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Auto-Generating Sources from SOLE Ingestion Tables

You can use the ingestion tables you manage in Snowflake Object Lifecycle Engine (SOLE) configuration files as dbt sources for MATE without manually adding new dbt sources whenever you create a table with the same schema, columns, etc. The benefits of this approach are:

  • Faster onboarding of new data sources
  • Shorter time to go live for new projects
  • No duplication of effort

Sharing ingestion table definitions allows:

This mechanism is a huge productivity win as you don't have to duplicate your effort and create the table definition twice.

For more information about using a script to generate sources for tables not managed by SOLE, see Generating Sources for Tables Managed Outside SOLE.

Prerequisites​

Before sharing ingestion table definitions between engines, ensure you enable the autogeneration of MATE dbt sources.

Ingesting data​

Declaring a table in SOLE and using it in MATE​

To start sharing sources between SOLE for Data Products and MATE, follow the below steps:

  1. Configure a table in the SOLE configuration file dataops/snowflake/databases.template.yml.

  2. Run a pipeline so that the SOLE job prepares the data needed by MATE to generate a new sources.yml file.

  3. Enable at least one MATE job to generate the source file.

    • The name of the generated file is suffixed by CI_PIPELINE_ID.

  4. To use the newly generated file in MATE and add the directory autogenerated_sources in the dbt_project.yml file:

    dataops/modelling/dbt_project.yml
    ## Project
    name: MyProject
    version: 0.1
    config-version: 2

    ## Sources
    model-paths: [models, sources, autogenerated_sources]

By adding autogenerated_sources as an additional sources directory, MATE finds and uses the new source file during the pipeline run.

tip

The new sources.yml file is available only during a pipeline run. It does not get hardcoded in your repository. See Capturing the generated source for more information.

The below examples show in detail how to ingest data from a single source system , ingest data from two source systems, declare MATE tests in SOLE, use tags to build specific tables in MATE, and generate a joined table of manual and autogenerated sources.

Ingesting data from a single source system​

In this example, you want to ingest customer data from Salesforce:

/dataops/snowflake/databases.template.yml
databases:
  CUSTOMER_DATA:
    namespacing: none
    orchestrators:
      mate:
        source_system: SALESFORCE
    schemas:
      STAGING:
        tables:
          CUSTOMER:
            columns:
              CUSTOMER_NAME:
                type: INT
              CUSTOMER_ADDRESS:
                type: VARCHAR
          PURCHASES:
            columns:
              PRODUCT_NAME:
                type: VARCHAR
              QUANTITY:
                type: VARCHAR
              PURCHASE_DATE:
                type: DATE
              PURCHASE_COUNT:
                type: INT

Without further setup, you can immediately use the ingestion tables as MATE source tables.

/dataops/modelling/curation/customers.sql
SELECT
  CUSTOMER_NAME,
  CUSTOMER_ADDRESS
FROM {{ source('DATAOPS_STAGING_SALESFORCE', 'CUSTOMER') }}

The source's name always starts with the DATAOPS_ prefix, followed by the schema name. If source_system is provided, the value thereof is used as a postfix. In this example, STAGING is the schema name, SALESFORCE is the name of the source system, and CUSTOMER is the table name.

Ingesting data from two source systems​

In this second example, you want to ingest customer data from Salesforce and SAP:

