DataWorks allows you to view, modify, and delete your MaxCompute, AnalyticDB for PostgreSQL, and E-MapReduce (EMR) tables. You can view and manage these tables by type on the Workspace Tables tab of the DataStudio page.

Prerequisites

  • To manage MaxCompute tables, you must associate a MaxCompute compute engine instance with your workspace. For more information, see Configure a workspace.
  • To manage AnalyticDB for PostgreSQL tables, you must associate an AnalyticDB for PostgreSQL compute engine instance with your workspace and collect the metadata of the AnalyticDB for PostgreSQL compute engine instance on the DataMap page in the DataWorks console. For more information, see Configure a workspace and Collect metadata from an AnalyticDB for PostgreSQL data source.
  • To manage EMR tables, you must associate an EMR compute engine instance with your workspace and collect the metadata of the EMR compute engine instance on the DataMap page in the DataWorks console. For more information, see Configure a workspace and Collect metadata from an EMR data source.

Manage tables

  1. Go to the DataStudio page.
    1. Log on to the DataWorks console.
    2. In the left-side navigation pane, click Workspaces.
    3. In the top navigation bar, select the region where your workspace resides, find the workspace, and then click Data Analytics in the Actions column.
  2. In the left-side navigation pane, click the Workspace Tables icon.
    You can click the More icon in the lower-left corner to show or hide the names of tabs in the left-side navigation pane.
  3. Select an engine type from the drop-down list in the upper part of the Workspace Tables tab.
    Workspace Tables
    In this example, MaxCompute is selected. The following table describes how to view, modify, and delete a MaxCompute table. For more information about how to create a table, see Create a MaxCompute table.
    Operation Description
    View a table You can click the Filter icon next to the search box and select an environment.
    Note
    • For a workspace in standard mode, tables in both the development and production environments are displayed.

      For a workspace in basic mode, only tables in the production environment are displayed.

    • The current environment is highlighted in blue.

    Double-click a table. The configuration tab of the table appears and displays the details of the table.

    Rename a table In a workspace in standard mode, right-click a table and select Rename Table. In the Rename Table dialog box, enter a name and click OK.
    Note You cannot right-click a table in the production environment and rename the table. Therefore, tables in workspaces in basic mode do not support this operation.
    Import data to a table Right-click a table and select Import Data. For more information, see Create tables and import data.
    Delete a table You can delete a table in either the development or production environment.
    • Delete a table in the development environment:
      1. Before you delete a table, make sure that you have permissions to delete the table. Right-click a table and select Delete Table.
      2. In the Delete Table dialog box, select the I have been aware of the risk and confirm the deletion check box.
      3. Click OK.
      Note You cannot right-click a table in the production environment and delete the table. Therefore, tables in workspaces in basic mode do not support this operation.

      If the name of a table starts with tmp_pyodps, the table is of the PyODPS type. For more information, see Create a PyODPS 2 node.

    • Delete a table in the production environment:
      1. Method 1: Create an ODPS SQL node, enter the command that is used to delete the table, and then commit and deploy the node to the production environment to run the node. For more information, see Create an ODPS SQL node.
      2. Method 2: Delete the table on the My Data tab of the DataMap page. Delete a table

Divide a data warehouse into layers

In the Physical Model section of the configuration tab of a table, you can define table layers for a data warehouse. This enables you to have better planning and control over your data.

In most cases, a data warehouse consists of the following layers:
  • The ODS layer stores raw data in the data warehouse. The data structure is basically consistent with that in the source system. The ODS layer serves as the data staging area of the data warehouse. It imports basic data to MaxCompute and records historical changes of basic data.
  • The CDM layer, which is also called the general data model layer, consists of the dimension data (DIM), data warehouse detail (DWD), and data warehouse service (DWS) layers. The CDM layer processes and integrates the data of the ODS layer to define conformed dimensions, create reusable detailed fact tables for analysis and statistics, and aggregate common metrics.
    • The DIM layer defines conformed dimensions for an enterprise based on the concepts of dimensional modeling. It reduces the risk of inconsistent statistical criteria and algorithms.

      Tables at the DIM layer are also called logical dimension tables. Generally, each dimension corresponds to a logical dimension table.

    • The DWS layer is driven by analyzed subjects during data modeling. Based on the metric requirements of upper-layer applications and products, the DWS layer creates fact tables to aggregate common metrics and builds a physical data model by using wide tables. The DWS layer creates statistical metrics in compliance with uniform naming conventions and statistical criteria, provides common metrics for the upper layer, and generates aggregate wide tables and detailed fact tables.

      Tables at the DWS layer are also called logical aggregate tables, which are used to store derived metrics.

    • The DWD layer is driven by business processes during data modeling. It creates detailed fact tables at the finest granularity based on each specific business process. In combination with the data usage habits of an enterprise, you can duplicate some key attribute fields of dimensions in detailed fact tables to create wide tables.

      Tables at the DWD layer are also called logical fact tables.

  • The ADS layer stores personalized statistical metrics of data products. It processes the data of the CDM and ODS layers.