[02/Architecture/02]

<p>Parquet or Avro files</p> Signup and view all the answers

<p>The structure of source systems changes over time</p> Signup and view all the answers

<p>Multiple cloud regions, multi-cloud scenario, and on-premises solutions</p> Signup and view all the answers

<p>Microsoft Purview</p> Signup and view all the answers

<p>Data Management Zone template</p> Signup and view all the answers

<p>Microsoft PowerBI</p> Signup and view all the answers

<p>It can easily be extended by additional technologies</p> Signup and view all the answers

<p>Consumption services</p> Signup and view all the answers

<p>All of the above</p> Signup and view all the answers

<p>The Raw Data Vault layer</p> Signup and view all the answers

<p>To bridge the gap between the data lake and the information mart layer</p> Signup and view all the answers

<p>The data lake is modeled by the source systems, while the information mart layer is modeled by the information requirements</p> Signup and view all the answers

<p>To apply business rules and model the results of the business logic</p> Signup and view all the answers

<p>To deliver useful information to the end-user</p> Signup and view all the answers

<p>The Raw Data Vault layer models the raw data, while the Business Vault layer models the information requirements</p> Signup and view all the answers

<p>Dimensions and fact entities</p> Signup and view all the answers

<p>To reduce effort in building, documenting, maintaining, and re-engineering the Business Vault</p> Signup and view all the answers

<p>To be used by both the Data Vault team and data scientists</p> Signup and view all the answers

<p>It provides a library of business rule results ready for consumption</p> Signup and view all the answers

<p>Enterprise organizations deal with different types of data that don't fit into a relational database</p> Signup and view all the answers

<p>Store the results in the Business Vault or link the unstructured data with the structured data</p> Signup and view all the answers

<p>Microsoft Azure</p> Signup and view all the answers

<p>NoSQL databases</p> Signup and view all the answers

<p>Parquet and Avro</p> Signup and view all the answers

<p>Relational databases cannot handle semi-structured data</p> Signup and view all the answers

<p>To model the raw data without changing its content</p> Signup and view all the answers

<p>It provides a centralized data governance solution</p> Signup and view all the answers

<p>Data Management Zone template</p> Signup and view all the answers

<p>To provide a historical view of the data</p> Signup and view all the answers

<p>It allows for easy extension with additional technologies</p> Signup and view all the answers

<p>Define glossaries and classify sensitive data</p> Signup and view all the answers

<p>Microsoft PowerBI</p> Signup and view all the answers

<p>Metadata</p> Signup and view all the answers

<p>Data Product Streaming template</p> Signup and view all the answers

<p>CosmosDB</p> Signup and view all the answers

<p>Data Product Analytics template</p> Signup and view all the answers

<p>Azure Event Hub</p> Signup and view all the answers

<p>To implement business logic and store results</p> Signup and view all the answers

<p>Reduced cost of adjusting the reference architecture</p> Signup and view all the answers

<p>The Raw Data Vault layer</p> Signup and view all the answers

<p>To bridge the gap between the data lake and the information mart layer</p> Signup and view all the answers

<p>The data lake is functionally oriented, while the information mart layer is modeled by information requirements</p> Signup and view all the answers

<p>To model the results of the business logic and apply business rules</p> Signup and view all the answers

<p>It allows for flexible and scalable data modeling</p> Signup and view all the answers

<p>The data lake can store semi-structured and unstructured data</p> Signup and view all the answers

<p>Raw Data Vault</p> Signup and view all the answers

<p>To model the raw data without changing its content</p> Signup and view all the answers

<p>To handle semi-structured and unstructured data</p> Signup and view all the answers

<p>Link the unstructured data with the structured data</p> Signup and view all the answers

<p>Data Catalog</p> Signup and view all the answers

<p>The Raw Data Vault models the raw data without changing its content, while the Business Vault stores the results of applying business logic</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>Raw Data Vault layer</p> Signup and view all the answers

<p>The data lake is functionally oriented, while the information mart layer is modeled by information requirements.</p> Signup and view all the answers

<p>To capture and store raw data from source systems</p> Signup and view all the answers

<p>It bridges the gap between the data lake and the information mart layer</p> Signup and view all the answers

<p>Business Vault layer</p> Signup and view all the answers

<p>Relational databases cannot handle changes in the internal structure of files</p> Signup and view all the answers

