[01/DataVault/01]

<p>Data Vault 2.0 architecture, methodology, implementation practices, and model</p> Signup and view all the answers

<p>Fully auditable solutions</p> Signup and view all the answers

<p>Full data lineage at the attribute level</p> Signup and view all the answers

<p>The unmodified data still exists</p> Signup and view all the answers

<p>To build scalable business intelligence solutions</p> Signup and view all the answers

<p>Methodology, architecture, and modeling</p> Signup and view all the answers

<p>It can extract load data from multiple sources, whether internal or external</p> Signup and view all the answers

<p>Real-time feeds and REST APIs</p> Signup and view all the answers

<p>Nightly batches, CDC loads, and real-time</p> Signup and view all the answers

<p>Relational database applications</p> Signup and view all the answers

<p>To provide ready-to-consume data for decision-making</p> Signup and view all the answers

<p>Dynamic Access Control List (ACL)</p> Signup and view all the answers

<p>To comply with GDPR regulations</p> Signup and view all the answers

<p>Delivering business value</p> Signup and view all the answers

<p>By adding additional entities</p> Signup and view all the answers

<p>To provide different perspectives on business data</p> Signup and view all the answers

<p>A method for shooting functionality through the layers of a data analytics platform</p> Signup and view all the answers

<p>Clients with no code modification restrictions</p> Signup and view all the answers

<p>Small solutions</p> Signup and view all the answers

<p>Both labels in a dashboard and data and information</p> Signup and view all the answers

<p>It simplifies the addition of thousands of tables</p> Signup and view all the answers

<p>Both on-premises Microsoft SQL Server and Azure Synapse Dedicated SQL Pool</p> Signup and view all the answers

<p>To capture data from different organisations</p> Signup and view all the answers

<p>Very large data warehousing (VLDW) environments</p> Signup and view all the answers

<p>To deploy on single-node systems</p> Signup and view all the answers

<p>To linearly scale with the data volume or speed</p> Signup and view all the answers

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

<p>Extracts load data from multiple sources</p> Signup and view all the answers

<p>Web-Service and REST APIs</p> Signup and view all the answers

<p>Providing a new approach for building scalable business intelligence solutions</p> Signup and view all the answers

<p>Structured, semi-structured, and unstructured data</p> Signup and view all the answers

<p>To accomplish the enterprise vision in Data Warehousing and Information Delivery</p> Signup and view all the answers

<p>Relational database applications</p> Signup and view all the answers

<p>Various loading cycles including independent nightly batches, CDC loads, near real-time, and actual real-time</p> Signup and view all the answers

<p>To serve all business users regardless of their definition of the truth</p> Signup and view all the answers

<p>To extract and load all data from hundreds of source systems</p> Signup and view all the answers

<p>It is often distributed across different environments</p> Signup and view all the answers

<p>Data Vault 2.0 architecture, methodology, implementation practices, and model</p> Signup and view all the answers

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

<p>To store and keep unstructured data on Azure Data Lake Storage</p> Signup and view all the answers

<p>To implement fully auditable solutions</p> Signup and view all the answers

<p>Cell-level security</p> Signup and view all the answers

<p>Ability to extend the model by adding additional source data</p> Signup and view all the answers

<p>To shoot functionality through the layers of the data analytics platform</p> Signup and view all the answers

<p>The inability to modify existing code</p> Signup and view all the answers

<p>To support the deletion of PII data</p> Signup and view all the answers

<p>Business value</p> Signup and view all the answers

<p>Ability to modify existing code without impacting functionality</p> Signup and view all the answers

<p>Limited functionality</p> Signup and view all the answers

<p>It is only suitable for large enterprise solutions</p> Signup and view all the answers

<p>Both Azure Synapse Dedicated SQL Pool and Microsoft SQL Server on premises</p> Signup and view all the answers

<p>Simplifies the addition of thousands of tables</p> Signup and view all the answers

<p>To process twice the volume of data, clients need twice the resources</p> Signup and view all the answers

<p>Real-time processing</p> Signup and view all the answers

<p>To process any volume of data at any speed</p> Signup and view all the answers

<p>To capture data from different organizations</p> Signup and view all the answers

<p>To improve scalability</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>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>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>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>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>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>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>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>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>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>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>Data Vault 2.0 architecture = The technical framework for building a decentralised data analytics platform Data Vault 2.0 methodology = The approach for implementing data-driven solutions with high auditability requirements Data Vault 2.0 implementation practices = The set of guidelines and best practices for building enterprise data warehouse solutions Data Vault 2.0 model = The data modelling approach that allows for full data lineage at the attribute level</p> Signup and view all the answers

