Podcast
Questions and Answers
Which characteristic is NOT typically associated with big data?
Which characteristic is NOT typically associated with big data?
- Validity (correct)
- Veracity
- Velocity
- Volume
- Variety
What distinguishes big data from traditional data management systems?
What distinguishes big data from traditional data management systems?
- Big data can be stored, processed and analyzed by traditional systems due to the smaller size
- Big data is limited to structured data, while traditional systems handle unstructured data
- Big data often cannot be stored, processed, and analyzed by traditional systems due to its volume, velocity, and variety (correct)
- Big data is solely concerned with financial transactions, while traditional systems handle other data types
Which of the following best describes unstructured data in the context of big data?
Which of the following best describes unstructured data in the context of big data?
- Data with a pre-defined model and format, such as XML files
- Data formatted with effort and software tools allowing for easier analysis
- Data organized in a relational database with a defined schema and format
- Data with no inherent structure, typically stored as different types of files such as text documents, images, and videos (correct)
Which of the following is the most accurate definition of 'Veracity' in the context of Big Data?
Which of the following is the most accurate definition of 'Veracity' in the context of Big Data?
How do 'Volume' and 'Velocity' interact in defining Big Data challenges?
How do 'Volume' and 'Velocity' interact in defining Big Data challenges?
In the context of big data, what does 'Variety' refer to?
In the context of big data, what does 'Variety' refer to?
How does the increase in data volume impact the need for new data management architectures?
How does the increase in data volume impact the need for new data management architectures?
Which of the following represents a key difference between Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP)?
Which of the following represents a key difference between Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP)?
How does 'Velocity' in big data relate to real-time analytics and decision-making?
How does 'Velocity' in big data relate to real-time analytics and decision-making?
What is the significance of 'scalability' in the context of Big Data technologies?
What is the significance of 'scalability' in the context of Big Data technologies?
How have advancements in digital technology contributed to the rapid growth of data?
How have advancements in digital technology contributed to the rapid growth of data?
Which of these scenarios best illustrates the application of 'velocity' as a characteristic of Big Data?
Which of these scenarios best illustrates the application of 'velocity' as a characteristic of Big Data?
What is the impact of a delay in data processing in businesses that rely on real-time analytics?
What is the impact of a delay in data processing in businesses that rely on real-time analytics?
Why is it important for modern systems to manage, analyze, summarize, visualize, and discover knowledge from collected data in a timely manner?
Why is it important for modern systems to manage, analyze, summarize, visualize, and discover knowledge from collected data in a timely manner?
How does the concept of 'data diversity' relate to the challenges of big data analytics?
How does the concept of 'data diversity' relate to the challenges of big data analytics?
Which of the following statements accurately describes one of the key shifts in how data is generated and consumed?
Which of the following statements accurately describes one of the key shifts in how data is generated and consumed?
For a marketing team in a retail company, what outcome would BEST represent the successful application of Big Data 'velocity'?
For a marketing team in a retail company, what outcome would BEST represent the successful application of Big Data 'velocity'?
Why is preventing fraud as it is occurring a good example of real-time analytics?
Why is preventing fraud as it is occurring a good example of real-time analytics?
How do optimization and predictive analytics contribute to the value of Big Data?
How do optimization and predictive analytics contribute to the value of Big Data?
What factor has changed which now drives Big Data?
What factor has changed which now drives Big Data?
Flashcards
What is Big Data?
What is Big Data?
Extremely large and diverse collections of structured, unstructured, and semi-structured data that grows exponentially.
What makes data 'Big'?
What makes data 'Big'?
The scale, diversity, and complexity of data requires new architecture and techniques to extract value.
Structured Data
Structured Data
Data that has a predefined data model, format, and structure.
Unstructured Data
Unstructured Data
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Semi-Structured Data
Semi-Structured Data
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Big Data: Volume
Big Data: Volume
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Big Data: Velocity
Big Data: Velocity
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Big Data: Variety
Big Data: Variety
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Big Data: Veracity
Big Data: Veracity
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Big Data: Value
Big Data: Value
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Big Data: Variability
Big Data: Variability
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OLTP
OLTP
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OLAP
OLAP
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RTAP
RTAP
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Study Notes
- Big data refers to extremely large, diverse collections of structured, unstructured, and semi-structured data
- These datasets grow exponentially and are too complex for traditional data management systems to store, process, and analyze.