/dataops/snowflake/databases.template.yml
databases:
  CUSTOMER_DATA:
    namespacing: none
    comment: This is the main DataOps database for environment {{ env.DATAOPS_ENV_NAME }}
    orchestrators:
      mate:
        source_system: SALESFORCE
    schemas:
      STAGING:
        tables:
          CUSTOMER:
            columns:
              ID:
                type: INT
              CUSTOMER_NAME:
                type: INT
              CUSTOMER_ADDRESS:
                type: VARCHAR
          PURCHASES:
            columns:
              CUSTOMER_ID:
                type: INT
              PRODUCT_NAME:
                type: VARCHAR
              QUANTITY:
                type: VARCHAR
              PURCHASE_DATE:
                type: DATE
              PURCHASE_COUNT:
                type: INT
  DB:
    namespacing: none
    comment: this DB has the same schemas and tables as above, but the source_system key will ensure there is no name conflict
    orchestrators:
      mate:
        source_system: SAP
    schemas:
      STAGING:
        tables:
          CUSTOMER:
            columns:
              ID:
                type: INT
              CUSTOMER_NAME:
                type: INT
              CUSTOMER_ADDRESS:
                type: VARCHAR
          PURCHASES:
            columns:
              CUSTOMER_ID:
                type: INT
              PRODUCT_NAME:
                type: VARCHAR
              QUANTITY:
                type: VARCHAR
              PURCHASE_DATE:
                type: DATE
              PURCHASE_COUNT:
                type: INT
        external_tables:
          DEALER:
            file_format:
              format_type: "CSV"
              compression: "AUTO"
              field_delimiter: ","
              skip_header: 1
              skip_blank_lines: false
            location:
              database: "EXT_DATABASE"
              schema: "PUBLIC"
              stage: "EXT_STAGE"
            columns:
              Name:
                type: "text"
                as: "(value:c1::text)"
              Location:
                type: "text"
                as: "(value:c2::text)"
            comment: "Test external table 1"
            auto_refresh: true
            copy_grants: false
            refresh_on_create: true

When ingesting from two different sources, a sample of the generated sources.yml file looks like this. Note the file name suffix CI_PIPELINE_ID:

/dataops/modelling/autogenerated_sources/sources_{CI_PIPELINE_ID}.yml
version: 2
sources:
  - name: DATAOPS_STAGING_SALESFORCE
    description: ""
    database: CUSTOMER_DATA
    schema: STAGING
    tables:
      - name: CUSTOMER
        description: ""
        columns:
          - name: ID
            description: ""
          - name: CUSTOMER_NAME
            description: ""
          - name: CUSTOMER_ADDRESS
            description: ""
      - name: PURCHASES
        description: ""
        columns:
          - name: CUSTOMER_ID
            description: ""
          - name: PRODUCT_NAME
            description: ""
          - name: QUANTITY
            description: ""
          - name: PURCHASE_DATE
            description: ""
          - name: PURCHASE_COUNT
            description: ""
  - name: DATAOPS_STAGING_SAP
    description: ""
    database: DB
    schema: STAGING
    tables:
      - name: CUSTOMER
        description: ""
        columns:
          - name: ID
            description: ""
          - name: CUSTOMER_NAME
            description: ""
          - name: CUSTOMER_ADDRESS
            description: ""
      - name: PURCHASES
        description: ""
        columns:
          - name: CUSTOMER_ID
            description: ""
          - name: PRODUCT_NAME
            description: ""
          - name: QUANTITY
            description: ""
          - name: PURCHASE_DATE
            description: ""
          - name: PURCHASE_COUNT
            description: ""
      - name: DEALER
        description: ""
        columns:
          - name: Name
            description: ""
          - name: Year
            description: ""

Without further setup, you can immediately use the ingestion tables as MATE source tables.

/dataops/modelling/curation/customers.sql
SELECT
  CUSTOMER_NAME,
  (SELECT COUNT(*) FROM {{ source('DATAOPS_STAGING_SAP', 'PURCHASES') }}
  WHERE ID = CUSTOMER_ID)
  AS "Number Purchases"

FROM {{ source('DATAOPS_STAGING_SALESFORCE', 'CUSTOMER') }}

The source's name always starts with the DATAOPS_ prefix, followed by the schema name. If source_system is provided, the value thereof is used as a postfix. In this example, STAGING is the schema name for the two sources, SALESFORCE and SAP are the names of the source systems, and PURCHASE and CUSTOMER are the table names.

Declaring MATE tests in SOLE​

In this third example, you want to use the tags you define on columns in SOLE to run a MATE source testing stage independently for Salesforce and SAP by using a job that uses model selections via tags.