<p>Data Transformation = The process of turning the raw data from the source into actionable information Data Distribution = The approach of making all data and all information available across different environments Business Logic = The set of rules and requirements used to pre-process the raw data into useful information Data Consumption = The act of information users consuming the pre-processed data</p> Signup and view all the answers

<p>Information User = The individual or group that consumes the pre-processed data from the data analytics platform Raw Data = The unprocessed data from the source that is transformed into useful information Data Lake = A storage repository that holds a vast amount of raw data in its native format until it is needed Scalefree = A company co-founded by Dan Linstedt, the inventor of Data Vault</p> Signup and view all the answers

<p>Data Vault 2.0 is a methodology, architecture, and modeling approach = True Data Vault 2.0 supports full data lineage at the attribute level = True Data Vault 2.0 can only be deployed on massively parallel processing (MPP) platforms = False Data Vault 2.0 is only suitable for large enterprise solutions or solutions with high data volume = False</p> Signup and view all the answers

<p>Data Vault 1.0 = The original version of the data modelling approach invented by Dan Linstedt Data Analytics Platform = The system that turns raw data into actionable information and makes it available to information users Decentralised Data Analytics Platform = A platform that extracts and loads all data from hundreds of source systems and delivers the information resulting from transformations to information users with varying and potentially contradicting business rules and information requirements Pillars of Data Vault 2.0 = The aspects covered by Data Vault 2.0, including its architecture, methodology, implementation practices, and model</p> Signup and view all the answers

<p>Structured Data = Data that is organized and easily searchable, typically stored in a relational database Unstructured Data = Data that does not conform to a specific data model or does not have a pre-defined data structure, such as text or multimedia content Data Warehouse = A system used for reporting and data analysis, often containing large amounts of historical data Data Lake Storage = A storage repository that holds a vast amount of raw data in its native format until it is needed</p> Signup and view all the answers

<p>Actionable Information = Data that has been processed and analyzed in a way that it can be used for decision making Business Perspective = The viewpoint or approach that an information user wants to apply on the raw data Data Pre-processing = The application of business logic to the raw data to turn it into useful information Massively Parallel Processing (MPP) = A technique used to process large amounts of data in parallel, typically in a distributed computing environment</p> Signup and view all the answers

<p>Not limited to one single source system or type of data = The solution can extract load data from multiple sources, whether internal systems or external sources Data may be structured, semi-structured or unstructured = Structured data often originates from relational database applications while semi-structured data, such as JSON or XML documents and messages are loaded from real-time feeds, Web-Service and REST APIs or semi-structured applications and their databases. Unstructured data might be images, PDFs and video files or streams Data for each source could be delivered in various loading cycles = Loading cycles can include independent nightly batches, CDC loads, near real-time or in actual real-time Scalability = The solution is designed to handle very large data warehousing (VLDW) environments</p> Signup and view all the answers

<p>Methodology = A set of principles or rules for performing a particular activity or solving a problem Architecture = The conceptual structure and logical organization of a computer or computer-based system Modelling = Creating a representation of a system or process in order to better understand or improve it Data Warehousing and Information Delivery = The process of collecting, organizing, and storing large amounts of data to be analyzed and used in decision-making</p> Signup and view all the answers

<p>Structured data = Data that often originates from relational database applications Semi-structured data = Data such as JSON or XML documents and messages that are loaded from real-time feeds, Web-Service and REST APIs or semi-structured applications and their databases Unstructured data = Data that might be images, PDFs and video files or streams</p> Signup and view all the answers

<p>Data Vault 2.0 = A concept to build scalable business intelligence solutions that provide a new approach for building a modern data analytics platform Enterprise vision = The long-term strategy and goals of an organization Data lineage = The life cycle of data, including its origins and where it moves over time Multi-tenancy = The ability of a system to serve multiple customers or tenants</p> Signup and view all the answers

<p>Independent nightly batches = Data is consumed from the source in independent batches on a nightly basis CDC loads = Data is consumed from the source using the Change Data Capture (CDC) method Near real-time = Data is consumed from the source with minimal delay Actual real-time = Data is consumed from the source immediately as it becomes available</p> Signup and view all the answers

