Podcast
Questions and Answers
What is the primary goal of data mining within enterprises?
What is the primary goal of data mining within enterprises?
- To sort through data sets to identify patterns (correct)
- To create new data formats and types
- To ensure data security and integrity
- To store data in a centralized location
Which of the following best describes the complexity characteristic of Big Data?
Which of the following best describes the complexity characteristic of Big Data?
- Data is only available in numerical formats
- Data complexity decreases as volume increases
- Data must be stored in a single database system
- Data varies in formats, types, and structures (correct)
Which of the following is NOT a characteristic of Big Data?
Which of the following is NOT a characteristic of Big Data?
- Complex data structures
- Exponential increase in data volume
- Diversity in data types
- Uniform size of data sets (correct)
What significant increase in data volume occurred from 2009 to 2020?
What significant increase in data volume occurred from 2009 to 2020?
Why do enterprises invest resources in data modeling and security?
Why do enterprises invest resources in data modeling and security?
What is a primary characteristic of big data in terms of velocity?
What is a primary characteristic of big data in terms of velocity?
Which statement best describes the change in the model of generating and consuming data?
Which statement best describes the change in the model of generating and consuming data?
What technology is associated with handling real-time data analytics?
What technology is associated with handling real-time data analytics?
What primarily drives the need for big data analytics?
What primarily drives the need for big data analytics?
Which of the following sources is NOT typically associated with big data generation?
Which of the following sources is NOT typically associated with big data generation?
Flashcards
Big Data
Big Data
Data with a scale, diversity, and complexity requiring new methods for management and analysis to extract value and hidden knowledge.
Big Data Volume
Big Data Volume
Enormous amount of data generated, growing exponentially over time. A significant increase in data collection over recent years.
Big Data Variety
Big Data Variety
Data in various formats (text, numbers, images, etc.), and structures.
Enterprise Data
Enterprise Data
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Data Mining
Data Mining
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Streaming Data
Streaming Data
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Big Data Velocity
Big Data Velocity
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Real-Time Analytics
Real-Time Analytics
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OLTP
OLTP
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OLAP
OLAP
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RTAP
RTAP
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Data Sources
Data Sources
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Big Data Model Change
Big Data Model Change
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Driving Forces of Big Data
Driving Forces of Big Data
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Value of Big Data Analytics
Value of Big Data Analytics
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Study Notes
Big Data Overview
- Big data is data whose scale, diversity and complexity necessitate new architecture, techniques, algorithms and analytics to manage and extract value and hidden knowledge from it.
- Big data is characterized by the 3Vs: volume, velocity, and variety.
- Volume refers to the sheer amount of data.
- Velocity describes the speed at which data is generated and needs to be processed.
- Variety emphasizes the different formats and types of data (structured, unstructured, semi-structured).
Data Mining
- Data mining is the process of sorting through large data sets to identify patterns and relationships.
- These patterns help solve business problems through data analysis and help predict future trends, thus aiding informed business decisions.
- The process of data mining involves several steps: collection, understanding, preparation, modeling, and evaluation.
Data Warehousing and Data Streams
- Data warehousing is a system of managing very large amounts of data and extracting value from them.
- Data streams are continuous flows of data needing to be processed rapidly.
- The arrival rate of data streams makes storage capacity a challenge.
- "Real-time" processing is required for data streams, necessitating effective decision-making.
- Window models are used in data stream processing.
Data Mining Tasks
- Data mining tasks include classification, clustering, association rule discovery, sequential pattern discovery, regression, deviation detection, and collaborative filtering.
Other Types of Mining
- Text mining applies data mining to textual documents, such as clustering web pages to find related pages or classify them into a web directory.
- Graph mining deals with graph data.
Big Data and Technology
- New architecture, algorithms, and techniques are needed to handle the big data boom.
- Experts with technical skills are crucial to successfully use the new technologies and deal with big data.
- Technologies like Hadoop, Hive, Vertica, MapReduce, and others are used in big data applications.
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