Big Data refers to massive, complex datasets that require advanced technologies and methods to process, analyze, and extract insights. It’s commonly used in sectors like healthcare, finance, marketing, and more.

The 3 Vs are Volume (amount of data), Velocity (speed of data creation), and Variety (different data forms). Some add two more: Veracity (data accuracy) and Value (insight potential).

Big Data is collected from various sources like social media, IoT sensors, transactions, GPS, and logs from devices and systems.

Big Data provides insights that help organizations improve decision-making, customer experience, operational efficiency, and can drive innovation.

Tools include Hadoop, Apache Spark, Apache Kafka, NoSQL databases, and visualization tools like Tableau and Power BI.

Challenges include data storage, data integration, processing speed, data quality, privacy concerns, and skilled workforce requirements.

Big Data is stored in distributed storage systems like Hadoop Distributed File System (HDFS), NoSQL databases, and cloud storage solutions.

Hadoop is an open-source framework that allows distributed storage and processing of large data sets across

An AWS Data Engineer focuses on building and maintaining data infrastructure, including data lakes, ETL pipelines, and big data processing systems. They ensure the data is available, reliable, and optimized for analysis. On the other hand, a Data Scientist uses the data prepared by Data Engineers to build statistical models, perform advanced analytics, and derive insights using machine learning.

The demand for AWS Data Engineers is growing due to the increasing adoption of cloud-based solutions. Career prospects include roles such as:

• Cloud Data Engineer
• Big Data Engineer
• Data Architect
• ETL Developer
• Cloud Solutions Architect

NoSQL databases are designed to store and manage unstructured or semi-structured data, which is common in Big Data. They support scalability and flexibility needed for handling large datasets.