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Open Data Contract Standard (ODCS) Source

Info

Generating data based on an external metadata source is a paid feature.

Data Caterer reading from ODCS file for schema metadata and data quality

Creating a data generator for a CSV file based on metadata stored in Open Data Contract Standard (ODCS).

Requirements

  • 10 minutes
  • Git
  • Gradle
  • Docker

Get Started

First, we will clone the data-caterer-example repo which will already have the base project setup required.

git clone git@github.com:data-catering/data-caterer-example.git
git clone git@github.com:data-catering/data-caterer-example.git
git clone git@github.com:data-catering/data-caterer-example.git

Open Data Contract Standard (ODCS) Setup

We will be using the following ODCS file for this example.

Plan Setup

Create a new Java or Scala class.

  • Java: src/main/java/io/github/datacatering/plan/MyAdvancedODCSJavaPlanRun.java
  • Scala: src/main/scala/io/github/datacatering/plan/MyAdvancedODCSPlanRun.scala
  • YAML: docker/data/customer/plan/my-odcs.yaml

Make sure your class extends PlanRun.

import io.github.datacatering.datacaterer.java.api.PlanRun;
...

public class MyAdvancedODCSJavaPlanRun extends PlanRun {
    {
        var conf = configuration().enableGeneratePlanAndTasks(true)
            .generatedReportsFolderPath("/opt/app/data/report");
    }
}
import io.github.datacatering.datacaterer.api.PlanRun
...

class MyAdvancedODCSPlanRun extends PlanRun {
  val conf = configuration.enableGeneratePlanAndTasks(true)
    .generatedReportsFolderPath("/opt/app/data/report")
}

In docker/data/custom/plan/my-odcs.yaml:

name: "my_odcs_plan"
description: "Create account data in CSV via ODCS metadata"
tasks:
  - name: "csv_account_file"
    dataSourceName: "customer_accounts"
    enabled: true

In application.conf:

flags {
  enableUniqueCheck = true
}
folders {
  generatedReportsFolderPath = "/opt/app/data/report"
}

  1. Click on Advanced Configuration towards the bottom of the screen
  2. Click on Flag and click on Unique Check
  3. Click on Folder and enter /tmp/data-caterer/report for Generated Reports Folder Path

We will enable generate plan and tasks so that we can read from external sources for metadata and save the reports under a folder we can easily access.

Schema

We can point the schema of a data source to our Open Data Contract Standard (ODCS) file.

var accountTask = csv("my_csv", "/opt/app/data/account-odcs", Map.of("header", "true"))
        .schema(metadataSource().openDataContractStandard("/opt/app/mount/odcs/full-example.yaml"))
        .count(count().records(100));
val accountTask = csv("customer_accounts", "/opt/app/data/customer/account-odcs", Map("header" -> "true"))
  .schema(metadataSource.openDataContractStandard("/opt/app/mount/odcs/full-example.yaml"))
  .count(count.records(100))

In docker/data/custom/task/file/csv/csv-odcs-account-task.yaml:

name: "csv_account_file"
steps:
  - name: "accounts"
    type: "csv"
    options:
      path: "/opt/app/data/csv/account-odcs"
      metadata_source_type: "open_data_contract_standard"
      dataContractFile: "/opt/app/mount/odcs/full-example.yaml"
    count:
      records: 100

  1. Click on Connection tab at the top
  2. Select ODCS as the data source and enter example-odcs
  3. Copy this file into /tmp/odcs/full-example.yaml
  4. Enter /tmp/odcs/full-example.yaml as the Contract File

The above defines that the schema will come from Open Data Contract Standard (ODCS), which is a type of metadata source that contains information about schemas. Specifically, it points to the schema provided here in the docker/mount/odcs folder of data-caterer-example repo.

Run

Let's try run and see what happens.