<p>Scalability = The ability of a system to handle increasing amounts of work or data Zero-change impact on code = The ability to make changes to the system without affecting the existing code Adaptability = The ability of the system to adjust to changes in source systems and business requirements Massively parallel processing (MPP) platforms = Platforms that use parallel processing across multiple processors to perform a task</p> Signup and view all the answers

<p>Relational data = Data that is organized into tables with a predefined structure Semi-structured data = Data that does not have a strict schema, but has some structure or organization Unstructured data = Data that does not have a predefined structure or organization</p> Signup and view all the answers

<p>Cell-level security = Combination of row-level and column-level security to apply usage restrictions via a dynamic Access Control List (ACL) to the data and information Data separation by privacy classification = Supports the deletion or reduction of PII data by independently deleting the data based on privacy class Tracer bullet approach = Shoots the functionality through the layers of the data analytics platform by adding only the required data for a specific business value Zero-change impact on code = Allows for the adaptability of the Data Vault 2.0 model in environments where existing code cannot be modified, by creating new tables or views</p> Signup and view all the answers

<p>Business value = Something that can be used by the business user, often a report, a dashboard, or at least some parts of it, such as KPIs Data-driven solutions = Solutions that are implemented based on the data available and the business value it can provide Multi-temporal solution = Application of one or sometimes multiple business timelines in a solution Data analytics platform = A platform that provides data analytics capabilities for an organization</p> Signup and view all the answers

<p>Agile delivery = Data analytics platform is implemented sprint-by-sprint, focusing on business value Adaptability = Ability of the Data Vault 2.0 model to easily absorb changes in source systems and business requirements Scalability = Data Vault 2.0 supports the addition of thousands of tables over time Data lineage = Data Vault 2.0 supports full data lineage at the attribute level to prove the source of data used in information artifacts</p> Signup and view all the answers

<p>Personal data = One of the two main data classes used by most industry clients in Data Vault 2.0, often subject to privacy regulations Non-personal data = One of the two main data classes used by most industry clients in Data Vault 2.0, not subject to privacy regulations Data separation = Process of dividing data based on its privacy classification in Data Vault 2.0 Physical delete = Implementation in Data Vault 2.0 that allows for the independent deletion of data by privacy class</p> Signup and view all the answers

<p>Data Vault 2.0 model = A scalable and adaptable data modeling approach used to build data analytics platforms Business logic = The unmodified logic of a report that still exists in Data Vault 2.0 Source data = Data that is added to the Data Vault 2.0 model based on the business value it provides Target information model = Often a star schema or snowflake schema, derived from the Data Vault 2.0 model to build the report</p> Signup and view all the answers

<p>Row-level security = A type of security that restricts access to specific rows in a database table Column-level security = A type of security that restricts access to specific columns in a database table Cell-level security = A type of security that is a combination of row-level and column-level security in Data Vault 2.0 Access Control List (ACL) = A dynamic list used in Data Vault 2.0 to apply usage restrictions to the data and information</p> Signup and view all the answers

<p>Data Vault 2.0 = A pattern-based implementation for data analytics platforms that supports real-time processing and security as additional features Data analytics platform = A platform that provides data analytics capabilities for an organization Business value = Something that can be used by the business user, often a report, a dashboard, or at least some parts of it, such as KPIs Agile delivery = Data analytics platform is implemented sprint-by-sprint, focusing on business value</p> Signup and view all the answers

<p>Scalability = The ability of Data Vault 2.0 to handle large data volumes and high data velocities Pattern-based implementation = The concept that all entities in Data Vault 2.0 follow similar patterns, allowing for automation of the data warehouse Multi-tenancy = A feature that allows the capture of data from different organizations or entities, and supports either tenant-specific reports or reports across tenants MPP platforms = Massively parallel processing platforms that Data Vault 2.0 can be deployed on, such as Azure Synapse Dedicated SQL Pool</p> Signup and view all the answers

<p>Multi-lingual platforms = Supports the implementation of not only multi-lingual labels in a dashboard, but also multi-lingual data and information Multi-currency support = Ability to transform all foreign currencies into a leading currency, or any currency selected by the user Data variety = Supports the addition of thousands of source tables over time, with the help of automation tools Scalability = Ability to handle additional requirements as the client's needs grow</p> Signup and view all the answers

<p>Data Vault 2.0 is only good for large enterprise solutions or solutions with very high volume = False Data Vault 2.0 supports the application of multiple business timelines in a multi-temporal solution = True Data Vault 2.0 was invented by Dan Linstedt and co-founder of Scalefree to build decentralised data analytics platforms = False Data Vault 2.0 allows for the physical deletion of data by privacy class = True</p> Signup and view all the answers

