24 Questions
What is a key benefit of accurate data labeling in machine learning?
Enhancing model accuracy
Which is a possible consequence of human error in data labeling?
Decreased quality of data
How can reclassifying a categorical variable as a binary variable benefit a model?
Makes the variable more consumable
What is one challenge associated with data labeling?
It is prone to human coding errors
Which of the following is a benefit of data lineage?
Tracks errors in data processes
What does the process of data lineage include?
Recording data transformations
Which of the following is a typical use case for data lineage?
Deprecating columns
How does data lineage help with system migrations?
By maintaining metadata integrity
Which of the following best describes a data lake?
A repository that stores pools of big data for advanced analytics applications
Which characteristic is essential for a data warehouse but not necessarily for a data lake?
Highly structured and unified data
What is the primary benefit of data discovery?
Democratization, collaboration, and improved decision-making
In the data discovery process, what comes after establishing objectives?
Determining the data storage scope
Which of the following is NOT a benefit of data discovery?
Increased storage efficiency
What is a key characteristic of quality data?
Validity, ensuring conformity to business rules
What does the data cleaning process aim to remove?
Incorrectly formatted or incomplete data
Why are data warehouses inefficient for streaming analytics?
They require data to be cleansed and processed sequentially
Which of the following is NOT a challenge of data integration?
Consistent data formatting
What does the tight-coupling approach, also known as ETL, involve?
Creating a centralized repository to store integrated data
In the context of data integration, what does 'ETL' stand for?
Extraction, Transformation, and Loading
What is a primary benefit of data warehousing in the tight-coupling approach?
Providing a cohesive view for analysis and decision-making
Which issue can occur due to manual data entry errors in the example of the tight-coupling approach?
Number of aquariums shipped not matching the actual number sold
Which approach is also known as data federation?
Loose-coupling approach
What is one potential downside of the loose-coupling approach?
Difficulty in maintaining consistency and integrity across data sources
Which of the following is a characteristic of the loose-coupling approach?
Integrating data at the record level
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.
Compare and contrast data lakes and data warehouses, including their uses, advantages, and storage methods in big data analytics.
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