数据库 vs 数据集市 vs 数据仓库 vs 数据湖答案

【问题标题】：Database vs DataMart vs Data Warehouse vs Data Lake数据库 vs 数据集市 vs 数据仓库 vs 数据湖
【发布时间】：2020-05-12 12:23:31
【问题描述】：

寻找高层之间的差异/比较

数据库
数据集市（自上而下的方法）
数据仓库
数据湖

如果没有具体情况，请使用相对比较。

【问题讨论】：

标签： database comparison data-warehouse data-lake datamart

【解决方案1】：

下面是提到的各种数据层之间的高级比较。如果其中任何一个需要更正，请随时发表评论。

注意：执行HTML查看结果

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  font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
  border-collapse: collapse;
  width: 100%;
}

#dataTierComparison td,
#dataTierComparison th {
  border: 1px solid #ddd;
  padding: 8px;
}

#dataTierComparison tr:nth-child(even) {
  background-color: #f2f2f2;
}

#dataTierComparison tr:hover {
  background-color: #ddd;
}

#dataTierComparison th {
  padding-top: 12px;
  padding-bottom: 12px;
  text-align: left;
  background-color: #4CAF50;
  color: white;

<table id="dataTierComparison">
  <tbody>
    <tr>
      <th> </th>
      <th>Database</th>
      <th>Data Mart (Top-down)</th>
      <th>Data Warehouse</th>
      <th>Data Lake</th>
    </tr>
    <tr>
      <th>Source</th>
      <td>Single</td>
      <td>Single</td>
      <td>Multiple</td>
      <td>Multiple</td>
    </tr>
    <tr>
      <th>Structure</th>
      <td>Structured</td>
      <td>Structured</td>
      <td>Structured</td>
      <td>Raw</td>
    </tr>
    <tr>
      <th>Purpose</th>
      <td>Determined</td>
      <td>Determined</td>
      <td>Determined</td>
      <td>Undertermined</td>
    </tr>
    <tr>
      <th>Storage</th>
      <td>Centralized</td>
      <td>Decentralized</td>
      <td>Centralized</td>
      <td>Centralized</td>
    </tr>
    <tr>
      <th>Data Format</th>
      <td>Detailed</td>
      <td>Summarized</td>
      <td>Detailed</td>
      <td>All</td>
    </tr>
    <tr>
      <th>Flexibility</th>
      <td>Low</td>
      <td>Medium</td>
      <td>Medium</td>
      <td>High</td>
    </tr>
    <tr>
      <th>Primary Use</th>
      <td>Transactional</td>
      <td>Reporting</td>
      <td>Analytics &amp; Reporting</td>
      <td>Analytics</td>
    </tr>
    <tr>
      <th>Cost</th>
      <td>Low</td>
      <td>Medium</td>
      <td>Medium</td>
      <td>High</td>
    </tr>
    <tr>
      <th>Data Volume</th>
      <td>Low</td>
      <td>Low</td>
      <td>Medium</td>
      <td>High</td>
    </tr>
    <tr>
      <th>Development</th>
      <td>Top-down</td>
      <td>Bottom-up</td>
      <td>Top-down</td>
      <td>All</td>
    </tr>
    <tr>
      <th>Design Time</th>
      <td>Medium</td>
      <td>Medium</td>
      <td>High</td>
      <td>Low</td>
    </tr>
    <tr>
      <th>Volatility</th>
      <td>Medium</td>
      <td>Low</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>Data Operations</th>
      <td>CRUD</td>
      <td>CR</td>
      <td>CRU</td>
      <td>CR</td>
    </tr>
    <tr>
      <th>Subject Area</th>
      <td>Single</td>
      <td>Single</td>
      <td>Multiple</td>
      <td>Multiple</td>
    </tr>
    <tr>
      <th>Design Schema</th>
      <td>Relational</td>
      <td>Multi-dimensional</td>
      <td>Relational</td>
      <td>No Schema</td>
    </tr>
  </tbody>
</table>

【讨论】：