Big Data and Data Mining - Glossary

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 The glossary provides definitions for key terms related to Big Data and Data Mining. Here are some of the terms covered:

  1. Analytics:

    • The process of examining data to draw conclusions and make informed decisions through statistical analysis and data-driven insights.

  2. Big Data:

    • Vast amounts of structured, semi-structured, and unstructured data characterized by volume, velocity, variety, and value, offering competitive advantages when analyzed.

  3. Big Data Cluster:

    • A distributed computing environment with thousands or tens of thousands of interconnected computers collectively storing and processing large datasets.

  4. Cloud Computing:

    • The delivery of on-demand computing resources over the Internet, including networks, servers, storage, applications, and data centers, on a pay-for-use basis.

  5. Data Science:

    • An interdisciplinary field involving extracting insights and knowledge from data using various techniques such as programming, statistics, and analytical tools.

  6. Hadoop:

    • A distributed storage and processing framework used for handling and analyzing large datasets, particularly suitable for big data analytics.

  7. Hadoop Distributed File System (HDFS):

    • A storage system within the Hadoop framework that partitions and distributes files across multiple nodes, facilitating parallel data access and fault tolerance.

  8. Infrastructure as a Service (IaaS):

    • A cloud service model providing access to computing infrastructure, including servers, storage, and networking, without users managing them.

  9. Map Process:

    • The initial step in Hadoop’s MapReduce programming model, where data is processed in parallel on individual cluster nodes, often used for data transformation tasks.

  10. Reduce Process:

    • The second step in Hadoop's MapReduce model where results from the mapping process are aggregated and processed further to produce the final output, typically used for analysis.

  11. V's of Big Data:

    • Characteristics common across Big Data definitions, including Velocity, Volume, Variety, Veracity, and Value, highlighting the rapid generation, scale, diversity, quality, and value of data.

The glossary is a valuable resource for individuals working in the industry, participating in user groups, and engaging in certificate programs related to Big Data and Data Mining.

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