
• Scenario Simulation

Target Audience:

A data engineer builds and maintains data architectures for ingestion, processing, and deployment in large-scale, data-intensive applications. This career is ideal for professionals passionate about data, including:

• IT professionals
• Database administrators
• Beginners entering the data engineering field
• BI developers
• Data scientists looking to expand their skill set

Prerequisites:

• Experience: Preferably 2+ years of professional work experience.
• Education: High School Diploma, Bachelor’s Degree, or equivalent.
• Skills: Basic understanding of programming concepts and mathematics.

Learning Path

1. Python Basics for Data Engineering
2. Database Management using SQL
3. NoSQL Mastery with MongoDB
4. Big Data with Hadoop and Spark
5. AWS Tech Essentials
6. AWS Certified Data Engineer - Associate
7. Azure Fundamentals
8. Azure Data Engineer
9. Data Engineering Capstone

Electives

• Snowflake SnowPro Core Certification
• Version Control using Git
• Google Cloud Platform Fundamentals
• Generative AI Masterclass

COURSE CONTENT DETAILS

Course 1: Python Basics for Data Engineering

Build foundational Python skills essential for advancing through the program.

Learning Outcomes

• Gain proficiency in procedural and object-oriented programming.
• Master loops, conditional statements, data types, operators, and string functions.
• Understand variable scope, methods, attributes, access modifiers, identifiers, indentations, and comments.
• Learn to install Python, use IDEs, and work with Jupyter Notebook.
• Acquire knowledge of multi-threading and its applications in data processing.

Course curriculum

• Introduction to Python Programming
• Fundamentals of Programming & Python Functions
• Object-Oriented Programming Concepts
• Python Data Types and Operators
• Conditional Statements and Loops
• Threading in Python

Course 2: Database Management using SQL

Gain essential SQL skills to effectively manage databases and integrate them into applications. Learn SQL statements, commands, joins, subqueries, conditional logic, and functions to support scalable data solutions.

Learning Outcomes

• Understand database structures and relationships.
• Gain proficiency in SQL queries, including filtering, ordering, aliasing, aggregate functions, grouping, joins, subqueries, views, and indexing.
• Work with common query tools and SQL commands.
• Apply string, mathematical, date/time, and pattern-matching functions.
• Master transactions, table creation, and views for efficient database management.
• Implement user access controls to ensure database security.
• Execute stored procedures for complex operations.

Course curriculum

• SQL Statements & Selection Commands (Filtering & Ordering)
• Aggregate Commands & Group By
• Conditional Statements
• Joins
• String, Mathematical, and Date/Time Functions
• Pattern (String) Matching
• User Access Control Functions
• Restore and Back-Up

Course 3: NoSQL Mastery with MongoDB

Master MongoDB and NoSQL databases with this comprehensive course. Learn data modeling, indexing, scalability, security, and integration with data engineering pipelines through hands-on labs and real-world projects. Gain the skills to efficiently store, retrieve, and process data in practical scenarios.

Learning Outcomes

• Master MongoDB fundamentals for data engineering projects.
• Build efficient data pipelines and perform storage, retrieval, and analytics.
• Apply MongoDB solutions to real-world data engineering challenges.
• Learn data modeling techniques for MongoDB collections.
• Gain hands-on experience querying, manipulating, and optimizing MongoDB data.

Topics Covered

• MongoDB security and access control
• Document structure and schema design
• Data management, processing, and modeling for scalability
• CRUD operations and querying
• Indexing and performance optimization
• Integration and scalability in pipelines
• Developing and monitoring data pipelines

Course 4: Big Data with Hadoop and Spark

After mastering workflow automation, this module applies generative AI to core business functions, beginning with sales and marketing. Through hands-on learning and case studies, participants will discover how to deploy AI effectively while overcoming common challenges to maximize business impact.

Learning Outcomes

• Navigate and optimize the Hadoop ecosystem for data processing.
• Ingest and process data using Sqoop, Flume, Kafka, and real-time streaming.
• Implement partitioning, bucketing, and indexing in Hive.
• Work with RDDs, UDFs, and UDAFs in Apache Spark.
• Apply Big Data concepts to real-world processing scenarios.

Topics Covered

• Introduction to Big Data and Hadoop ecosystem
• Hadoop Distributed File System (HDFS)
• MapReduce fundamentals, programming, and advanced concepts
• Distributed Cache, Job Chaining, and Hadoop Scheduler
• Apache Hive: Architecture, Data Types, Serialization, Optimization, UDF/UDAF
• Pig for data analysis
• Data ingestion, Data Lake vs Data Warehouse
• Apache Kafka and Flume for streaming data
• YARN: Infrastructure and Architecture
• Apache Spark: RDDs, PySpark Shell, Framework, and Machine Learning (Spark ML)
• Real-time processing: DStreams, Structured Streaming APIs
• Spark GraphX: GraphFrames, Pregel API, and algorithms
• Python for Big Data: Functions, OOPs, and modules

Course 5: AWS Tech Essentials

Learn to navigate the AWS Management Console, understand AWS security, storage, and database options, and gain proficiency in key web services such as RDS and EBS. This course equips you to efficiently identify and use AWS services.

