6.0
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6.0.2
Unified some calculation logics of direct-connected data and extracted data. For details, see the "Calculation Logic of Direct-Connected Data and Extracted Data in Components" section.
This document introduces the definitions of direct-connected data and extracted data and the differences between these two kinds of data.
If you are unsure whether a direct connection or extraction mode better fits your business environment, you can conduct a quick assessment via a tool. You can contact our technical support to obtain the tool.
Direct-Connected Data
If you use direct connection datasets, FineBI will directly use data in your database for calculations.
Extracted Data
If you use extracted data, data in the database will be extracted to FineBI (similar to saving data to FineBI). Therefore, data in the database and data in FineBI are not continuously synchronous. You need to regularly update data in FineBI so that data can be consistent with that in the database.
Since data is extracted and saved to the FineBI engine, enough space in the local disk is required in the Extracted Data mode.
Target Users of Direct-Connected Data
Users with Big Data Platforms
If you (as many enterprise clients) currently have professional big data platforms with high data quality, you can retrieve data through the direct connection engine to ensure the data analysis performance and avoid data resource redundancy.
Users with High Real-Time Requirements
If you have high real-time requirements during business analyses, you can retrieve data in real time through the direct connection engine to achieve a millisecond-level data refresh.
Users with High Data Security Requirements
If you do not want to extract data into third-party software, you can directly connect FineBI to your database through the new direct connection version.
Users with Small Data Volumes
The performance requirement for direct data is higher than that for extracted data. However, if the data volume is small, the performance will not be affected. In this case, you can use the direct connection engine to avoid additional data updates.
Users with Large User Volumes and High Concurrency
Large user volumes may result in the proliferation of tables and self-service datasets, leading to troublesome updates. In this case, you can use the direct connection engine to avoid updates.
Target Users of Extracted Data
If you need to perform joint analyses through data from multiple databases, you can use extracted data. The direct connection version cannot allow you to perform joint analyses (such as creating associations and setting Join and Union All) through data from multiple databases.
Influence of the filtering and quick calculation on the summary value
No influence
Influence of one quick calculation on other quick calculation indicators
Influence of one quick calculation on the summary values of other quick calculations
Dimension filtering/sorting (relying on the total row) based on indicators
The system relies on the automatically configured total row.
Filtering logic of the cross table
Each filtering is performed separately.
The filtering (with the flattened level) of the result filter and the header
The filtering of the result filter is performed first, and then the filtering of the header is performed.
The filtering of the result filter and the header is at the same level.
Different filtering logics for null and empty strings
If you choose one kind of data (belonging to either null or empty strings), both of these two kinds of data will be filtered.
Data belonging to null and empty strings corresponds to different filtering logics. For example, if you choose data belonging to empty strings, only this kind of data can be filtered, with data belonging to null uninfluenced, and vice versa.
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