Week 9-BigdataDataLakeDataWarehose

Week 9: Bigdata, Data Lake and Data Warehouse Quality vs. Quantity in Big Data: Primary Question: How does the sheer volume of Big Data impact the quality of insights one can derive from it? Answer:  Definition : The volume of Big Data refers to the massive amount of data generated from various sources. Quality, in this context, means the accuracy, relevance, and reliability of the insights drawn from analyzing this data.  Detailed Explanation : o Positive Impact : Big Data allows for more comprehensive analyses, capturing larger trends and patterns that smaller datasets might miss. This comprehensive coverage can improve the reliability of insights. o Negative Impact : However, the larger the dataset, the higher the likelihood of "noise," or irrelevant, misleading, or erroneous data. This noise can distort insights and require additional resources for data cleaning and analysis.  Real-world Examples : o Positive : Netflix uses Big Data to generate highly personalized recommendations, improving user engagement. o Negative : During election polling, too much data from unverified or biased sources can lead to incorrect predictions. Additional Chain-of-Thought Prompts:  What is "noise" in Big Data?  Discuss techniques to filter out noise.  Explore the trade-offs between having too much data and not having enough. Data Lakes in Cloud Computing: Primary Question: How has cloud computing influenced the adoption and functionality of data lakes? Answer:  Definition : Data lakes are storage repositories that hold a large amount of raw data in its native format. Cloud computing refers to the delivery of computing services over the internet.  Detailed Explanation : o Positive Impact : Cloud computing has made it easier to scale data lakes, adapting to the storage needs of an organization. This scalability enhances data accessibility and sharing across different departments. o Negative Impact : However, scalability can also lead to increased costs, especially if not managed efficiently.  Real-world Examples : o Positive : Healthcare organizations use cloud-based data lakes for real-time analytics in patient care. o Negative : Poorly managed data lakes in the cloud can lead to "data swamps," making data retrieval slow and costly.