Data Science MCQ

Master Big Data with Hadoop, Spark, Kafka & Cloud | Build Real-World Projects & Scalable Data Pipelines from Scratch

Created by: Krish Naik
Chief AI Engineer

Created by: Mayank Aggarwal
Educator

Created by: KRISHAI Technologies Private Limited
Artificial intelligence and machine learning engineer

What Will I Learn?

• Learn Hadoop, Spark, and Kafka from scratch, understanding the 3Vs (Volume, Velocity, Variety) and their real-world applications.
• Master ETL workflows, data ingestion, transformation, and storage using Apache Spark, Airflow,Kafka, and distributed systems.
• Deploy & manage Big Data solutions on Azure, and GCP
• Work on real-world Big Data projects, implementing scalable architectures, data pipelines, and analytics using industry tools.

Requirements

• Basic Computer Knowledge – No prior experience in Big Data is needed, but familiarity with using a computer and basic software is helpful.
• Basic Python or SQL (Optional) – While not mandatory, having a basic understanding of Python or SQL can make learning data processing easier.
• Willingness to Learn – A strong motivation to explore Big Data technologies and work with large-scale data solutions is essential.
• A Laptop with Internet Access – Any system (Windows/Mac/Linux) with at least 8GB RAM is recommended for running Big Data tools locally or on the cloud.

Target audience

• Beginners & Freshers – Anyone new to Big Data who wants to start their career in data engineering, data science, or cloud computing.
• Software Developers – Developers looking to expand their skills into Big Data frameworks like Hadoop, Spark, and Kafka for scalable applications.
• Data Analysts & Data Scientists – Professionals who want to work with large datasets, ETL pipelines, and real-time processing in Big Data environments.
• Cloud & DevOps Engineers – Engineers who want to learn how to deploy and manage Big Data solutions on Azure, and GCP.
• Experienced Professionals – IT professionals looking to transition into Big Data Engineering and work on real-world projects with cutting-edge tools.