<p>Raw Data Vault</p> Signup and view all the answers

<p>To store pre-processed data for reporting</p> Signup and view all the answers

<p>Provides a library of business rule results</p> Signup and view all the answers

<p>Link the unstructured data with the structured data</p> Signup and view all the answers

<p>Data Product Batch</p> Signup and view all the answers

<p>Because enterprise organizations deal with such types of data</p> Signup and view all the answers

<p>Data Lake = Functionally oriented, modeled by the source systems Information Mart = Delivers useful information to the end-user, often modeled using a dimensional model Raw Data Vault = Models the raw data without changing its content, breaks it down into fundamental components Business Vault = Models the results of the business logic, a sparsely modeled layer</p> Signup and view all the answers

<p>Data Lake = A storage repository that holds a vast amount of raw data in its native format Information Mart = Used to deliver useful information to the end-user, just like a data mart Raw Data Vault = Models the raw data without changing its content, breaks it down into the fundamental components Business Vault = A sparsely modeled layer that only exists if a business rule needs to be applied</p> Signup and view all the answers

<p>Data Lake = Doesn't mind storing data as files, or if the internal structure of those files change Information Mart = Defined by the end-user and often modeled using a dimensional model Raw Data Vault = Models the raw data but doesn't change its content, it just restructures the incoming data set into smaller, underlying components Business Vault = A sparsely modeled layer that only exists if a business rule needs to be applied</p> Signup and view all the answers

<p>Data Lake = A repository for storing large amounts of raw data in its original format Information Mart = Delivers useful information to the end-user, often modeled using a dimensional model Raw Data Vault = Models the raw data without changing its content, breaks it down into the fundamental components Business Vault = A sparsely modeled layer that only exists if a business rule needs to be applied</p> Signup and view all the answers

<p>Data Lake = A storage repository that holds a vast amount of data in its native format Information Mart = Used to deliver useful information to the end-user, just like a data mart Raw Data Vault = Models the raw data without changing its content, breaks it down into the fundamental components Business Vault = A sparsely modeled layer that only exists if a business rule needs to be applied</p> Signup and view all the answers

<p>Data Lake = A storage repository that holds a vast amount of raw data in its native format Information Mart = Used to deliver useful information to the end-user, just like a data mart Raw Data Vault = Models the raw data without changing its content, breaks it down into the fundamental components Business Vault = A sparsely modeled layer that only exists if a business rule needs to be applied</p> Signup and view all the answers

<p>Raw Data Vault = Models the raw data without changing its content Business Vault = Provides a library of business rule results ready for consumption Data Lake = Contains semi-structured and unstructured data that are not a good fit for a relational database Information Mart = Derived from entities in both Data Vault layers, the raw data and pre-processed data</p> Signup and view all the answers

<p>Face Recognition = Can be applied to images on the data lake to associate them with customer records Optical Character Recognition = Can be used to extract plain text from a PDF document that contains scanned pages Data Lake-centric Business Logic = Can be stored in the Business Vault or linked with the unstructured data, depending on the use case Data Flow Join = Can be used to bridge the gap between raw data and information on the data lake</p> Signup and view all the answers

<p>The similar L-shape of the data lake is due to its dual-use by the Data Vault team and data scientists = true Unstructured data such as PDF documents or images are a good fit for a relational database = false The option to store the results of unstructured data processing in the Business Vault or link it with structured data depends on the specific use case = true The Raw Data Vault layer breaks down the raw data into smaller components and changes its content = false</p> Signup and view all the answers

<p>Reference Architecture = A blueprint that can be adjusted based on specific project requirements Data Analytics Platform = Includes the data lake and invites both casual users and data scientists to use both raw data and information Deviation from Reference Architecture = Should be minimal, justified, and allow for adjustments and extensions in the future Real-time Data = Can be captured and delivered using the message queue in the architecture</p> Signup and view all the answers

<p>The structure of source systems remains constant over time = false Most enterprises cannot afford having two separate data lakes = true The actual implementation of the Data Vault 2.0 architecture may deviate from the reference architecture = true The data lake is primarily used by the Data Vault team to build the data analytics platform = false</p> Signup and view all the answers

<p>JSON = Not a good fit for a relational database XML = Not a good fit for a relational database PDF = Not a good fit for a relational database Avro = Suitable for a relational database</p> Signup and view all the answers