<p>Very large data warehousing (VLDW) environments = Data Vault 2.0 has been designed for such environments Massively parallel processing (MPP) platforms = Data Vault 2.0 can be deployed on these platforms, but it is not a requirement Single-node systems = Data Vault 2.0 can scale down to such systems, such as Azure SQL DB</p> Signup and view all the answers

<p>Raw Data Vault = A part of the data analytics platform that can be 100% automated Tracer bullet approach = An approach in Agile software development that is used when implementing the Data Vault 2.0 model Three pillars (methodology, architecture, and modeling) = The foundation of the Data Vault 2.0 concept Zero-change impact on code = A characteristic of Data Vault 2.0 that benefits clients with high auditability requirements</p> Signup and view all the answers

<p>Scalability = Ability of Data Vault 2.0 to handle additional requirements as the client's needs grow Pattern-based implementation = Allows for automation of the data warehouse, which is helpful when the client processes hundreds or thousands of source tables Multi-tenancy = Allows the client to capture and report on data from different organizations or entities Data variety = Supports the addition of thousands of tables over time</p> Signup and view all the answers

<p>Data Vault 2.0 is a scalable business intelligence solution = True Data Vault 2.0 can be used to implement data-driven solutions with high auditability requirements = True Data Vault 2.0 supports real-time processing and security as additional features = True Data Vault 2.0 is not suitable for agile delivery of data-driven solutions = False</p> Signup and view all the answers

<p>Data volume and speed = Data Vault 2.0 systems can process any volume of data at any speed, and well-implemented solutions can linearly scale with the data volume or speed Data variety = Data Vault 2.0 implementation is pattern-based, and automation tools can rely on this to handle the addition of thousands of tables Multi-lingual platforms = Data Vault 2.0 supports the implementation of multi-lingual platforms that cover not only the labels in a dashboard but also the data and information itself Scalability = Data Vault 2.0 concept allows many client solutions to start small and grow with additional requirements</p> Signup and view all the answers

<p>Massively parallel processing (MPP) platforms = Data Vault 2.0 can be deployed on these platforms, but it is not a requirement Single-node systems = Data Vault 2.0 can scale down to such systems, such as Azure SQL DB Very large data warehousing (VLDW) environments = Data Vault 2.0 has been designed for such environments</p> Signup and view all the answers

<p>Structured data = Relational database applications Semi-structured data = Real-time feeds, Web-Service and REST APIs or semi-structured applications and their databases Unstructured data = Images, PDFs and video files or streams Data from multiple sources = Internal systems or external sources, for example, purchased data</p> Signup and view all the answers

<p>Independent nightly batches = Structured data CDC loads = Semi-structured data Near real-time or in actual real-time = Unstructured data No specific loading cycle = Data from multiple sources</p> Signup and view all the answers

<p>Methodology = A set of principles and practices used to guide the process of building a modern analytics solution Architecture = The overall design and structure of a modern data analytics platform Modelling = The process of creating a representation of a real-world system in Data Vault 2.0 Data Vault 2.0 = A concept that provides a new approach for building a modern data analytics platform</p> Signup and view all the answers

<p>Not limited to one single source system or type of data = Data from multiple sources can be extracted and loaded into the platform Structured, semi-structured or unstructured data = Different types of data can be processed in the platform Delivered in various loading cycles = Different loading cycles can be used to deliver data to the platform Context = Additional information that provides meaning to the data in the platform</p> Signup and view all the answers

<p>Structured data = Formatted according to a specific schema, such as a table in a relational database Semi-structured data = Does not conform to a specific schema, but has some form of structure Unstructured data = Has no specific format or structure, such as images or video files Data from multiple sources = Can have different formats depending on the source system</p> Signup and view all the answers

<p>Data Warehouse = A central repository of integrated data from one or more sources Information Delivery = The process of providing data and information to users in a usable format Scalable = The ability to handle increasing amounts of data and users without significant performance degradation Business Intelligence = The set of techniques and tools used to transform raw data into meaningful and useful information for business analysis</p> Signup and view all the answers

<p>Source system = The system that generates or holds the data to be loaded into the platform Loading cycle = The frequency and method by which data is loaded into the platform Data type = The format or structure of the data, such as structured, semi-structured, or unstructured Context = Additional information that provides meaning to the data in the platform</p> Signup and view all the answers

