Overview
Version
| FineBI Version | Adjusted Function |
|---|---|
V6.0 | / |
6.0.2 | Unified some calculation logics of direct-connected data and extracted data. For details, see section "Calculation Logic of Direct Data and Extracted Data in Components." |
Application Scenario
This document introduces the definitions of direct-connected data and extracted data and the differences between these two kinds of data.
Introduction to Direct-Connected Data and Extracted Data
Difference
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
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 the effect of 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 with 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 (such as creating associations and setting Join and Union All) through data from multiple databases, which cannot be achieved through the direct connection version, you can use extracted data.
Calculation Logic of Direct-Connected Data and Extracted Data in Components
Calculation Logic in the Same Scenario
| Calculation Logic | In the Extraction Mode | In the Direct Connection Mode |
|---|---|---|
The influence of quick calculation filtering on the total value | The total value is not influenced. | The total value is not influenced. |
The influence of quick calculations on other quick calculation indicators. | Other quick calculation indicators are not influenced. | Other quick calculation indicators are not influenced. |
The influence of quick calculations on total values of other quick calculations | Total values of other quick calculations are not influenced. | Total values of other quick calculations are not influenced. |
Dimension filtering/sort (relying on the total row) based on indicators | The system relies on the automatically-configured total row. | The system relies on the automatically-configured total row. |
The filtering logic of the cross table | Each filtering is performed separately. | 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 data belonging to 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. |