<p>Data Vault 2.0 = A concept increasingly used for NoSQL databases where semi-structured and unstructured data is processed Data Lake = Used for staging purposes and follows the hybrid Data Vault 2.0 architecture Relational Databases = Not limited to the Data Vault 2.0 architecture, but the presented reference architecture prefers a functional structure of the data lake over a transient staging area using this ETL, Python scripts, or pipelines = Used to load the data from the source systems into the data lake in the Azure platform</p> Signup and view all the answers

<p>Parquet or Avro files = Used for data persistence in the data lake Data Lake = Used for staging purposes in the architecture ETL, Python scripts, or pipelines = Used to load the data from the source systems into the data lake Relational databases = Not preferred for a transient staging area in the presented reference architecture</p> Signup and view all the answers

<p>The Data Vault 2.0 architecture is limited to the Azure cloud = False The actual implementation of the Data Vault 2.0 architecture may deviate from the reference architecture = True Unstructured data such as PDF documents or images are a good fit for a relational database and should be stored in the Business Vault = False The burden of adjusting data structures in downstream layers lies with the source system = True</p> Signup and view all the answers

<p>Functional structure of the data lake = Preferred over a transient staging area using relational databases for multiple reasons, such as the changing structure of source systems over time Data Vault 2.0 System of Business Intelligence = The foundation for the modern data analytics platform Data Lake = Used for staging purposes and follows the hybrid Data Vault 2.0 architecture ETL, Python scripts, or pipelines = Used to load the data from the source systems into the data lake in the Azure platform</p> Signup and view all the answers

<p>Data Lake = Used for staging purposes in the architecture Relational databases = Not preferred for a transient staging area in the presented reference architecture ETL, Python scripts, or pipelines = Used to load the data from the source systems into the data lake Parquet or Avro files = Used for data persistence in the data lake</p> Signup and view all the answers

<p>Data Vault 2.0 = A concept increasingly used for NoSQL databases where semi-structured and unstructured data is processed Data Lake = Used for staging purposes and follows the hybrid Data Vault 2.0 architecture Relational Databases = Not limited to the Data Vault 2.0 architecture, but the presented reference architecture prefers a functional structure of the data lake over a transient staging area using this ETL, Python scripts, or pipelines = Used to load the data from the source systems into the data lake in the Azure platform</p> Signup and view all the answers

<p>Data Landing Zone Template = Provides data lake capabilities for the Data Vault 2.0 architecture Data Product Batch template = Provides the components for the EDW layer in the Data Vault 2.0 architecture Data Product Streaming template = Used to implement real-time capabilities in the Data Vault 2.0 architecture Data Product Analytics template = Provides services to analyze data in both the EDW layer and the data lake</p> Signup and view all the answers

<p>Data Landing Zone Template = Provides data lake capabilities and services for regional deployments, data ownership separation, cost management, and data sharing within the organization Data Product Batch template = Deploys the relevant databases for the EDW layer, primarily Azure Synapse Data Product Streaming template = Deploys streaming components, including Azure Event Hub, IoT Hub and Stream Analytics services Data Product Analytics template = Provides services to analyze data in both the EDW layer and the data lake</p> Signup and view all the answers

<p>Raw data lake zone = Relevant for the data ingestion into the Raw Data Vault Curated data lake zone = Applies data quality and other transformations downstream from the Raw Data Vault layer Workspace data lake zone = Applies data quality and other transformations downstream from the Raw Data Vault layer Business Vault = Stores the results of applying business logic, typically in a relational format</p> Signup and view all the answers

<p>Azure Event Hub = Used to transport message streams towards and within the data analytics platform IoT Hub = Used to transport message streams towards and within the data analytics platform Stream Analytics = Used to apply business logic in real-time for the real-time enabled Business Vault AzureML = Used to perform data mining tasks on the structured or semi-structured/unstructured data in the EDW layer or the data lake</p> Signup and view all the answers

<p>Raw Data Vault = Responsible for modeling the raw data without changing its content EDW layer = Provides the components for the Data Vault 2.0 architecture, such as Azure Synapse Business Vault = Introduces and applies business rules to the raw data Information Mart layer = Modeled using a dimensional model in a Data Vault model</p> Signup and view all the answers