- Digital technology advancements are spurring rapid growth in data amount and availability
- These advancements including connectivity, mobility, IoT (Internet of Things), and AI (Artificial Intelligence).
- Companies are using new big data tools to collect, process, and analyze data quickly to gain the most value.
- There is no single standard definition for Big Data
- "Big Data" is data that requires new architecture, techniques, algorithms, and analytics because of its scale, diversity, and complexity. The goal is to manage it, extract value, and uncover hidden knowledge.
Types of Data
- Unstructured data has no inherent structure and is stored in various file types (e.g., documents, PDFs, images, videos).
- Quasi-Structured data features erratic formats that require effort and software tools to format properly (e.g., Clickstream data).
- Semi-Structured data refers to textual data files that have an apparent pattern, which enables simple analysis (e.g., Spreadsheets and XML files).
- Structured data has a well-defined data model, format, and structure (e.g., Databases).
Characteristics of Big Data: Volume (Scale)
- Data volume increased 44x from 2009 to 2020
- Data volume went from 0.8 zettabytes to 35zb
- Data volume is increasing exponentially
Characteristics of Big Data: Variety (Complexity)
- Big data encompasses relational data (tables, transaction/legacy data), text data (web), semi-structured data (XML), and graph data (social networks, Semantic Web (RDF)).
- Streaming data (stream vs. static): data can only be scanned once.
- A single application can generate/collect many types of data.
- Big public data includes online, weather, and financial data.
- All types of data need linking together to extract knowledge.
Characteristics of Big Data: Velocity (Speed)
- Data is generated and needs processing fast
- Online Data Analytics
- Late decisions have missing opportunities
- Examples being E-Promotions when user location and search history are used to send promotions and Healthcare monitoring where sensor data will need immediate reaction
Real-time/Fast Data
- Social media and networks (all users generate data)
- Scientific instruments (collect all data types)
- Mobile devices (track objects all the time)
- Sensor networks (measure all data types)
- Progress is no longer hindered by collecting data, rather by managing, analyzing, summarizing, visualizing, and discovering knowledge in a timely, scalable manner.
Real-Time Analytics/Decision Requirement examples
- Product Recommendations need to be relevant and compelling
- Learning user behaviour why they switch to competitors
- Friend invitations to join games
- Improving marketing effectiveness for promotions
- Preventing fraud
Big Data Considerations (The Vs)
- Volume: Massive data volumes present challenges in storage and analysis.
- Velocity: Rapidly changing data requires real-time analysis.
- Variety: Diverse data from numerous sources requires integration and analysis.
- Variability: Constantly changing data meaning requires gathering and interpreting.
- Veracity: Varying data quality and reliability requires transformation and trust.
- Value: Cost-effectiveness and business value are crucial.
Harnessing Big Data
- OLTP (Online Transaction Processing): DBMSs
- OLAP (Online Analytical Processing): Data Warehousing
- RTAP (Real-Time Analytics Processing): Big Data Architecture & Technology
Big Data: Generating/Consuming Data
- Old Model: Few companies generate data, while others consume it.
- New Model: Everyone generates and consumes data.
What Drives Big Data
- Optimizations and predictive analytics
- Complex statistical analysis
- Various data types and numerous sources
- Large datasets
- Real-time processing
The Bottleneck
- The bottleneck is in technology
- New architecture, algorithms, and techniques are needed
- Requires technical skills of experts in new technology and dealing with big data
Topics Overview
- Cloud Computing
- Data Modeling
- Data Warehouse
- Dimensional Data Modeling
- ETL
- The Power of Spark
- Big Data Processing Pipeline
- Data Wrangling with Spark
- Postgres with Python
- ETL with Python
- Framework for Big Data (Python Spark)
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