You can use an aggregate table to store statistical data of multiple derived metrics that have the same period and dimension in a data domain. An aggregate table can be used for subsequent business query, online analytical processing (OLAP) analysis, and data distribution. This topic describes how to create an aggregate table.

Prerequisites

  • A data layer is created. Each data layer stores tables that serve the same purpose. This helps you easily find and use tables. In most cases, an aggregate table is stored at the data warehouse summary (DWS) layer and contains statistical data of multiple derived metrics in a dimension or a set of dimensions. The statistical data can be used for subsequent business query and data distribution. You can also store aggregate tables at other data layers based on your business requirements. For more information about how to create a data layer, see Create a data layer.
  • A data domain is created. An aggregate table is created based on a data domain. The data domain determines the business category based on which you want to build a model and is used to bear business processes. For more information about how to create a data domain, see Data domain.

Background information

An aggregate table integrates multiple derived metrics in a data domain based on the period and associated dimension that you specified. The associated dimension, period, and derived metrics are used to generate statistical fields in the aggregate table to help you analyze and generate reports. You can use an aggregate table to display the status of your business based on the statistics of multiple derived metrics that have the same period and same dimension.

Procedure

  1. Go to the Data Modeling page.
  2. Go to the Dimensional Modeling page.
    In the top navigation bar of the Data Modeling page, click Dimensional Modeling.
  3. Create an aggregate table.
    On the Dimensional Modeling page, click the Plus sign icon, select Create Aggregate Table, and then specify the parameters that are described in the following table. Basic Information
    Parameter Description Example
    Data Layer The data layer that stores the aggregate table. DWS is selected by default. You can also store aggregate tables at other data layers based on your business requirements. For more information about how to create a data layer, see Create a data layer. DWS layer
    Business Category Set the parameter to the created business category. For more information, see Business category. Sales
    Data Domain Set the parameter to the data domain of the aggregate table. The data domain determines the theme of the aggregate table after data aggregation.
    Note Each aggregate table can belong to only one data domain.
    		Transaction
    Period The period during which the statistical data that is to be aggregated is generated. Examples: last day and last week.

    Select a period from the existing periods. If the existing periods do not meet your business requirements, you can create a period. For more information about how to create a period, see Period.

    		Last week
    Naming Rule Set the parameter to the checker that is used to check whether the names of tables conform to the naming conventions. You can select the checker that you created for each data layer during data warehouse planning. For more information, see Configure a data layer checker. None
    Name The name of the aggregate table. We recommend that you specify the name in the following format: dws_{Data domain}_{Statistical dimension}_{Custom table name tag}_{Period}. This way, you can easily understand the statistical content of the aggregate table.

    The name can contain lowercase letters, digits, and underscores (_), and must start with a lowercase letter.

    		Example: dws_trade_buyer_subpay_1d. You can obtain the following information from the name:
    • The data domain is trade.
    • The statistical dimension is buyer.
    • The custom table name tag is subpay.
    • The period is 1d.
    Display Name The display name of the aggregate table. Naming rules:

    The display name can be up to 256 characters in length, and can contain letters, digits, underscores (_), ampersands (&), and parentheses (). It must start with a letter or a digit.

    		Phased payments for transactions
    Lifecycle The lifecycle of the aggregate table. Unit: day. 90 days
    Description The description of the aggregate table. None
    Table Type Select a table type from the following options based on your business requirements:
    • Single-granularity Aggregate Table: You can collect data only at a single granularity, and cannot aggregate data at multiple granularities again.
      For example, you can execute the following statement to create a single-granularity aggregate table named dws_demo_2: 
      create table dws_demo_2 (
    shop_code comment 'Dimension: shop code',
    pay_amt comment 'Metric: sales amount of the shop',
    pay_cnt comment 'Metric: number of orders in the shop'
) comment 'Single-granularity Aggregate Table';
      To collect the metric data at a specific granularity, such as the shop granularity, you need only to use the SELECT syntax to query the desired metric field. You cannot use single-granularity aggregate tables to query data of multiple statistical granularities by using combinatorial logic.
    • Multi-granularity Aggregate Table: You can collect data at multiple granularities and aggregate data at different granularities again.
      For example, you can execute the following statement to create a multi-granularity aggregate table named dws_demo_1: 
      create table dws_demo_1 (
    merchant_code comment 'Dimension: merchant code',
    company_code comment 'Dimension: subsidiary company code',
    shop_code comment 'Dimension: shop code',
    sku_code comment 'Dimension: SKU code',
    pay_type comment 'Dimension: payment type',
    pay_amt comment 'Metric: sales amount',
    pay_cnt comment 'Metric: number of orders'
) comment 'Multi-granularity Aggregate Table';
      You can use the GROUP BY syntax to generate combinations of derived metrics at different granularities for statistical aggregation.
      • Execute the group by merchant_code statement to collect the metric data at the merchant granularity.
      • Execute the group by merchant_code,company_code,shop_code statement to collect the metric data at the shop granularity.
    		Single-granularity Aggregate Table
  4. Click Save.