/dataops/snowflake/databases.template.yml
databases:
  CUSTOMER_DATA:
    namespacing: none
    comment: This is the main DataOps database for environment {{ env.DATAOPS_ENV_NAME }}
    orchestrators:
      mate:
        source_system: SALESFORCE
    schemas:
      STAGING:
        tables:
          CUSTOMER:
            columns:
              ID:
                type: INT
                tests:
                  - unique
                orchestrators:
                  mate:
                    tags:
                      - src.salesforce
              CUSTOMER_NAME:
                type: INT
              CUSTOMER_ADDRESS:
                type: VARCHAR
          PURCHASES:
            columns:
              CUSTOMER_ID:
                type: INT
              PRODUCT_NAME:
                type: VARCHAR
              QUANTITY:
                type: VARCHAR
              PURCHASE_DATE:
                type: DATE
              PURCHASE_COUNT:
                type: INT
                tests:
                  - unique
                orchestrators:
                  mate:
                    tags:
                      - src.salesforce
  DB:
    namespacing: none
    comment: this DB has the same schemas and tables as above, but the source_system key will ensure there is no name conflict
    orchestrators:
      mate:
        source_system: SAP
    schemas:
      STAGING:
        tables:
          CUSTOMER:
            columns:
              ID:
                type: INT
              CUSTOMER_NAME:
                type: INT
              CUSTOMER_ADDRESS:
                type: VARCHAR
          PURCHASES:
            columns:
              CUSTOMER_ID:
                type: INT
              PRODUCT_NAME:
                type: VARCHAR
              QUANTITY:
                type: VARCHAR
              PURCHASE_DATE:
                type: DATE
              PURCHASE_COUNT:
                type: INT

Without further setup, you can immediately use the tags you define on columns in SOLE to run a MATE source testing.

Test only Salesforce with tag:
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  variables:
    TRANSFORM_ACTION: TEST
    TRANSFORM_MODEL_SELECTOR: tag:src.salesforce
  stage: Source Testing
  script:
    - /dataops
  icon: ${TESTING_ICON}
  artifacts:
    when: always
    reports:
      junit: $CI_PROJECT_DIR/report.xml

The two tags src.salesforce applied to the columns ID and CUSTOMER_ID in the SALESFORCE source system are used to run the MATE source testing stage.

The results of running the test look like this:

docs-overview !!shadow!!

Using tags to build specific tables in MATE​

In this third example, you want to use the tags you define on tables in SOLE to run a MATE source to build a specific table by using a job that uses model selections via tags.

/dataops/snowflake/databases.template.yml
databases:
  CUSTOMER_DATA:
    namespacing: none
    comment: This is the main DataOps database for environment {{ env.DATAOPS_ENV_NAME }}
    orchestrators:
      mate:
        source_system: SALESFORCE
    schemas:
      STAGING:
        tables:
          CUSTOMER:
            orchestrators:
              mate:
                tags:
                  - sf_table_tag
            columns:
              ID:
                type: INT
              CUSTOMER_NAME:
                type: INT
              CUSTOMER_ADDRESS:
                type: VARCHAR
          PURCHASES:
            orchestrators:
              mate:
                tags:
                  - sf_table_tag
            columns:
              CUSTOMER_ID:
                type: INT
              PRODUCT_NAME:
                type: VARCHAR
              QUANTITY:
                type: VARCHAR
              PURCHASE_DATE:
                type: DATE
              PURCHASE_COUNT:
                type: INT
  DB:
    namespacing: none
    comment: this DB has the same schemas and tables as above, but the `source_system` tag will ensure there is no name conflict
    orchestrators:
      mate:
        source_system: SAP
    schemas:
      STAGING:
        tables:
          CUSTOMER:
            columns:
              ID:
                type: INT
              CUSTOMER_NAME:
                type: INT
              CUSTOMER_ADDRESS:
                type: VARCHAR
          PURCHASES:
            columns:
              CUSTOMER_ID:
                type: INT
              PRODUCT_NAME:
                type: VARCHAR
              QUANTITY:
                type: VARCHAR
              PURCHASE_DATE:
                type: DATE
              PURCHASE_COUNT:
                type: INT

Without further setup, you can immediately use the tags you define on tables in SOLE to run a MATE source and build the table.

Build all Models:
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  variables:
    TRANSFORM_ACTION: RUN
    TRANSFORM_MODEL_SELECTOR: tag:sf_table_tag
  stage: Data Transformation
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

Capturing the generated source​

Sometimes, you may need to check all the details of the generated file for troubleshooting purposes, for example. However, since the new sources.yml file is available only during a pipeline run, here are a few simple steps to capture the file:

  1. Export the file as an artifact or from the log of the MATE job.
  2. Upload the file to the project for additional configuration.
  3. Disable the ingestion tables sharing to avoid conflict.
  4. Subsequently, consume the new tables.