<p>Data Loading = The process of loading all the required data into the data analytics platform Data Transformation = The process of converting raw data from the source into actionable information Data Consumption = The process of using the transformed information by the end-users Data Distribution = The process of distributing the data analytics platform across different environments</p> Signup and view all the answers

<p>Data Vault 2.0 Model = A modelling approach that allows full data lineage at the attribute level Data Vault 2.0 Architecture = An architecture that supports the implementation of enterprise-wide data analytics platforms Data Vault 2.0 Methodology = A methodology used to implement data-driven solutions with high auditability requirements Data Vault 2.0 Implementation Practices = Practices that ensure the unmodified data still exists, contradicting legacy approaches</p> Signup and view all the answers

<p>Fully Auditable Solutions = Data Vault 2.0 is designed for such environments and allows full data lineage at the attribute level Compliance with Legal Requirements = Data Vault 2.0 supports the separation of data across different environments to physically separate compliance data Integration of Unstructured and Structured Data = Data Vault 2.0 allows the use of the integration of the data lake and relational database Application of Business Perspective on Raw Data = Data Vault 2.0 allows every information user to apply their business perspective on the raw data</p> Signup and view all the answers

<p>Raw Data = The facts that are stored in a single point of facts Unstructured Data = Data that is stored and kept on Azure Data Lake Storage Structured Data = Data that is stored in Synapse Analytics Consumer's Financial Data = An example of data that may have legal requirements to remain in a certain jurisdiction</p> Signup and view all the answers

<p>Data Lineage = The ability to prove the source of data that was used to produce some information artifact Data Warehousing = The legacy approach where data is made to conform Data-driven Solutions = Solutions that are implemented using Data Vault 2.0 for high auditability requirements Decentralised Data Analytics Platform = The platform that Dan Linstedt was tasked with building for the US government</p> Signup and view all the answers

<p>Data Vault 2.0 is a methodology, architecture, and modeling approach for building modern analytics solutions. = True Data Vault 2.0 is designed for agile delivery of data-driven solutions. = False Data Vault 2.0 is a scalable business intelligence solution. = True Data Vault 2.0 can easily absorb changes in the structure of the source system. = False</p> Signup and view all the answers

<p>Privacy Classification = Data Vault 2.0 supports the separation of attributes by this classification Multi-tenancy = A feature in Data Vault 2.0 that allows the implementation of platforms for international clients Data Security = A key aspect considered in the hybrid architecture used in Data Vault 2.0 Jurisdictional Requirements = Some clients have legal requirements that certain data remains in a certain jurisdiction</p> Signup and view all the answers

<p>Adaptability = Ability to easily absorb changes in the structure of the source system or business logic Agile Delivery = Implementation of the data analytics platform sprint-by-sprint, focusing on business value Business Timelines = Application of one or multiple timelines in a multi-temporal solution Cell-level Security = Combination of row-level and column-level security to apply usage restrictions to the data</p> Signup and view all the answers

<p>Data Vault 2.0 = A data modeling approach that provides long-term historical storage of data Tracer Bullet Approach = Shoots the functionality through the layers of the data analytics platform Zero-change Impact on Code = Ability to absorb changes without modifying existing code Multi-lingual Platforms = Support for platforms that use multiple languages</p> Signup and view all the answers

<p>Cell-level Security = A must-have in environments with high security and privacy requirements Multiple Security Classifications = Supported and used by most clients, often with two classes: personal data and non-personal data Deletion or Reduction of PII Data = A fundamental requirement in many government projects Dynamic Access Control List (ACL) = Used to apply any usage restrictions to the data and information</p> Signup and view all the answers

<p>Structured Data = Data that has a well-defined schema and is organized in a tabular format Semi-structured Data = Data that does not have a fixed schema, but has some organizational properties Unstructured Data = Data that does not have a predefined format or structure Personal Data and Non-personal Data = Two common classes used for data separation in Data Vault 2.0</p> Signup and view all the answers

<p>Data Analytics Platform = The system used to analyze data and generate insights Business Value = Something that can be used by the business user, often a report, a dashboard, or KPIs Business Logic = The rules and processes that drive the operations of a business Target Information Model = The model used to build the report, often a star schema or snowflake schema</p> Signup and view all the answers

<p>Big Bang Approach = An approach that often fails in the domain of Data Vault 2.0 implementation Data-driven Solutions = Solutions that are driven by data and utilize analytics to make informed decisions Versioning Business Logic = The process of maintaining different versions of the business logic Data Deletion by Privacy Class = The ability to independently delete the data based on its privacy classification</p> Signup and view all the answers

