Podcast
Questions and Answers
What is a key benefit of accurate data labeling in machine learning?
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?
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?
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?
What is one challenge associated with data labeling?
Which of the following is a benefit of data lineage?
Which of the following is a benefit of data lineage?
What does the process of data lineage include?
What does the process of data lineage include?
Which of the following is a typical use case for data lineage?
Which of the following is a typical use case for data lineage?
How does data lineage help with system migrations?
How does data lineage help with system migrations?
Which of the following best describes a data lake?
Which of the following best describes a data lake?
Which characteristic is essential for a data warehouse but not necessarily for a data lake?
Which characteristic is essential for a data warehouse but not necessarily for a data lake?
What is the primary benefit of data discovery?
What is the primary benefit of data discovery?
In the data discovery process, what comes after establishing objectives?
In the data discovery process, what comes after establishing objectives?
Which of the following is NOT a benefit of data discovery?
Which of the following is NOT a benefit of data discovery?
What is a key characteristic of quality data?
What is a key characteristic of quality data?
What does the data cleaning process aim to remove?
What does the data cleaning process aim to remove?
Why are data warehouses inefficient for streaming analytics?
Why are data warehouses inefficient for streaming analytics?
Which of the following is NOT a challenge of data integration?
Which of the following is NOT a challenge of data integration?
What does the tight-coupling approach, also known as ETL, involve?
What does the tight-coupling approach, also known as ETL, involve?
In the context of data integration, what does 'ETL' stand for?
In the context of data integration, what does 'ETL' stand for?
What is a primary benefit of data warehousing in the tight-coupling approach?
What is a primary benefit of data warehousing in the tight-coupling approach?
Which issue can occur due to manual data entry errors in the example of the tight-coupling approach?
Which issue can occur due to manual data entry errors in the example of the tight-coupling approach?
Which approach is also known as data federation?
Which approach is also known as data federation?
What is one potential downside of the loose-coupling approach?
What is one potential downside of the loose-coupling approach?
Which of the following is a characteristic of the loose-coupling approach?
Which of the following is a characteristic of the loose-coupling approach?
Flashcards are hidden until you start studying
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.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