The example below shows how to set an export as an artifact condition:

Test all Sources:
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  variables:
    TRANSFORM_ACTION: TEST
  stage: Source Testing
  script:
    - /dataops
  icon: ${TESTING_ICON}
  artifacts:
    paths:
      - $CI_PROJECT_DIR/dataops/modelling/autogenerated_sources/

User-defined and autogenerated sources​

Below is an example that shows the differences between a user-defined source with a freshness clause and an autogenerated source based on the same ingestion table:

version: 2
sources:
- name: MANUALLY_DEFINED_EXAMPLE_SCHEMA_SALESFORCE
  freshness:
    warn_after:
      count: 1
      period: day
  description: ''
  database: CUSTOMER_DATA
  schema: EXAMPLE_SCHEMA
  tables:
  - name: PURCHASES
    description: ''
    columns:
    - name: CUSTOMER_ID
      description: ''
    - name: PRODUCT_NAME
      description: ''
    - name: QUANTITY
      description: ''
    - name: PURCHASE_DATE
      description: ''
    - name: PURCHASE_COUNT
      description: ''
      tags:
      - src.salesforce
      tests:
      - unique

--- sources{ci_pipeline_id}.yml
version: 2
sources:
- name: DATAOPS_EXAMPLE_SCHEMA_SALESFORCE
  description: ''
  database: CUSTOMER_DATA
  schema: EXAMPLE_SCHEMA
  tables:
  - name: CUSTOMER
    description: ''
    columns:
    - name: ID
      description: ''
      tags:
      - src.salesforce
      tests:
      - unique
    - name: CUSTOMER_NAME
      description: ''
    - name: CUSTOMER_ADDRESS
      description: ''
  - name: PURCHASES
    description: ''
    columns:
    - name: CUSTOMER_ID
      description: ''
    - name: PRODUCT_NAME
      description: ''
    - name: QUANTITY
      description: ''
    - name: PURCHASE_DATE
      description: ''
    - name: PURCHASE_COUNT
      description: ''
      tags:
      - src.salesforce
      tests:
      - unique
- name: DATAOPS_EXAMPLE_SCHEMA_SAP
  description: ''
  database: DB
  schema: EXAMPLE_SCHEMA
  tables:
  - name: CUSTOMER
    description: ''
    columns:
    - name: ID
      description: ''
    - name: CUSTOMER_NAME
      description: ''
    - name: CUSTOMER_ADDRESS
      description: ''
  - name: PURCHASES
    description: ''
    columns:
    - name: CUSTOMER_ID
      description: ''
    - name: PRODUCT_NAME
      description: ''
    - name: QUANTITY
      description: ''
    - name: PURCHASE_DATE
      description: ''
    - name: PURCHASE_COUNT
      description: ''

Without further setup, you can immediately generate a joined table of manual and autogenerated sources.

example_autogenerated_and_regular_source.sql
{{ config(alias='table_using_autogenerated_and_client_generated_source') -}}

SELECT
  CUSTOMER_NAME,
  PURCHASE_DATE

FROM {{ source('DATAOPS_EXAMPLE_SCHEMA_SALESFORCE', 'CUSTOMER') }}
JOIN {{ source('MANUALLY_DEFINED_EXAMPLE_SCHEMA_SALESFORCE', 'PURCHASES') }}
ON  ID = CUSTOMER_ID

Enabling/Disabling the auto-generation of the MATE source​

A new variable named DATAOPS_GENERATE_TRANSFORM_SOURCES has been implemented and set to 0 (False) by default.

Setting this variable to true allows the metadata of any table you create in the SOLE configuration file to be automatically generated and transformed into a ready-to-use MATE dbt sources.yml file. This file contains all the details from the configuration file databases.template.yml in the template project.

To enable autogeneration:

  1. Add the variable DATAOPS_GENERATE_TRANSFORM_SOURCES to the variables.yml file in your project settings.
  2. Set this variable to 1 (True).

Once set, each run of MATE automatically generates ready-to-use MATE sources from SOLE.

With the sources file generated, your next step is to give explicit permission to use the generated dbt sources in your MATE project by making a few changes to the dbt_project.yml file.

Head to ingesting data for the details.

To disable the autogeneration of the MATE sources and stop the sharing of ingestion table definitions, prevent the generation of the sources file by setting the variable DATAOPS_GENERATE_TRANSFORM_SOURCES to 0.

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