<p>CosmosDB = Used for semi-structured or graph data in the EDW layer Azure Synapse = Used for structured, relational data in the EDW layer MySQL = Optional database that can be deployed by the Data Product Batch template PostgreSQL = Optional database that can be deployed by the Data Product Batch template</p> Signup and view all the answers

<p>Data Management Zone template = Provides network, governance, and consumption services Microsoft Purview = Provides data governance capabilities and allows the data analytics team to define glossaries, classify sensitive data, and define data assets and their relationships as metadata Data Vault 2.0 model = Generated and automated using metadata setup by Microsoft Purview Microsoft PowerBI = Used for dashboarding in the technology stack for the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Raw Data Vault = Responsible for modeling the raw data without changing its content Business Vault = Used to store the results of unstructured data processing or link it with structured data depending on the use case Information Mart = Conceptually the same as a data mart in legacy data warehousing Data Lake = Used for staging purposes and can easily adapt to changes in the internal structure of the files stored within it</p> Signup and view all the answers

<p>Data Management Zone template = Provides network, governance, and consumption services Microsoft Purview = Allows the data analytics team to define glossaries, classify sensitive data, and define data assets and their relationships as metadata Data Vault 2.0 model = Generated and automated using metadata setup by Microsoft Purview Microsoft PowerBI = Used for dashboarding in the technology stack for the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Raw Data Vault = Breaks down the raw data into smaller components without changing its content Business Vault = Responsible for modeling the raw data without changing its content Information Mart = Often modeled using a dimensional model Data Lake = Used for staging purposes and can easily adapt to changes in the internal structure of the files stored within it</p> Signup and view all the answers

<p>Data Management Zone template = Provides network, governance, and consumption services Microsoft Purview = Allows the data analytics team to define glossaries, classify sensitive data, and define data assets and their relationships as metadata Data Vault 2.0 model = Generated and automated using metadata setup by Microsoft Purview Microsoft PowerBI = Used for dashboarding in the technology stack for the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Raw Data Vault = Breaks down the raw data into smaller components without changing its content Business Vault = Stores the results of unstructured data processing or links it with structured data depending on the use case Information Mart = Conceptually the same as a data mart in legacy data warehousing Data Lake = Used for staging purposes and can easily adapt to changes in the internal structure of the files stored within it</p> Signup and view all the answers

<p>Data Lake = Functionally oriented, modeled by the source systems Information Mart = Defined by the end-user, often modeled using a dimensional model EDW layer = Bridges the gap between the data lake and the information mart Business Vault = Sparsely modeled layer, only exists if a business rule needs to be applied</p> Signup and view all the answers

<p>Data Lake = A central repository for storing structured, semi-structured, and unstructured data Information Mart = A layer that delivers useful information to the end-user, modeled by the information requirements Raw Data Vault = A layer that models the raw data without changing its content, using hubs, links, and satellites Business Vault = A layer that models the results of the business logic and the business logic itself, if virtualized using SQL views</p> Signup and view all the answers

<p>Raw Data Vault = Models the raw data but breaks it down into the fundamental components of all enterprise data Business Vault = Models the results of the business logic and, if virtualized using SQL views, the business logic itself Information Mart = Delivers useful information to the end-user, just like a data mart EDW layer = Bridges the gap between the raw data in the data lake and the information in the information mart</p> Signup and view all the answers

<p>The Raw Data Vault layer breaks down the raw data into smaller components without changing its content. = True The Data Landing Zone Template provides data lake capabilities for the Data Vault 2.0 architecture. = False The data lake in the Data Vault 2.0 architecture is primarily used by the Data Vault team to build the data analytics platform. = False Unstructured data such as PDF documents or images are a good fit for a relational database and should be stored in the Business Vault. = False</p> Signup and view all the answers

<p>Data Lake = Stores structured, semi-structured, and unstructured data, modeled by the source systems Information Mart = Delivers useful information to the end-user, often modeled using a dimensional model EDW layer = Bridges the gap between the raw data in the data lake and the information in the information mart Business Vault = Models the results of the business logic and, if virtualized using SQL views, the business logic itself</p> Signup and view all the answers

<p>Python = General-purpose programming JavaScript = Client-side scripting for web applications SQL = Database queries CSS = Styling web pages</p> Signup and view all the answers

