[01/Powder/01]

Any JDBC driver

It breaks them into business keys, relationships, and descriptive data

To generate a logical model

True

True

False

JDBC driver = Fundamentally every Salesforce CRM = Must support Mailchimp = Must support Semi-structured data (JSON, XML) = Supported, as well

Real-time data (Kafka) = Supported Logical model = Generated by Flow.BI Business keys, relationships, descriptive data = Generated from real-time messages in the queue Physical model = Not generated by Flow.BI

Breaks the real-time messages in the queue = Flow.BI Generates the logical model = Flow.BI Generates the physical model = Not Flow.BI Supports any JDBC driver = Flow.BI

JSON = A text-based data format that is easy to read and write, supported by all major programming languages XML = A markup language that is more complex than JSON, but more powerful and flexible, supported by all major programming languages and databases

JSON = JavaScript Object Notation XML = Extensible Markup Language

Based on JavaScript objects = JSON More powerful and flexible = XML

JSON = Less complex than XML XML = More complex than JSON

JSON

XML

XML

JSON

False

True

False

False

True

True

True

True

Historizing data over time

Abstraction of data records

Configuring data sources

Flow.BI works only with the data itself

Defining connections to source systems

To analyze past data and changes

Once a day

A connection to a remote system used as a source and target

Data extraction is performed as soon as possible

Having multiple connections to the same data source

Data is permanently deleted

To add a new data source

Knowledge of existing relations

Both meta data and data

Data extraction is performed via the scheduler

Semi-structured data formats

Data profiling and analysis

Both the logical model and the physical model

To build an enterprise-grade data analytics platform

To guarantee data security and privacy

True

True

True

True

True

True

True

False

False

True

True

False

False

True

False

True

True

True

False

True

Data Source = A connection to a typical source system or a generic relational database application Deployment Target = A connection to a remote system that can be used as a source of data and a target for the final solution Meta Data = Describes the structure of the data Data Extraction = The process of retrieving both meta data and data from a data source

JSON = Easy to read and write, supported by all major programming languages and databases XML = More complex but powerful and flexible, supported by all major programming languages and databases

Adding new Data Sources = The first step after setting up the instance, involves defining connections to source systems or databases Scheduling a new data source with priority = A way to ensure immediate extraction when a source cannot provide all data in one connection

Right to be forgotten = A feature provided by Flow.BI to implement data security and privacy guidelines Historizing data over time = A feature provided by Flow.BI to meet the requirements of enterprise organizations

Data Security and Privacy Guidelines = Enforced by Flow.BI to control access to data Data Profiling = Allowed by data retention in Flow.BI to analyze past data and changes Real-time messages in the queue = Handled by Flow.BI

Meta data = Abstraction of actual data that describes the structure of the data Data records = Actual customer and product records, such as 'John Smith' and 'Milk 1.5%' Data source extraction = Process of retrieving data from a source for analysis in Flow.BI Data retention = Storage of a few subsequent data loads to allow data profiling for past data and changes in between

Add New Source = Action to connect a new data source to the Flow.BI instance Data profiling = Process of analyzing data to understand its structure and content Scheduling = Process of setting up a regular extraction frequency for a data source Deployment target = Connection to a remote system that can be used as a source of data and a target for the final solution

Immediate extraction = Process of extracting a new data source with priority, performed as soon as possible via the scheduler Regular job = Data source extraction scheduled at a specific time, with the start time not guaranteed Minimal meta data = Only the knowledge of which relations (tables, files, documents) exist Data access = Always required for Flow.BI to work, even with minimal meta data

Data source removal = Process that leads to permanent deletion of the data for this source in Flow.BI Optimized data format = Format in which the data will be temporarily stored for data profiling Job execution = Process of performing a data source extraction as soon as possible, but via the scheduler Data processing agreement = Document that contains details about the handling of data between Flow.BI and the organization

Source with meta data = Data source that provides information about its structure Source without meta data = Data source, such as CSV files, that is provided without meta data Data source extraction start time = Time at which a regular job starts in Flow.BI, not guaranteed Data source extraction priority = Status given to a new data source to speed up the initial process in Flow.BI

Data source extraction = Data is extracted from a source and temporarily stored in a format optimized for data profiling Data source removal = Data for the removed source is permanently deleted in Flow.BI Scheduling a new data source with priority = Data source is extracted immediately and with priority over other scheduled jobs Data retention = A few subsequent data loads are stored to allow data profiling for past data and changes in between

Abstraction of data = Meta data that describes the structure of the actual data Data records = Actual customer and product records that are defined as data Data access = Always required by Flow.BI, even with minimal meta data Minimal meta data = Sufficient knowledge of which relations exist in the data

Immediate extraction = Process of extracting a new data source with priority, performed as soon as possible via the scheduler Regular job = Data source extraction scheduled at a specific time, with the start time not guaranteed Minimal meta data = Only the knowledge of which relations (tables, files, documents) exist Data access = Always required for Flow.BI to work, even with minimal meta data

Data source extraction = Data is extracted from a source and temporarily stored in a format optimized for data profiling Data source removal = Data for the removed source is permanently deleted in Flow.BI Scheduling a new data source with priority = Data source is extracted immediately and with priority over other scheduled jobs Data retention = A few subsequent data loads are stored to allow data profiling for past data and changes in between

Abstraction of data = Meta data that describes the structure of the actual data Data records = Actual customer and product records that are defined as data Data access = Always required by Flow.BI, even with minimal meta data Minimal meta data = Sufficient knowledge of which relations exist in the data