Learning Outcomes

• Understand fundamental concepts of AWS and cloud computing.
• Identify deployment, networking, and service options in AWS.
• Learn AWS terminologies, benefits, and how to align services with business requirements.

Topics Covered

• Introduction to Cloud Computing and AWS
• Load Balancing, Auto Scaling, and DNS/Content Delivery
• AWS Managed Services, Databases, and Lambda
• Identity and Access Management (IAM)
• Networking: VPCs and Virtual Private Clouds
• Elastic Compute Cloud (EC2) and Storage Solutions
• Deployment, Management, Pricing, and Billing

Course 6: AWS Certified Data Engineer - Associate

Learn to design, develop, and manage data pipelines on AWS, preparing you for the AWS Certified Data Engineer—Associate exam and a career in cloud data engineering.

Learning Outcomes

• Understand data engineering fundamentals and modern storage systems.
• Gain proficiency in Amazon DynamoDB, RDS, Redshift, and data modeling techniques.
• Master analytics tools like Amazon Athena, Apache Spark, and AWS Glue.
• Learn cloud financial management and AWS cost optimization.
• Acquire skills in computing services: EC2, Lambda, and AWS Batch.
• Work with container technologies: Docker, ECS, and EKS.
• Develop expertise in developer tools, ML concepts, and governance principles.
• Understand migration, networking, content delivery, security, and compliance in AWS.
• Gain proficiency in storage solutions: S3, EBS, and EFS.
• Ensure data quality and implement best practices for data pipelines.

Topics Covered

• Data Engineering Fundamentals & Cloud Financial Management
• Analytics Tools: Amazon Athena, Apache Spark
• Compute Services: EC2, Lambda
• Data Modeling & Database Management (DynamoDB, Redshift)
• Developer Tools (AWS CLI, CDK) & Management/Governance (CloudFormation, CloudTrail)
• Networking & Content Delivery (VPC, Route 53)
• Security, Identity, Compliance & Migration/Transfer Services
• Containers & Orchestration (Docker, ECS)
• Storage Solutions (S3, EBS)
• Machine Learning Concepts (SageMaker) & Advanced Data Processing (Kinesis, Glue)

Course 7: Azure Fundamentals

Learn core cloud computing principles and their implementation in Microsoft Azure. Gain hands-on experience with Azure services, including virtual machines, web apps, SQL databases, Azure Active Directory, security, compliance, pricing, and on-premises integration.

Learning Outcomes

• Understand core cloud computing principles and Azure implementation.
• Work with Azure services including virtual machines, web apps, SQL databases, and Azure Active Directory.
• Learn Azure security, privacy, compliance, pricing, and support.
• Deploy and manage Azure storage and databases.
• Integrate Azure services with on-premises environments.

Topics Covered

• Introduction to Cloud Computing & IaaS
• Core Azure Services & SaaS
• Public, Private, and Hybrid Cloud Models
• Security, Privacy, Compliance, and Trust
• Azure Pricing, Support, and Economies of Scale
• Creating and Configuring Virtual Machines in Azure

Course 8: Azure Data Engineer (DP 203)

Expanding on generative AI applications in technical functions, this module focuses on how Product R&D teams can leverage AI to accelerate innovation. Participants will explore how AI automates market research, predicts customer needs, optimizes product design, and aligns product strategy with brand values. The goal is to enable data-driven, customer-centric, and future-ready product development while addressing ethical and privacy concerns.

Learning Outcomes

• Implement and manage data storage solutions using Azure SQL Database, Synapse Analytics, Data Lake Storage, Data Factory, Stream Analytics, and Databricks.
• Ingest, transform, and process batch and streaming data.
• Apply security, retention policies, and optimize performance.
• Monitor and access external data sources effectively.

Topics Covered

• Microsoft Azure Data Orchestration & Storage Accounts
• Azure Key Vault, SQL Pool, and Data Masking
• Azure Databricks, Synapse Notebooks, and Apache Spark
• Azure Data Factory & Event Hubs
• Structured and Unstructured Streaming
• Batch and Real-Time Data Processing
• Configuration of Synapse Link with Cosmos DB
• Databricks File System & Data Exploration

Course 9: Data Engineering Capstone

Apply your skills in a real-world, industry-aligned project covering ETL, data streaming, analysis, and visualization. Receive mentoring support while tackling practical challenges, and showcase your expertise to potential employers. Projects can be selected from relevant industry domains to maximize applicability.

Industry Case Studies and Projects

• Project 1 – Market Basket Analysis (Instacart): Analyze online grocery purchase patterns using a large dataset to uncover shopping trends.

