Data Streaming Overview and Class Rules
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Questions and Answers

What is a key component of event-driven architecture in IoT streaming?

  • Users manually inputting data into the system
  • Producers generating events from IoT devices (correct)
  • Pre-determined static data sets
  • Batch processors handling large sets of data
  • Which of the following accurately describes the difference between static data computation and streaming data computation?

  • Static data involves fixed questions regarding unchanging data. (correct)
  • Static data computation processes continuous data streams.
  • Streaming data requires batch processing techniques.
  • Streaming data computation asks questions of dynamic data.
  • One of the challenges of streaming data management is that:

  • There are multiple opportunities to analyze the same data set.
  • Streaming data is always low in volume and easy to handle.
  • Data can be processed at any point in time without urgency.
  • Changes in data require alerts on recent developments. (correct)
  • What is a challenge specifically associated with Digital Signal Processing (DSP) in streaming?

    <p>Design and validation of stream processing algorithms (B)</p> Signup and view all the answers

    Recency matters in streaming data analytics because:

    <p>Timeliness of data affects the effectiveness of alerts. (D)</p> Signup and view all the answers

    Which of the following is NOT an example of a streaming data source?

    <p>Batch processed data (D)</p> Signup and view all the answers

    What is a characteristic of streaming data?

    <p>It flows continuously without interruptions. (A)</p> Signup and view all the answers

    What is a primary benefit of real-time decision-making in IoT?

    <p>It allows for immediate processing of time-sensitive data. (A)</p> Signup and view all the answers

    Which of the following is a challenge associated with IoT data streaming?

    <p>Volume and variety of generated data. (A)</p> Signup and view all the answers

    What is a key requirement for IoT data in terms of network connections?

    <p>Low latency for real-time processing (B)</p> Signup and view all the answers

    What opportunity does the untapped IoT sensor data represent?

    <p>Possibility for prediction and optimization. (D)</p> Signup and view all the answers

    What characterizes unbounded data in data streams?

    <p>It is an ever-growing stream that needs continuous processing (C)</p> Signup and view all the answers

    Which model controls the production and processing of data in a push model?

    <p>The source dictates data production and flow (B)</p> Signup and view all the answers

    What differentiates streaming analytics from traditional analytics?

    <p>Streaming analytics acts on data as it is produced. (C)</p> Signup and view all the answers

    What aspect of time is important when processing data streams?

    <p>Both ingestion and processing times should be considered (C)</p> Signup and view all the answers

    Why is scalability a crucial concern in IoT networks?

    <p>The volume of streaming data is vast and growing. (C)</p> Signup and view all the answers

    Which term describes the practice of processing data as it is produced?

    <p>Streaming analytics. (D)</p> Signup and view all the answers

    How is data traditionally moved prior to the advent of data streaming?

    <p>In large batches with long latency (A)</p> Signup and view all the answers

    What does the acronym ETL stand for in the context of data management?

    <p>Extract, Transform, Load. (A)</p> Signup and view all the answers

    What is a significant limitation of batch processing in relation to streaming data?

    <p>Data may become stale by the time it is processed (D)</p> Signup and view all the answers

    Which of the following is NOT a benefit of data streaming?

    <p>Requires periodic processing (A)</p> Signup and view all the answers

    What is one consequence of not utilizing sensor data effectively in manufacturing?

    <p>Failure to detect operational conditions. (B)</p> Signup and view all the answers

    Which of the following best describes the nature of data in IoT?

    <p>The data is diverse and generated in large volumes. (D)</p> Signup and view all the answers

    Which of the following statements best describes the concept of the data value continuum?

    <p>Value changes from individual items to aggregates over time (D)</p> Signup and view all the answers

    What technique is commonly used for data streaming?

    <p>Data stream processing for on-the-fly analytics (D)</p> Signup and view all the answers

    What processing method is ideal for handling IoT data streams?

    <p>Real-time aggregation and filtering (D)</p> Signup and view all the answers

    What is the primary feature of IoT devices as described?

    <p>They are interconnected and communicate with each other. (B)</p> Signup and view all the answers

    In which application would IoT data be most relevant?

    <p>Real-time GPS tracking in connected cars. (B)</p> Signup and view all the answers

    What kind of data do wearables and remote health monitoring devices produce?

    <p>Real-time data for immediate health assessments. (A)</p> Signup and view all the answers

    Which sector benefits from IoT through real-time monitoring of traffic systems?

    <p>Transportation (D)</p> Signup and view all the answers

    What does 'data streaming' refer to in the context of IoT?

    <p>The transmission of data continuously and in real-time. (A)</p> Signup and view all the answers

    Which of the following describes a disadvantage of IoT technology?

    <p>Potential security vulnerabilities due to interconnected devices. (C)</p> Signup and view all the answers

    How do smart cities utilize IoT technologies?

    <p>By connecting various systems for data communication. (D)</p> Signup and view all the answers

    Which aspect is critical to the functioning of IoT in healthcare?

