35 Questions
What is the current exception in the Flow.BI architecture?
Repository based on PostgreSQL
Which component of Flow.BI runs in the cloud or on premise?
Repository
What type of architecture is used in Flow.BI for its components?
Distributed Data Vault 2.0 architecture
Flow.BI's repository is not based on PostgreSQL.
False
Flow.BI's repository is used to store metadata and results.
True
Flow.BI's distributed Data Vault 2.0 architecture is not based on Massively Parallel Processing (MPP).
False
Match the following components of Flow.BI with their descriptions:
MPP (Massively Parallel Processing) = Basis for every component except the repository PostgreSQL = Used for the repository with one writer instance and multiple reader instances Repository = Stores metadata and results, runs in the cloud or on premise Distributed Data Vault 2.0 architecture = Architecture used for Flow.BI's components
Match the following characteristics with their description in Flow.BI architecture:
One writer instance, multiple reader instances = Characteristic of PostgreSQL usage in Flow.BI Configurability of host name = Applies to testing and load balancing in Flow.BI Cloud or on premise deployment = Applies to the repository in Flow.BI MPP (Massively Parallel Processing) = Basis for every component except the repository in Flow.BI
Match the following terms with their association in Flow.BI architecture:
Flow.BI components = Based on MPP (Massively Parallel Processing) except the repository PostgreSQL = Used for the repository and supports one writer instance and multiple reader instances Distributed Data Vault 2.0 architecture = The architecture used in Flow.BI's components Repository = Stores metadata and results, and can run in the cloud or on premise
What is the main characteristic of an MPP cluster?
Each node has its own CPU, memory, and storage
Why are MPP clusters well-suited for applications that require a high degree of parallelism?
They can distribute the workload across multiple nodes
What benefit makes MPP clusters a good choice for applications that require a lot of processing power?
Scalability
What makes MPP clusters more cost-effective than traditional mainframes and supercomputers?
They can be built using commodity hardware
Which application is often associated with the use of MPP clusters?
Data warehousing
What is the primary function of MPP clusters in data warehousing?
Storing and analyzing large amounts of data
What does each node in an MPP cluster have?
CPU, memory, and storage
How do nodes in an MPP cluster communicate with each other?
Using a high-speed network
What makes MPP clusters suitable for scientific computing?
High degree of parallelism
What characteristic allows MPP clusters to provide very high levels of performance?
Distributing the workload across multiple nodes
Why are MPP clusters often more cost-effective than traditional mainframes and supercomputers?
They can be built using commodity hardware
True or false: MPP clusters consist of interconnected nodes that work together to process data in series, not in parallel?
False
True or false: Each node in an MPP cluster has its own CPU, memory, and storage?
True
True or false: MPP clusters are not suitable for applications that require a high degree of parallelism?
False
True or false: MPP clusters can be easily scaled by adding more nodes, making them a good choice for applications that require a lot of processing power?
True
True or false: MPP clusters are often more cost-effective than traditional mainframes and supercomputers because they require specialized and expensive hardware?
False
True or false: MPP clusters are well-suited for scientific computing due to their ability to distribute the workload across multiple nodes?
True
True or false: MPP clusters are not commonly used for data warehousing applications?
False
True or false: MPP clusters can provide very high levels of performance due to their ability to distribute the workload across multiple nodes?
True
True or false: MPP clusters can communicate with each other using a low-speed network?
False
True or false: MPP clusters are often used to store and analyze large amounts of data in data warehousing applications?
True
True or false: MPP clusters are not cost-effective because they can be built using commodity hardware?
False
True or false: MPP clusters are not suitable for big data analytics applications?
False
Match the following benefits with their descriptions of MPP clusters:
Scalability = MPP clusters can be easily scaled by adding more nodes Performance = MPP clusters can provide very high levels of performance Cost-effectiveness = MPP clusters are often more cost-effective than traditional mainframes and supercomputers Reliability = MPP clusters can distribute the workload across multiple nodes, improving performance
Match the following applications with their usage of MPP clusters:
Data warehousing = MPP clusters are often used to store and analyze large amounts of data Big data analytics = MPP clusters are well-suited for applications that require a high degree of parallelism Scientific computing = MPP clusters are suitable for scientific computing applications Web development = MPP clusters are not commonly used for web development purposes
Match the following components with their description in an MPP cluster:
CPU, memory, and storage = Each node in an MPP cluster has its own CPU, memory, and storage High-speed network = Nodes in an MPP cluster communicate with each other using a high-speed network Interconnected nodes = MPP clusters consist of interconnected nodes that work together to process data in parallel Commodity hardware = MPP clusters can be built using commodity hardware, which is relatively inexpensive
Learn about the architecture of Flow.BI, which is based on Massively Parallel Processing (MPP) with components like PostgreSQL and Distributed Data Vault 2.0. Explore the use of repository for storing metadata and results, along with the configuration for testing and load balancing.
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