• Project 2 – YouTube Video Analysis: Rank trending videos based on user interactions and extract actionable insights.

• Project 3 – Data Visualization with Azure Synapse: Create dashboards for sales data to guide retail expansion decisions.

• Project 4 –  End-to-End Data Ingestion Pipeline: Upload and manage large datasets in Azure Data Lake and Delta Lake for easy access and processing.

• Project 5 – Server Monitoring on AWS: Track EC2 performance, debug failures, and optimize server operations.

• Project 6 – E-Commerce Analytics: Analyze sales data to generate region-wise insights and product evaluation metrics.

Elective Courses:

Elective 1: Snowflake SnowPro Core Certification

Learn Snowflake fundamentals, including architecture, security, SQL support, caching, query optimization, data loading, and semi-structured data handling. Gain hands-on experience in connecting to Snowflake, managing data, scaling virtual warehouses, controlling access, and optimizing costs for efficient data management and analysis.

Elective 2: Google Cloud Platform Fundamentals

Learn to analyze and deploy GCP infrastructure, including networks, storage, and application services. Gain hands-on knowledge of IAM, networking, cloud storage, and explore the flexible infrastructure and platform services offered by Google Cloud.

Elective 3: Version Control with Git

Learn Source Code Management (SCM) with Git and GitHub. Gain hands-on experience with Git commands, GitHub features, and workflows like forking, branching, and merging. Develop the skills to efficiently manage and collaborate on code repositories in real-world projects.

Elective 4: Generative AI Masterclass

Gain a comprehensive understanding of generative AI models, with a focus on ChatGPT. Learn prompt engineering, explainable AI, conversational AI, and practical applications of large language models (LLMs).

FREQUENTLY ASKED QUESTIONS

How is the program delivered?

The course is delivered entirely online through live virtual classes, offering an 80:20 blend of experiential training and theoretical learning. You'll engage in hands-on projects, case studies, and interactive sessions led by industry experts.

How is the class schedule looks like? Is there recordings?

The course typically spans about 7 months with an estimated 5–8 hours of weekly weekend live sessions with a variety of schedules. In between courses, there will be a lot of hands-on project to complete. Please email us to get the details of the schedule of the program. If you miss a class, you can always watch the recording.

NOTE:

Attendance cannot be marked by simply watching the session recordings. Attendance is recorded only when a learner joins the live session. Since these are university-affiliated programs, the criteria are more stringent, as they are set by the universities themselves. However recordings will be available . Learners can view the specific certificate criteria for each course directly on their LMS

Can I work full-time while enrolled in this program?

Yes, you can! The program schedule is designed to help busy professionals with full-time work. You can attend live instructor-led sessions which are mostly held on weekends at the designated time according to your schedule and then complete assignments/projects during your free time.

What is Data Engineering?

Data engineering focuses on the practical side of data science, designing and building systems to collect, process, and analyze raw data from multiple sources. Data engineers create data warehouses, models, and pipelines, transforming raw data into actionable insights to support better business decisions.

What a Data Engineer Does?

Data engineers design and maintain systems that make organizational data accessible and actionable. Their key responsibilities include:

• Collecting large datasets from various sources
• Cleaning and validating data to ensure accuracy
• Removing duplicates and inconsistencies
• Transforming data into usable formats
• Analyzing and interpreting data to support informed business decisions

Benefits of the Data Engineering Certificate Course

This Purdue University Online program equips you with practical skills in data engineering, covering data warehousing, lakes, and pipelines to build scalable architectures. Key benefits include:

• Certificate from Purdue University Online
• Curriculum aligned with Microsoft, AWS, and Snowflake certifications
• Access to Purdue Alumni Association membership
• Live sessions on cutting-edge AI topics, including generative AI and explainable AI

Instructors for the Data Engineering Certificate Program

The program is taught by seasoned industry professionals with deep expertise in data engineering. Instructors are carefully vetted based on their knowledge, teaching experience, credentials, and proven ability to deliver practical, real-world insights for an effective learning experience.

Career Paths After Completing the Data Engineering Course

Completing this program opens multiple opportunities in the data domain:

• Data Engineer – Design, build, and maintain scalable data pipelines and applications.
• Big Data Engineer – Work with technologies like Hadoop and Kafka to handle large-scale data processing.
• Data Architect – Design and manage an organization’s data architecture, ensuring security and integrity.
• Data Analyst – Analyze complex datasets to provide actionable business insights.
• Business Intelligence (BI) Developer – Develop and manage BI solutions using dashboards and reports.

Do Data Engineers require prior coding experience?

Yes, data engineers typically must have basic programming knowledge in languages like Python, Java, or R to build and manage data pipelines effectively.

Can I apply for this data engineering course with no technical background?

Yes, you can enroll in this data engineering course without a technical background, though having basic programming knowledge and around two years of work experience is recommended.