1) [Explain the concept of Big Data and its significance in modern data-driven decision-making.](/1.md) 2) [List and describe the three V's commonly used to define the characteristics of Big Data. Provide examples for each characteristic.](/2.md) 3) [Trace the historical development of Big Data from its inception to the present day. Highlight three key milestones in this evolution. ](/3.md) 4) [Explain the concept of scalability in Big Data and how parallel processing contributes to achieving scalability. Provide examples to illustrate these concepts.](/4.md) 5) [Compare and contrast different data storage and analysis technologies commonly used in Big Data environments. Highlight five important factors to consider when choosing between storage and analysis options for a specific project.](/5.md) ### 6) [Describe the three main categories of analytics � descriptive, predictive, and prescriptive analytics. Provide examples for each category and explain when they are most useful in a business context.](/6.md) ### 7) [Describe the Hadoop ecosystem, focusing on the core components, and provide examples of their roles in a typical Big Data processing workflow. ](/7.md) ### 8) [Choose two Hadoop ecosystem tools, explain their specific use cases, and how they complement the core components of Hadoop. Provide real-world examples of where these tools are commonly applied.](/8.md) ### 9) [Explain the concept of clustering in data analysis. Provide an overview of two distinct clustering algorithms, and discuss their applications and differences.](9.md) ### 10) [Discuss the Apriori algorithm and its role in mining association rules. Explain the key steps involved in the Apriori algorithm, and provide a real-world example of how it can be applied to market basket analysis.](/10.md) ### 11) [Define the concept of NoSQL in the context of Big Data management. Discuss why traditional relational databases may not be well-suited for Big Data and how NoSQL databases address these limitations.](/11.md) ### 12) [Provide an introductory overview of both MongoDB and Cassandra. Explain their key features and use cases in managing and storing Big Data. ](/12.md) ### 13) [Describe the fundamental characteristics of a NoSQL data store. Compare and contrast NoSQL data stores with traditional relational databases, highlighting three key differences.](/13.md) ### 14) [Discuss common data architecture patterns used in NoSQL databases for managing Big Data. Provide examples of how these patterns are employed in real-world applications.](/14.md) ### 15) [Explain how NoSQL databases are employed in managing and analyzing Big Data. Highlight five advantages of using NoSQL databases for handling large and complex datasets.](/15.md) ### 16) [Explain the core features and use cases of MongoDB as a NoSQL database. Provide examples of scenarios where MongoDB is a suitable choice for data storage and retrieval.](/16.md) ### 17) [Explain the importance of ACID (Atomicity, Consistency, Isolation, Durability) properties in traditional relational databases. How do these properties ensure data reliability and integrity? ](/17.md)