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Questions and Answers
What is the primary purpose of silver tables?
What is the primary purpose of silver tables?
Which statement best describes the data quality in gold tables?
Which statement best describes the data quality in gold tables?
In what scenario would silver tables be utilized?
In what scenario would silver tables be utilized?
What is a common use case for data found in gold tables?
What is a common use case for data found in gold tables?
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Which best describes a workload using bronze tables as a source?
Which best describes a workload using bronze tables as a source?
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Silver tables contain raw data that is unprocessed and unvalidated.
Silver tables contain raw data that is unprocessed and unvalidated.
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Gold tables are primarily used for training machine learning models.
Gold tables are primarily used for training machine learning models.
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Data in gold tables is in its raw form and not suitable for business intelligence applications.
Data in gold tables is in its raw form and not suitable for business intelligence applications.
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Silver tables are not ideal for self-service analytics as they lack reliable data.
Silver tables are not ideal for self-service analytics as they lack reliable data.
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Historical data analysis workloads require aggregated data stored in gold tables.
Historical data analysis workloads require aggregated data stored in gold tables.
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Match the following tables with their key characteristics:
Match the following tables with their key characteristics:
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Match the following use cases with the appropriate tables:
Match the following use cases with the appropriate tables:
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Match the following examples with the respective data tables:
Match the following examples with the respective data tables:
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Match the following data qualities with the corresponding table:
Match the following data qualities with the corresponding table:
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Match the following workloads with the type of tables they originate from:
Match the following workloads with the type of tables they originate from:
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Silver tables are used to provide an enterprise view of key business ______ and transactions.
Silver tables are used to provide an enterprise view of key business ______ and transactions.
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Gold tables are optimized for analytics, machine learning, and production ______.
Gold tables are optimized for analytics, machine learning, and production ______.
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Data in silver tables is validated and ______, ensuring it is reliable for downstream analytics.
Data in silver tables is validated and ______, ensuring it is reliable for downstream analytics.
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Gold tables represent the final, most ______ stage of data.
Gold tables represent the final, most ______ stage of data.
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Workloads that validate and clean raw data before moving it to the silver layer are part of ______ processes.
Workloads that validate and clean raw data before moving it to the silver layer are part of ______ processes.
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What is the primary distinction between the serverless compute plane and the classic compute plane in Databricks?
What is the primary distinction between the serverless compute plane and the classic compute plane in Databricks?
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Which component of Databricks architecture contains the backend services that are managed in the user's Databricks account?
Which component of Databricks architecture contains the backend services that are managed in the user's Databricks account?
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What purpose does the workspace storage bucket serve in Databricks architecture?
What purpose does the workspace storage bucket serve in Databricks architecture?
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What is true about the security measures in the serverless compute plane?
What is true about the security measures in the serverless compute plane?
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Which statement best describes the regional aspect of the serverless compute plane?
Which statement best describes the regional aspect of the serverless compute plane?
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What is a significant feature of the classic compute plane that enhances its security?
What is a significant feature of the classic compute plane that enhances its security?
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How does Databricks achieve natural isolation in the classic compute plane?
How does Databricks achieve natural isolation in the classic compute plane?
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What type of resources are utilized in the serverless compute plane?
What type of resources are utilized in the serverless compute plane?
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Study Notes
Silver Tables
- Purpose: Silver tables contain cleansed and conformed data, offering an enterprise view of essential business entities and transactions.
- Data Quality: Data in silver tables is validated and deduplicated, ensuring its reliability for downstream analytics.
- Use Cases: Silver tables are ideal for self-service analytics purposes, ad-hoc reporting, and serve as a source for further data transformations and aggregations.
- Examples: Master customer records, non-duplicated transactions, and cross-reference tables.
Gold Tables
- Purpose: Gold tables contain highly refined and aggregated data, optimized for analytics, machine learning, and production applications.
- Data Quality: Data in gold tables is enriched and transformed into a format ready for business intelligence and advanced analytics.
- Use Cases: Gold tables are utilized for dashboards, reporting, and feeding machine learning models. They represent the final, most refined stage of data.
- Examples: Sales performance metrics, customer lifetime value calculations, and predictive analytics outputs.
Workloads Using Bronze Tables as a Source
- Initial data ingestion and transformation processes: These processes load raw data from external sources into the data lake.
- Data Validation: Workloads validate and clean raw data before moving it to the silver layer.
- Historical Data Analysis: Use cases that require access to raw, unprocessed data for audit and lineage purposes.
Workloads Using Gold Tables as a Source
- Business Intelligence: Dashboards and reports requiring high-quality, aggregated data for decision-making.
- Machine Learning: Training and deploying machine learning models that need enriched and feature-engineered data.
Silver Tables
- Silver tables contain cleansed and conformed data.
- Silver tables provide an enterprise view of key business entities and transactions.
- Silver tables are used for self-service analytics, ad-hoc reporting, and further data transformations.