<p>Data Landing Zone Template = Provides data lake capabilities Data Product Batch template = Provides the components of the EDW layer Data Product Streaming template = Provides real-time capabilities Data Product Analytics template = Provides services to analyze data in both the EDW layer and the data lake</p> Signup and view all the answers

<p>Raw data lake zone = Relevant for the data ingestion into the Raw Data Vault Curated data lake zone = Applies data quality and other transformations downstream from the Raw Data Vault Workspace data lake zone = Applies data quality and other transformations downstream from the Raw Data Vault Managed Self-Service BI solution = Applicable when dealing with semi-structured or unstructured data that should be processed in the data lake instead of the relational Synapse database</p> Signup and view all the answers

<p>Azure Event Hub = Used to transport the message streams towards and within the data analytics platform IoT Hub = Used to transport the message streams towards and within the data analytics platform Stream Analytics = Used to apply business logic in real-time for the real-time enabled Business Vault AzureML = Used to perform data mining tasks on the structured data in Synapse or the semi-structured or unstructured data in the data lake</p> Signup and view all the answers

<p>Raw Data Vault = Models the raw data without changing its content Business Vault = Introduces and applies business rules to the raw data Information Mart = Derived only from the pre-processed data in the Business Vault Data Lake = Used by the architecture to stage data, can also be used for other analytical use cases</p> Signup and view all the answers

<p>Raw Data Vault = Models the raw data without changing its content Business Vault = Introduces and applies business rules to the raw data Information Mart = Derived only from the pre-processed data in the Business Vault Data Lake = Used by the architecture to stage data, can also be used for other analytical use cases</p> Signup and view all the answers

<p>Batch data processing = Performed in the EDW layer, for example using Azure Synapse Real-time data processing = Performed using services like Azure Event Hub, IoT Hub, and Stream Analytics Data mining = Performed on the structured data in Synapse or the semi-structured or unstructured data in the data lake Managed Self-Service BI = Used when dealing with semi-structured or unstructured data that should be processed in the data lake instead of the relational Synapse database</p> Signup and view all the answers

<p>Data Management Zone template = Provides network, governance, and consumption services Microsoft Purview = Allows the data analytics team to define glossaries, classify sensitive data, and define data assets and their relationships as metadata Data Vault 2.0 model = Automatically generated using metadata setup in Microsoft Purview Microsoft PowerBI = Used for dashboarding in the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>The solution can easily be extended by additional technologies across different environments = Advantage of the Data Vault 2.0 concept Unstructured data such as PDF documents or images are a good fit for a relational database and should be stored in the Business Vault = False, unstructured data should not be stored in the Business Vault The data lake in the Data Vault 2.0 architecture contains semi-structured and unstructured data = True The burden of adjusting data structures in downstream layers lies with the data analytics team, not the source system = False, the burden lies with the source system</p> Signup and view all the answers

<p>Data Vault 2.0 = A concept that allows for easy extension by additional technologies across different environments Data Management Zone = A template that provides network, governance, and consumption services Microsoft Purview = A service that allows the data analytics team to define glossaries, classify sensitive data, and define data assets and their relationships as metadata Microsoft PowerBI = A service used for dashboarding in the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Business Vault = Introduces and applies business rules to the raw data Raw Data Vault = Responsible for modeling the raw data without changing its content Information Mart = Derived from the pre-processed data in the Business Vault Data Lake = Contains semi-structured and unstructured data</p> Signup and view all the answers

<p>Data Management Zone template = Provides network, governance, and consumption services Microsoft Purview = Allows the data analytics team to define glossaries, classify sensitive data, and define data assets and their relationships as metadata Data Vault 2.0 model = Automatically generated using metadata setup in Microsoft Purview Microsoft PowerBI = Used for dashboarding in the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>The solution can easily be extended by additional technologies across different environments = Advantage of the Data Vault 2.0 concept Unstructured data such as PDF documents or images are a good fit for a relational database and should be stored in the Business Vault = False, unstructured data should not be stored in the Business Vault The data lake in the Data Vault 2.0 architecture contains semi-structured and unstructured data = True The burden of adjusting data structures in downstream layers lies with the data analytics team, not the source system = False, the burden lies with the source system</p> Signup and view all the answers