    <p>Streaming real-time data from health monitoring devices. (D)</p> Signup and view all the answers

    What is a common feature of connected devices in smart homes?

    <p>Ability to communicate and share data with each other. (C)</p> Signup and view all the answers

    What role does data processing play in the context of IoT?

    <p>Data processing occurs in real-time to provide immediate insights. (B)</p> Signup and view all the answers

    Flashcards

    Internet of Things (IoT)

    A network of interconnected devices that communicate with each other, generating data streams.

    Data Streaming

    Data that is generated and transmitted continuously in real-time.

    Connected Devices

    Devices that collect and transmit data in real-time, such as GPS systems, sensors, and wearables.

    IoT's Role in Data Streaming

    The use of interconnected devices to collect and analyze data in real-time, helping optimize operations and improve decision-making.

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    Connected Car Data Streaming

    Collecting and analyzing data from connected cars for purposes like traffic management, route optimization, and safety features.

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    Healthcare Data Streaming

    Using wearables and remote health monitoring devices to collect and analyze health data in real-time for diagnosis, treatment, and preventative care.

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    What is streaming data?

    Data that flows continuously, with no defined start or end, like data from sensors or cameras.

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    Where does streaming data come from?

    Streaming data is generated from sources like sensors, cameras, and internet-connected devices (IoT).

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    How is streaming data sent?

    Streaming data sources send data in small chunks as it's created, often in kilobytes.

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    What is data streaming?

    Data streaming involves processing and analyzing data as it arrives, without storing it all first.

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    What is the attendance policy?

    The university's attendance policy requires students to have at least 80% attendance to be eligible to take the final exam.

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    How is the course assessed?

    The course assessment is divided into a final exam (50%), midterm (20%), quizzes (10%), and a project (20%).

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    What is the project requirement?

    The project requires a report and presentation, with 2-3 students in each group.

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    What is the policy on cheating and plagiarism?

    Cheating and plagiarism will result in zero marks on any assignment.

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    Event-Driven Architecture in IoT Streaming

    A system where applications respond to events (like sensor readings) in real-time, allowing for immediate action.

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    Streaming Data Computation

    Focuses on continuously evaluating incoming data streams with pre-defined questions, unlike static data analysis which deals with fixed datasets.

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    Batch vs. Real-time Processing

    Data is processed in batches, typically offline, after collection, while real-time processing analyzes data as it arrives.

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    Streaming Data Management

    The challenge of ensuring data is handled effectively in a streaming environment, considering factors like data volume, velocity, and single-chance examination.

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    DSP (Data Streaming Platform) Challenges

    Streaming architecture and pipelines need to be designed, implemented, and managed to address challenges like scaling, data ingestion, processing algorithms, and load variations.

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    Real-Time Decision-Making in IoT

    The use of real-time data processing for immediate decision-making, crucial for applications like medical alerts and security systems.

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    Scalability Challenges in IoT Data Streaming

    The challenge of processing massive amounts of data flowing from IoT devices, requiring scalable solutions for efficient analysis.

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    Unused IoT Data

    The majority of data collected by IoT sensors remains unused, representing a significant opportunity for optimization and prediction.

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    Traditional Analytics at Rest

    Traditional data analytics processes where data is collected, stored, processed, and then analyzed, often resulting in delays.

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    Streaming Analytics

    A modern approach to data analysis where data is processed in real-time as it is generated, allowing for immediate insights and actions.

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    Volume and Variety in IoT Data Streaming

    The inherent complexity of managing a large volume and variety of data from multiple sources, like cameras and environmental sensors.

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    Data Stream

    A data source that continuously generates and transmits data, often at a high frequency.

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    ETL in Data Analytics

    The process of extracting, transforming, and loading data from various sources into a central repository for analysis.

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    Alerts and Reports in Data Analytics

    The act of using data to generate alerts or reports, providing information for decision-making.

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    Latency

    The time it takes for data to travel from its source to its destination and be processed.

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    Decisioning in Data Analytics

    The application of insights gained from data analysis to make informed decisions and improve outcomes.

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    Bandwidth

    The rate at which data is transferred over a network connection, measured in bits per second (bps).

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    Data Enrichment

    The process of making the most of available data by enriching it with additional information, enhancing its value and utility.

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    Data Streaming Processing

    Data is processed as it arrives, without being stored in batches. Ideal for real-time analysis and applications.

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    Push Model

    The data source controls when data is produced and sent to the processing system.

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    Concept of Time in Data Streams

    Data is considered to have a timeline, with different values and interpretations depending on when it was generated.

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    Time Series Analysis

    A significant benefit of data streaming processing, allowing for dynamic analysis of data patterns over time.

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    Security and Privacy in IoT

    The process of safeguarding data, especially sensitive information, as it moves from devices to processing systems.

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    Data Value Continuum

    The value of data can evolve over time, becoming more insightful as it accumulates and is combined with other data points.

