How does big data differ from the data we use in our lives

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Question: A) How does big data differ from the data we use in our lives?

Big data sets itself apart from data through its characteristics of volume, velocity, variety and complexity-commonly referred to as the three Vs. Unlike data big data involves amounts that can reach petabytes or exabytes necessitating specialized tools, for processing and analysis. The speed at which big data is generated and processed is much faster especially when it comes to real time analytics. Furthermore big data encompasses a range of types including structured, structured and unstructured formats making it more diverse than traditional data. The complexity of data arises from its sources and formats requiring sophisticated analytics tools for integration and analysis.

Question B) Can you provide information about two websites that offer access to large datasets?

Kaggle; Kaggle is a platform that hosts an array of datasets while also facilitating competitions focused on data science. It provides access to datasets, across domains supporting research endeavors as well as fostering learning opportunities and collaborative projects (Yang et al., 2017).

UCI Machine Learning Repository; The UCI Machine Learning Repository operated by the University of California Irvine offers a collection of datasets specifically tailored for machine learning research purposes.
These datasets cover a range of domains. Hold significant value for exploring the applications of big data, in machine learning (Yang et al., 2017).

Question C) Assessing why utilizing this data is more challenging compared to data in Relational Database Management Systems (RDBMS).

Incorporating data into Relational Database Management Systems (RDBMS) presents challenges due to the disparities between structured and unstructured data;

1. Schema Flexibility; Big data often lacks a predefined structure, which poses difficulties when fitting it into the schema of RDBMS. As data evolves it necessitates adaptable data models that traditional databases do not adequately support.

2. Scalability; RDBMS may face challenges in scaling to handle the immense volumes of big data. Specialized technologies, like Hadoop and Spark are specifically designed for scaling across distributed computing environments ensuring processing of large datasets (Yang et al., 2017).

3. Processing Speed; The high velocity at which big data arrives demands processing capabilities beyond what traditional RDBMS can offer. Big data technologies are optimized for processing enabling analysis of vast datasets (Yang et al., 2017).

4. Data Variety; The presence of data formats, in the realm of data encompassing unstructured and semi structured data poses difficulties for traditional relational databases primarily designed for structured data. Non relational databases, specifically tailored to handle types of data are better equipped to manage the variety encountered in data environments (Yang et al., 2017).

In summary the unique characteristics of data combined with the limitations of databases optimized for structured data make it more challenging to utilize big data effectively. To ensure the extraction of insights, specialized tools and technologies are required for processing and analyzing data.

Reference no: EM133627023

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