<p>The architecture is not limited to the Azure cloud = Data Vault 2.0 Increasingly used for NoSQL databases where semi-structured and unstructured data is processed = Data Vault 2.0 concept Used for staging purposes and follows the hybrid Data Vault 2.0 architecture = Data lake Loaded using ETL, Python scripts, or pipelines on the Azure platform into the data lake = Data from the source systems</p> Signup and view all the answers

<p>Data Vault 2.0 = A distributed solution that can span across multiple environments Data Vault 2.0 concept = Increasingly used for NoSQL databases where semi-structured and unstructured data is processed Data lake = Used for staging purposes and follows the hybrid Data Vault 2.0 architecture Data from the source systems = Loaded using ETL, Python scripts, or pipelines on the Azure platform into the data lake</p> Signup and view all the answers

<p>Data Vault 2.0 = A concept increasingly used for NoSQL databases Data Vault 2.0 reference architecture = Not limited to the Azure cloud Data lake = Preferred over a transient staging area using relational databases Staging area = The structure of source systems changes over time, making structured data look like semi-structured data</p> Signup and view all the answers

<p>Used for NoSQL databases where semi-structured and unstructured data is processed = Data Vault 2.0 concept Follows the hybrid Data Vault 2.0 architecture and is not limited to relational databases = Data lake Loaded using ETL, Python scripts, or pipelines on the Azure platform into the data lake = Data from the source systems A functional structure of this is preferred over a transient staging area using relational databases = Data lake</p> Signup and view all the answers

<p>Data Vault 2.0 = A concept increasingly used for NoSQL databases where semi-structured and unstructured data is processed Data lake = Used for staging purposes and follows the hybrid Data Vault 2.0 architecture Data from the source systems = Loaded using ETL, Python scripts, or pipelines on the Azure platform into the data lake Staging area = The original idea was to reduce the burden from the source system, but over time, structured data looks like semi-structured data</p> Signup and view all the answers

<p>Data Vault 2.0 concept = Increasingly used for NoSQL databases where semi-structured and unstructured data is processed Data lake = Used for staging purposes and follows the hybrid Data Vault 2.0 architecture Data from the source systems = Loaded using ETL, Python scripts, or pipelines on the Azure platform into the data lake Staging area = The structure of source systems changes over time, making structured data look like semi-structured data</p> Signup and view all the answers

<p>Raw Data Vault = Models the raw data without changing its content Business Vault = Provides a library of business rule results ready for consumption Data Lake = Used for both the Data Vault and data science, contains semi-structured and unstructured data Information Mart = Derived from entities in both Data Vault layers, contains dimensions and fact entities</p> Signup and view all the answers

<p>Structured Data = Fits into relational databases and is used in the Business Vault Semi-structured Data = Such as JSON and XML data, not a good fit for a relational database and stays on the data lake Unstructured Data = Such as PDF documents or images, not a good fit for a relational database and stays on the data lake Customer Business Key = Structured data extracted from an unstructured image in the data lake</p> Signup and view all the answers

<p>Data Lake = Used by the Data Vault team to build the data analytics platform and by data scientists to develop ad-hoc solutions Business Vault = Provides a library of business rule results ready for consumption, can store the results of data lake-centric business logic Raw Data Vault = Bridges the gap between the data lake and the information mart, models the raw data without changing its content Information Mart = Contains dimensions and fact entities, derived from entities in both Data Vault layers</p> Signup and view all the answers

<p>The actual implementation of the Data Vault 2.0 architecture may deviate from the reference architecture, but the deviations should be minimal and justified = True The technology stack for the Data Vault 2.0 architecture based on the Azure cloud includes Microsoft PowerBI for dashboarding = False The structure of source systems remains constant over time = False The Data Vault 2.0 architecture is primarily implemented on the Azure cloud platform = True</p> Signup and view all the answers

<p>Data Landing Zone Template = Provides data lake capabilities for the Data Vault 2.0 architecture Data Product Batch Template = Provides the components of the EDW layer in the Data Vault 2.0 architecture Data Management Zone Template = Provides network, governance, and consumption services Streaming Template = Used to deploy streaming components for real-time processing in the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Business Vault = Layer that provides a library of business rule results ready for consumption Data Lake = Storage repository that holds a large amount of raw data in its native format until it is needed Raw Data Vault = Layer that models the raw data without changing its content Information Mart = Layer that contains dimensions and fact entities, derived from entities in both Data Vault layers</p> Signup and view all the answers