数据库科学计数法转换_数据转换| 数据科学

雨点打透心脏的1/2处 2023-03-05 11:58 28阅读 0赞

数据库科学计数法转换

## 数据转换 **(**Data Transformation**)** ##

**Data transformation** is the method of changing knowledge from one format or structure into another format or structure. **Data transformation** is crucial to activities like data integration and data management. Data transformation will embody a variety of activities: you may convert data sorts, cleanse data by removing nulls or duplicate data, enrich the info, or perform aggregations, reckoning on the wants of your project.

**数据转换**是将知识从一种格式或结构更改为另一种格式或结构的方法。 **数据转换**对于诸如数据集成和数据管理之类的活动至关重要。 数据转换将包含各种活动：您可以转换数据类型，通过删除空值或重复数据来清理数据，丰富信息或执行聚合，根据项目需求进行计算。

**Typically, the method involves 2 stages,**

**通常，该方法涉及2个阶段，**

**In the initial stage**, you:

**在初始阶段** ，您：

Perform **data discovery** wherever you establish the sources and data sorts.  
Determine the structure and **data transformations** that require to occur.  
Perform **data mapping** to outline however individual fields are mapped, modified, joined, filtered, and aggregative.

无论您在何处建立源和数据类别，都可以执行**数据发现** 。  
确定需要发生的结构和**数据转换** 。  
执行**数据映射**以概述但可以映射，修改，合并，过滤和汇总各个字段。

**In the second stage**, you:

**在第二阶段** ，您：

**Extract data** from the first supply. The vary of sources will vary, together with structured sources, like databases, or streaming sources, like measurement from connected devices, or log files from customers victimization your internet applications.

从第一个供给中**提取数据** 。 来源的变化将与结构化的来源(如数据库)或流式来源(如来自已连接设备的测量结果)或来自受Internet应用程序侵害的客户的日志文件一起变化。

**Perform transformations -**You rework the info, like aggregating sales knowledge or changing date formats, piece of writing text strings, or connection rows and columns.

**执行转换-**您重新整理信息，例如汇总销售知识或更改日期格式，编写文本字符串或连接行和列。

Send the info to the target store. The target can be a knowledge base or a data warehouse that handles structured and unstructured data.

将信息发送到目标商店。 目标可以是处理结构化和非结构化数据的知识库或数据仓库。

### 为什么要返工数据？ **(**Why rework Data?**)** ###

You might need to remodel your knowledge for a variety of reasons. Generally, businesses need to remodel knowledge to create it compatible with different data, move it to a different system, be a part of it with different knowledge, or mixture data within the knowledge.

出于各种原因，您可能需要重塑知识。 通常，企业需要重塑知识以创建与不同数据兼容的知识，将其移至不同的系统，以不同的知识成为其一部分，或在知识中混合数据。

For example, contemplate the subsequent scenario: your company has purchased a smaller company, and you wish to mix data for the Human Resources departments. The purchased company uses distinct info than the parent company, thus you'll get to do some work to make sure that these records match. Every of the new staff has been issued an Associate in Nursing worker ID, thus this could function a key. But, you'll get to modification the information for the dates, you'll get to take away any duplicate rows, and you'll get to make sure that there aren't any null values for the worker ID field so that all staff is accounted for. These crucial functions are performed in an exceedingly area before you load the info to the ultimate target.

例如，考虑以下情况：您的公司购买了一家较小的公司，并且您希望混合人力资源部门的数据。 所购买的公司使用的信息与母公司使用的信息不同，因此，您将需要做一些工作来确保这些记录匹配。 每位新员工都被授予了一名护理人员助理ID，因此这可以起到关键作用。 但是，您将需要修改日期信息，删除所有重复的行，并确保“工作人员ID”字段没有空值，以便所有工作人员被占。 在将信息加载到最终目标之前，这些关键功能会在非常广阔的区域中执行。

### 重塑数据的其他常见原因包括 **(**Other common reasons to remodel data include**)** ###

You are moving your data to a replacement data store; for instance, you're moving to a cloud data warehouse and you wish to alter the info sorts.

您正在将数据移动到替换数据存储中； 例如，您正在迁移到云数据仓库，并且希望更改信息种类。

You want to affix unstructured data or streaming data with structured data thus you'll be able to analyze the info along.

您希望将非结构化数据或流数据与结构化数据一起附加，这样您就可以分析信息。

You want to feature data to your knowledge to counterpoint it, like acting lookups, adding geolocation knowledge, or adding timestamps.

您想对知识进行特征化处理以对付数据，例如进行查找，添加地理位置知识或添加时间戳。

You want to perform aggregations, like comparison sales knowledge from totally different regions or totaling sales from different regions.

您想要执行汇总，例如比较来自完全不同地区的销售知识或汇总来自不同地区的总销售额。

### 数据如何转换？ **(**How Is Data Transformed?**)** ###

**There are many alternative ways to remodel data**:

**有许多替代方法可以重塑数据** ：

Scripting. Some firms perform data recordation via scripts victimization SQL or Python to jot down the code to extract and transform the info.

脚本编写。 一些公司通过脚本受害者SQL或Python执行数据记录，记下代码以提取和转换信息。

On-premise ETL tools: **ETL (Extract, Transform, Load)** tools will take a lot of the pain out of scripting the transformations by automating the method. These tools are usually hosted on your company's web site and will need intensive experience and infrastructure prices.

本地ETL工具： **ETL(提取，转换，加载)**工具将通过自动化方法来编写转换脚本，从而减轻了很多麻烦。 这些工具通常托管在您公司的网站上，并且需要丰富的经验和基础架构价格。

Cloud-based ETL tools. These **ETL** tools are hosted within the cloud, wherever you'll be able to leverage the experience and infrastructure of the seller.

基于云的ETL工具。 这些**ETL**工具托管在云中，您可以在任何地方利用卖方的经验和基础架构。

### 数据转换挑战 **(**Data Transformation Challenges**)** ###

**Data transformation may be troublesome for a variety of reasons**:

**由于多种原因，数据转换可能很麻烦** ：

*  **Time-consuming**:
    
    **耗时** ：
    
    you'll get to extensively cleanse the info thus you'll be able to rework or migrate it. This could be extraordinarily long and could be a common grievance amongst knowledge scientists operating with unstructured data.
    
    您将需要彻底清理信息，从而可以进行返工或迁移。 这可能会非常长，并且可能是使用非结构化数据进行操作的知识科学家之间的共同抱怨。
 *  **Costly**:
    
    **昂贵的** ：
    
    Reckoning on your infrastructure, remodeling your knowledge might need a team of specialists and substantial infrastructure prices.
    
    估算您的基础架构，重塑您的知识可能需要一个专家团队和大量的基础架构价格。
 *  **Slow**:
    
    **慢** ：
    
    As a result of the method of extracting and reworking knowledge may be a burden on your system, it's typically worn-out batches, which implies you'll get to wait up to twenty-four hours for the successive batch to be processed. This could value your time in creating business selections.
    
    由于提取和重做知识的方法可能会给您的系统带来负担，因此通常是破旧的批次，这意味着您将需要等待多达24小时才能处理连续的批次。 这可以珍惜您的时间来创建业务选择。

> 翻译自: [https://www.includehelp.com/data-science/data-transformation.aspx][https_www.includehelp.com_data-science_data-transformation.aspx]

数据库科学计数法转换

[https_www.includehelp.com_data-science_data-transformation.aspx]: https://www.includehelp.com/data-science/data-transformation.aspx