- Data in silver tables is validated and deduplicated for reliability.
- Examples of silver tables include master customer records, non-duplicated transactions, and cross-reference tables.
Gold Tables
- Gold tables contain highly refined and aggregated data.
- Gold tables are optimized for analytics, machine learning, and production applications.
- Data in gold tables is enriched and transformed for business intelligence and advanced analytics.
- Gold tables are used for dashboards, reporting, and feeding machine learning models.
- Gold tables represent the final, most refined stage of data.
- Examples of gold tables include sales performance metrics, customer lifetime value calculations, and predictive analytics outputs.
Workloads Using Bronze Tables
- ETL processes use bronze tables as a source for initial data ingestion and transformation.
- Data validation workloads use bronze tables to clean raw data before moving to the silver layer.
- Historical data analysis uses bronze tables for access to raw, unprocessed data for audit and lineage purposes.
Workloads Using Gold Tables
- Business intelligence uses gold tables for dashboards and reports that require high-quality, aggregated data for decision-making.
- Machine learning uses gold tables to train and deploy models that need enriched and feature-engineered data.
Silver Tables
- Silver tables store cleansed and conformed data.
- They create an enterprise view of key business entities and transactions.
- The data is validated and deduplicated, ensuring reliability for downstream analytics.
- They are used for self-service analytics, ad-hoc reporting, and as a source for further data transformations and aggregations.
- Examples include master customer records, non-duplicated transactions, and cross-reference tables.
Gold Tables
- Gold tables contain highly refined and aggregated data.
- They're optimized for analytics, machine learning, and production applications.
- Data in gold tables is enriched and transformed into a format ready for business intelligence and advanced analytics.
- These tables are used for dashboards, reporting, and feeding machine learning models.
- They represent the final, most refined stage of data.
- Examples include sales performance metrics, customer lifetime value calculations, and predictive analytics outputs.
Workloads Using Bronze Tables
- Bronze tables are used in initial data ingestion and transformation processes to load raw data from external sources into the data lake.
- They are used for data validation and cleaning before moving data to the silver layer.
- Bronze tables are also used for historical data analysis when access to raw, unprocessed data is required for audit and lineage purposes.
Workloads Using Gold Tables
- Gold tables are a source for business intelligence dashboards and reports that require high-quality, aggregated data for decision-making.
- They are also used for training and deploying machine learning models that need enriched and feature-engineered data.
Silver Tables
- Contain cleansed and conformed data
- Provide an enterprise view of key business entities and transactions
- Data is validated and deduplicated, ensuring it is reliable for downstream analytics
- Used for self-service analytics, ad-hoc reporting, and as a source for further data transformations and aggregations
- Examples: Master customer records, non-duplicated transactions, and cross-reference tables
Gold Tables
- Contain highly refined and aggregated data
- Optimized for analytics, machine learning, and production applications
- Data is enriched and transformed into a format that is ready for business intelligence and advanced analytics
- Used for dashboards, reporting, and feeding machine learning models
- Represent the final, most refined stage of data
- Examples: Sales performance metrics, customer lifetime value calculations, and predictive analytics outputs
Workloads Using Bronze Tables as a Source
- ETL processes: Initial data ingestion and transformation processes that load raw data from external sources into the data lake
- Data validation: Workloads that validate and clean raw data before moving it to the silver layer
- Historical data analysis: Use cases that require access to the raw, unprocessed history of data for audit and lineage purposes
Workloads Using Gold Tables as a Source
- Business intelligence: Dashboards and reports that require high-quality, aggregated data for decision-making
- Machine learning: Training and deploying machine learning models that need enriched and feature-engineered data
Databricks Architecture Overview
- Databricks operates with two planes: control plane and compute plane.
- Control plane encompasses backend services managed within your Databricks account. The web application is part of the control plane.
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Compute plane handles data processing. Two compute planes exist:
- Serverless compute plane: Databricks compute resources operate within a serverless compute plane in your account.
- Classic compute plane: Compute resources reside in your AWS account, within a network referred to as the classic compute plane.
- Each Databricks workspace has a dedicated workspace storage bucket, located in your AWS account.
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Serverless compute plane:
- Runs in a compute layer within your Databricks account.
- Databricks sets up a serverless compute plane in the same AWS region as your workspace's classic compute plane.
- Ensures data security within the serverless compute plane with network boundaries and isolation between workspaces.
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Classic compute plane:
- Runs in your AWS account.
- New compute resources are created within each workspace's virtual network in your AWS account.
- Achieves natural isolation as it operates in each customer's dedicated AWS account.
- Regional support is based on Databricks clouds and regions.
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Description
Explore the roles of silver and gold tables in data warehousing. This quiz will cover their purposes, data quality, and use cases, helping you understand how they support analytics and business intelligence. Gain insight into how these tables are structured and utilized for effective data management.