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    Real-Time Data Analytics

    The process of handling and processing data streams to extract insights, patterns, and trends in real-time.

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    Study Notes

    Data Streaming Overview

    • Data streaming is a continuous flow of data, without a defined beginning or end.
    • Common data sources include sensors, cameras, and IoT devices.
    • Examples of data streaming scenarios are connected cars, healthcare, and smart cities.
    • Data is often generated in small sizes (kilobytes), as it's produced.
    • Data needs to be processed on the fly.

    Class Rules

    • Students are allowed to do anything except make noises (e.g., chatting, singing).
    • Students can ask questions during the lecture.
    • Taking attendance is required according to university policy.
    • 80% attendance is required to sit for the final exam.

    Course Assessment

    • Course assessment is temporary and subject to change.
    • Final exam counts for 50% of the grade.
    • Midterm exam counts for 20% of the grade.
    • Quizzes count for 10% of the grade.
    • Projects count for 20%. Students will be in 2-3 member groups and need to complete a report and presentation.
    • Cheating and plagiarism will result in zero marks.

    Data Challenges

    • Continuous data flow from diverse sources (IoT devices, cameras, sensors).
    • Data demands real-time processing, requiring high-speed networks.
    • Security and privacy concerns need to be addressed.

    IoT and Its Role in Data Streaming

    • IoT devices communicate with each other, generating data.
    • Examples include connected cars, healthcare (wearables), and smart cities (surveillance and traffic monitoring).
    • Real-time data processing is crucial in IoT applications.

    Importance of Data Streaming in IoT

    • Real-time decision-making is essential (e.g., medical alerts, security systems).
    • IoT generates massive streaming data. Scalable processing is needed.

    Most IoT Data Remains Unused

    • Data from sensors can detect conditions requiring attention.
    • A lot of sensor data remains largely unused for prediction/optimization.
    • Opportunities exist to process data as it is produced.

    Traditional vs. Streaming Analytics

    • Traditional analytics processes data at rest, stored in a database.
    • Streaming analytics processes data as it flows, immediately acting on fresh input.

    Challenges in IoT Data Streaming

    • High volume and variety of data from devices (sensors/cameras).
    • Processing needs to be fast, requiring low latency and high bandwidth.
    • Security is important, especially with sensitive data.

    Characteristics of Data Streams

    • Data streams are conceptually infinite and constantly growing.
    • Data needs to be processed in real-time or as it arrives (push or publish/subscribe model).
    • Real-time processing is critical, especially for time-sensitive data. Critical aspects are data production and processing time.

    Data Value Continuum

    • The value of data changes based on how recently it was generated.
    • Real-time data is valuable. Older data often holds less immediate value.

    Data Value Chain

    • Data values decrease with time.
    • Data is processed on the fly in streaming analysis.
    • Real-time analysis is required for timely events and decisions.

    Data Streaming

    • Data streaming processes data in real-time, as it arrives.
    • Batch processing stores and processes data in batches; Streaming processes data in real-time.
    • Cannot use batch processing for streaming data in most cases.

    Benefits of Data Streaming

    • Suitable for IoT data analysis.
    • Real-time insights from data streams.
    • Useful for monitoring, prediction and optimization strategies.

    Patterns that Drive Most Streaming Use Cases

    • Real-time data is useful for prevention, like security issues, or optimization of business functions like customer service.
    • Data is collected and observed to predict trends for future decision making.

    Event-Driven Architecture in IoT Streaming

    • Systems respond to events, such as temperature sensor readings.
    • Producers generate events from devices.
    • Consumers process the events from producers.
    • Example is Smart homes reacting to temperature.

    Static vs. Streaming

    • Static data is processed for pre-defined questions.
    • Streaming data is processed as it arrives, in near real-time

    Batch vs. Real-time Processing

    • Batch processing collects data over time, then processes it in a single step.
    • Real-time processing continuously inputs, processes and outputs data as it arrives.
    • Real-time systems are required for important applications such as customer services, ATMs, and radar systems.

    Challenges of Streaming

    • Managing large volumes of data (data management)
    • Real-time data needs to be continuously assessed (arbitrary and interactive exploration).
    • Real-time analytics is important (recency of events).
    • Data availability is a constant concern.

    Challenges of DSP

    • Managing the entire streaming architecture, pipeline and data handling.
    • Data is constantly changing and varies in volume and velocity.
    • Processing needs to be guaranteed and fault-tolerant, adapting as the data changes.
    • Edge Computing – Processing data closer to devices; reduces latency.
    • 5G Networks – Faster/More reliable connectivity for streaming data from IoT devices.
    • AI and Streaming – Real-Time AI models.

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    Description

    This quiz provides an overview of data streaming, highlighting its continuous nature and common sources such as IoT devices. Additionally, it outlines class rules and assessment criteria for students, emphasizing the importance of attendance and project collaboration.

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