<p>Data Vault 2.0 Model = The data modeling approach that provides long-term historical storage of data Business User = The end user of the data analytics platform who uses the data to make business decisions Source Data = The data that is loaded into the data analytics platform from the source system Data Lake = A storage repository that holds a vast amount of raw data in its native format</p> Signup and view all the answers

<p>Data Vault 2.0 = A methodology, architecture, and modeling approach for building modern analytics solutions Multi-tenancy = A feature in Data Vault 2.0 that allows the capturing of data from different organizations, production plants, countries, etc. Pattern-based implementation = An approach used in Data Vault 2.0 where all entities follow similar patterns Scalability = A characteristic of Data Vault 2.0 that allows easy addition of additional features and functionality to the data analytics platform</p> Signup and view all the answers

<p>Azure Synapse Dedicated SQL Pool = A massively parallel processing (MPP) platform that works well with Data Vault 2.0 Azure SQL DB = A single-node system that can be used with Data Vault 2.0 Microsoft SQL Server = An on-premises system that can be used with Data Vault 2.0 MPP platforms = Not a requirement to use with Data Vault 2.0, as it also scales down to single-node systems</p> Signup and view all the answers

<p>Data lake and 100% of the raw data processing in the Raw Data Vault = An approach that can be automated in Data Vault 2.0 using defined meta-data and automation templates Processing twice the volume of data = An example of linear scalability in Data Vault 2.0, where clients only need twice the resources Transformation of all foreign currencies into a leading currency = An example of multi-currency support in Data Vault 2.0 Transformation of any currency to be selected by the user in the dashboard = An example of the flexibility of Data Vault 2.0 in supporting different user requirements</p> Signup and view all the answers

<p>Data Vault 2.0 = A methodology, architecture, and modeling approach for building modern analytics solutions Multi-tenancy = A feature that allows the capturing of data from different organizations, production plants, countries, etc. Pattern-based implementation = An approach where all entities follow similar patterns Scalability = The ability of a system to handle increased workloads or adapt to changing circumstances</p> Signup and view all the answers

<p>Very large data warehousing (VLDW) environments = Designed for by the Data Vault 2.0 concept Massively parallel processing (MPP) platforms = Can be used with Data Vault 2.0 to process large volumes of data at high speed Single-node systems = Can also be used with Data Vault 2.0, such as Azure SQL DB or Microsoft SQL Server on premises Legacy systems = Not specifically mentioned in the text as being compatible with Data Vault 2.0</p> Signup and view all the answers

<p>Multi-lingual platforms = Implemented in Data Vault 2.0 to cover not only the labels in a dashboard but also the data and information itself for international clients Multi-currency support = Implemented in Data Vault 2.0 to transform all foreign currencies into a leading currency, or any currency to be selected by the user in the dashboard Multi-tenancy = Implemented in Data Vault 2.0 to capture data from different organizations, production plants, countries, etc. defined as tenants Scalability = A characteristic of Data Vault 2.0 that allows the easy addition of additional features and functionality to the data analytics platform</p> Signup and view all the answers

<p>Data Vault 2.0 is only good for large enterprise solutions or solutions with very high volume = False Data Vault 2.0 is a scalable business intelligence solution = True Data Vault 2.0 is designed to conform to legacy approaches where data is made to conform = False Data Vault 2.0 supports the application of multiple business timelines in a multi-temporal solution = True</p> Signup and view all the answers

<p>Data Vault 2.0 = A methodology, architecture, and modeling approach for building modern analytics solutions Multi-tenancy = A feature in Data Vault 2.0 that allows the capturing of data from different organizations, production plants, countries, etc. Pattern-based implementation = An approach used in Data Vault 2.0 where all entities follow similar patterns Scalability = A characteristic of Data Vault 2.0 that allows easy addition of additional features and functionality to the data analytics platform</p> Signup and view all the answers

<p>Scalability = A characteristic of Data Vault 2.0 that allows it to start small with additional requirements added later due to growing needs Pattern-based implementation = An approach used in Data Vault 2.0 where all entities follow similar patterns, making automation easier Multi-tenancy = A feature in Data Vault 2.0 that allows the capturing of data from different organizations, production plants, countries, etc. defined as tenants Compatibility with different types of systems = A characteristic of Data Vault 2.0 that allows it to work with both on-premises and cloud-based systems</p> Signup and view all the answers