About This Program
The high demand of data engineering, both locally and internationally, has prompted atomcamp to launch its data engineering bootcamp.
In our 12-week Data Engineering bootcamp, you’ll go from mastering the basics like data pipelines and ETL, to specialized skills in Big Data and cloud computing. Each week tackles a critical aspect of data engineering, offering hands-on exercises in tools like Hadoop, Apache Kafka, and AWS. The course culminates in a capstone project, ensuring you’re fully equipped to launch a global career in creating and managing sophisticated data architectures.
Skills You'll Gain
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Data streaming
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Real time analytics
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Cloud computing
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Data governance
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Automation
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Data visualisation
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Data warehouse
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Dimensional modelling
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Big data
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Sql
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Nosql
What You’ll Learn!
Proficiency in Data Pipelines and ETL
Gain a solid understanding of how to build and manage efficient data pipelines, including Extract, Transform, Load (ETL) processes essential for data flow.
Big Data and Distributed Computing Skills
Acquire hands-on experience with Big Data technologies like Hadoop and NoSQL databases, preparing you for large-scale data engineering projects.
Mastery in Data Warehousing and Real-Time Analytics
Learn the architectural and operational nuances of data warehousing technologies and become proficient in real-time data analytics using tools like Apache Kafka.
Cloud-Based Data Engineering
Get well-versed in using cloud platforms like AWS, Azure, or GCP for scalable and efficient data storage and processing solutions.
End-to-End Project Execution
Complete a capstone project that integrates all the core data engineering components, validating your ability to build a comprehensive data architecture from scratch.
Is This Program a Good Fit for You?
This bootcamp in data engineering is tailored for individuals with some understanding or professional experience in data-related or software engineering roles, aiming to transition into data engineering.
Prerequisites
- Professional experience in analytical roles using SQL or in software engineering with Python, Java, or C++
- Alternatively, a bachelor's degree in Computer Science or a related field involving extensive programming is acceptable.
- Must have proficiency in SQL and basic Python skills.
Technical Prerequisites
- Stable Internet connection
Key Features
Project Based Learning
Apply theory to real-world projects for practical proficiency.
Expert Instructors
Learn from industry professionals to gain practical insights.
Industry Ready Portfolio
Craft a portfolio that showcases your industry-ready data science projects.
Guidance & Support
Receive guidance and assistance in your pursuit of data science career opportunities. Skilled teaching assistants are there for you.
Speaker Sessions
Regular speaker series with industry professionals and hiring manager
1-on-1 Coaching & Mentorship
Receive individualized mentorship to navigate your way through your data science career.
Trusted by Leading Companies
Course Content
Introduction to Data Engineering
Data Modeling and Database Design
Big Data Technologies
Data Integration and Transformation
Data Warehousing and Dimensional Modeling
Data Streaming and Real-Time Analytics
Cloud Computing and Data Engineering
Data Governance and Security
Workflow Orchestration and Automation
Data Visualization and Reporting
Performance Optimization and Scalability
Project Work and Final Assessment
Introduction to Data Engineering
Step 1: Overview of data engineering and its role in the data lifecycle
Step 2: Understanding data pipelines and ETL (Extract, Transform, Load) processes
Step 3: Introduction to data modeling and DI (Data Integration) tools
Step 4: Setting up the development environment and tools for data engineering
Data Modeling and Database Design
Step 1: Concepts of data modeling and schema design
Step 2: Relational database management systems (RDBMS) and SQL
Step 3: Introduction to scoped source systems and identifying key business functions
Step 4: Hands-on exercises on designing and creating data models
Big Data Technologies
Step 1: Introduction to Big Data technologies and distributed computing
Step 2: Overview of Hadoop ecosystem (HDFS, MapReduce, Hive, Pig)
Step 3: Introduction to NoSQL databases for handling large-scale data
Step 4: Hands-on exercises on working with big data technologies
Data Integration and Transformation
Data Warehousing and Dimensional Modeling
Data Streaming and Real-Time Analytics
Cloud Computing and Data Engineering
Step 1: Overview of cloud computing platforms (e.g., AWS, Azure, GCP)
Data Governance and Security
Step 1: Introduction to data governance and its importance in data engineering
Workflow Orchestration and Automation
Step 1: Workflow orchestration tools (e.g., Apache Airflow, Luigi)
Data Visualization and Reporting
Step 1: Principles of data visualization and best practices
Performance Optimization and Scalability
Step 1: Techniques for optimizing data processing performance
Project Work and Final Assessment
Step 1: Students work on a data engineering project incorporating all the concepts learned
Course Outcome
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Focus on hands-on exercises and practical projects for real-world experience
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Use of up-to-date tools and technologies to reinforce learning
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Comprehensive training to become proficient in designing and managing data pipelines
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Skills in building and operating data warehouses
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Learn to set up and run real-time analytics systems
Trainers
Umer Qadri is a Senior Data Analyst at Kyndryl. He has been working at esteemed organizations such as IBM and has mastered the art of data modeling and data analysis. Currently Umer continues to shine, working on cutting-edge data engineering practices such as CI/CD pipelines, Docker, Kubernetes, extension services, and Git-Actions.
Afroz is the head Of Data Governance at Alibaba Group. He is responsible for strategizing/optimizing Engineering & Visuals. He has mastered the skills of Extract, Transform, Load (ETL) as well as Data Warehousing techniques in the course of his profession
Sidra Cheema professionally belongs to the technology industry, having worked for over 7+ years in consulting and solutioning which included data analysis and data science