Data Lakes and Data Warehouses
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Questions and Answers

What is a key benefit of accurate data labeling in machine learning?

  • Improving data variety
  • Reducing data collection needs
  • Simplifying algorithm code
  • Enhancing model accuracy (correct)

    • Which is a possible consequence of human error in data labeling?

  • Enhanced model performance
  • Improved data integrity
  • Increased data processing costs
  • Decreased quality of data (correct)

    • How can reclassifying a categorical variable as a binary variable benefit a model?

  • Makes the variable more consumable (correct)
  • Increases data redundancy
  • Reduces computational complexity
  • Eliminates the need for data cleaning

    • What is one challenge associated with data labeling?

    <p>It is prone to human coding errors</p> Signup and view all the answers

    Which of the following is a benefit of data lineage?

    <p>Tracks errors in data processes</p> Signup and view all the answers

    What does the process of data lineage include?

    <p>Recording data transformations</p> Signup and view all the answers

    Which of the following is a typical use case for data lineage?

    <p>Deprecating columns</p> Signup and view all the answers

    How does data lineage help with system migrations?

    <p>By maintaining metadata integrity</p> Signup and view all the answers

    Which of the following best describes a data lake?

    <p>A repository that stores pools of big data for advanced analytics applications</p> Signup and view all the answers

    Which characteristic is essential for a data warehouse but not necessarily for a data lake?

    <p>Highly structured and unified data</p> Signup and view all the answers

    What is the primary benefit of data discovery?

    <p>Democratization, collaboration, and improved decision-making</p> Signup and view all the answers

    In the data discovery process, what comes after establishing objectives?

    <p>Determining the data storage scope</p> Signup and view all the answers

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

    <p>Increased storage efficiency</p> Signup and view all the answers

    What is a key characteristic of quality data?

    <p>Validity, ensuring conformity to business rules</p> Signup and view all the answers

    What does the data cleaning process aim to remove?

    <p>Incorrectly formatted or incomplete data</p> Signup and view all the answers

    Why are data warehouses inefficient for streaming analytics?

    <p>They require data to be cleansed and processed sequentially</p> Signup and view all the answers

    Which of the following is NOT a challenge of data integration?

    <p>Consistent data formatting</p> Signup and view all the answers

    What does the tight-coupling approach, also known as ETL, involve?

    <p>Creating a centralized repository to store integrated data</p> Signup and view all the answers

    In the context of data integration, what does 'ETL' stand for?

    <p>Extraction, Transformation, and Loading</p> Signup and view all the answers

    What is a primary benefit of data warehousing in the tight-coupling approach?

    <p>Providing a cohesive view for analysis and decision-making</p> Signup and view all the answers

    Which issue can occur due to manual data entry errors in the example of the tight-coupling approach?

    <p>Number of aquariums shipped not matching the actual number sold</p> Signup and view all the answers

    Which approach is also known as data federation?

    <p>Loose-coupling approach</p> Signup and view all the answers

    What is one potential downside of the loose-coupling approach?

    <p>Difficulty in maintaining consistency and integrity across data sources</p> Signup and view all the answers

    Which of the following is a characteristic of the loose-coupling approach?

    <p>Integrating data at the record level</p> Signup and view all the answers

    Study Notes

    Data Lakes

    • A repository that stores large amounts of data for predictive modeling, machine learning, and advanced analytics applications.
    • Often contains raw, unprocessed data.
    • Supports native streaming, suitable for streaming analytics.
    • Everyone operates from the same data.

    Data Warehouses

    • A repository for business data, but only stores highly structured and unified data.
    • Data is cleansed and processed sequentially before storage.
    • Optimized for SQL-based access.
    • Inefficient for streaming analytics.

    Data Discovery

    • The process of applying advanced analytics to detect informative patterns in data.
    • Establishing objectives, determining data storage scope, choosing the best approach, and collecting and preparing data.
    • Benefits include a comprehensive picture of company data, democratization, collaboration, improved decision-making, better risk management, and contextual data classification.

    Data Cleaning

    • The process of fixing or removing incorrect, corrupted, duplicate, or incomplete data.
    • Characteristics of quality data include validity, accuracy, completeness, and consistency.
    • Challenges include expensiveness, time-consuming, human-error, and quality assurance checks.

    Data Labelling

    • Ensures accurate data labelling for machine learning models.
    • Challenges include expensiveness, time-consuming, and human-error.
    • Benefits include more precise predictions and better data usability.

    Data Lineage

    • The process of understanding, recording, and visualizing data from source to consumption.
    • Tracks transformations, changes, and errors in data processes.
    • Benefits include tracking errors, implementing process changes, performing system migrations, and combining data discovery with a comprehensive view of metadata.

    Data Integration Challenges

    • Unable to find data quickly, low-quality or outdated data, data coupled with other applications, disparate formats and sources, and too much data.

    Tight-Coupling Approach (ETL)

    • Involves creating a centralized repository or data warehouse to store integrated data.
    • Data is extracted, transformed, and loaded into a data warehouse.
    • Enables data consistency and integrity but can be inflexible and difficult to change or update.

    Loose-Coupling Approach (Data Virtualization)

    • Integrates data at the lowest level, such as individual data elements or records.
    • Allows data to be integrated without creating a central repository or data warehouse.
    • Enables data flexibility and easy updates, but can be difficult to maintain consistency and integrity across multiple data sources.

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    Description

    Compare and contrast data lakes and data warehouses, including their uses, advantages, and storage methods in big data analytics.

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