./run.sh MyAdvancedODCSJavaPlanRun
head docker/sample/account-odcs/part-00000-*
./run.sh MyAdvancedODCSPlanRun
head docker/sample/account-odcs/part-00000-*
./run.sh my-odcs.yaml
head docker/sample/account-odcs/part-00000-*
  1. Click on Execute at the top
    head /tmp/data-caterer/customer/account-odcs/part-00000*
    

It should look something like this.

txn_ref_dt,rcvr_id,rcvr_cntry_code
2023-07-11,PB0Wo dMx,nWlbRGIinpJfP
2024-05-01,5GtkNkHfwuxLKdM,1a
2024-05-01,OxuATCLAUIhHzr,gSxn2ct
2024-05-22,P4qe,y9htWZhyjW

Looks like we have some data now. But we can do better and add some enhancements to it.

Custom metadata

We can see from the data generated, that it isn't quite what we want. Sometimes, the metadata is not sufficient for us to produce production-like data yet, and we want to manually edit it. Let's try to add some enhancements to it.

Let's make the rcvr_id field follow the regex RC[0-9]{8} and the field rcvr_cntry_code should only be one of either AU, US or TW. For the full guide on data generation options, check the following page.

var accountTask = csv("my_csv", "/opt/app/data/account-odcs", Map.of("header", "true"))
            .schema(metadata...)
            .schema(
                field().name("rcvr_id").regex("RC[0-9]{8}"),
                field().name("rcvr_cntry_code").oneOf("AU", "US", "TW")
            )
            .count(count().records(100));
val accountTask = csv("customer_accounts", "/opt/app/data/customer/account-odcs", Map("header" -> "true"))
  .schema(metadata...)
  .schema(
    field.name("rcvr_id").regex("RC[0-9]{8}"),
    field.name("rcvr_cntry_code").oneOf("AU", "US", "TW")
  )
  .count(count.records(100))

In docker/data/custom/task/file/csv/csv-odcs-account-task.yaml:

name: "csv_account_file"
steps:
  - name: "accounts"
    type: "csv"
    options:
      path: "/opt/app/data/csv/account-odcs"
    count:
      records: 100
    schema:
      fields:
        - name: "rcvr_id"
          options:
            regex: "RC[0-9]{8}"
        - name: "rcvr_cntry_code"
          options:
            oneOf:
              - "AU"
              - "US"
              - "TW"

  1. Click on Generation and tick the Manual checkbox
  2. Click on + Field
    1. Go to rcvr_id field
    2. Click on + dropdown next to string data type
    3. Click Regex and enter RC[0-9]{8}
  3. Click on + Field
    1. Go to rcvr_cntry_code field
    2. Click on + dropdown next to string data type
    3. Click One Of and enter AU,US,TW

Let's test it out by running it again

./run.sh MyAdvancedODCSJavaPlanRun
head docker/sample/account-odcs/part-00000-*
./run.sh MyAdvancedODCSPlanRun
head docker/sample/account-odcs/part-00000-*
./run.sh my-odcs.yaml
head docker/sample/account-odcs/part-00000-*
  1. Click on Execute at the top
    head /tmp/data-caterer/customer/account-odcs/part-00000*
    
txn_ref_dt,rcvr_id,rcvr_cntry_code
2024-02-15,RC02579393,US
2023-08-18,RC14320425,AU
2023-07-07,RC17915355,TW
2024-06-07,RC47347046,TW

Great! Now we have the ability to get schema information from an external source, add our own metadata and generate data.

Data validation

To find out what data validation options are available, check this link.

Another aspect of Open Data Contract Standard (ODCS) that can be leveraged is the definition of data quality rules. Once the latest version of ODCS is released (version 3.x), there should be a vendor neutral definition of data quality rules that Data Caterer can use. Once available, it will be as easy as enabling data validations via enableGenerateValidations in configuration.

var conf = configuration().enableGeneratePlanAndTasks(true)
    .enableGenerateValidations(true)
    .generatedReportsFolderPath("/opt/app/data/report");

execute(conf, accountTask);
val conf = configuration.enableGeneratePlanAndTasks(true)
  .enableGenerateValidations(true)
  .generatedReportsFolderPath("/opt/app/data/report")

execute(conf, accountTask)

In application.conf:

flags {
  enableGenerateValidations = true
}

  1. Click on Advanced Configuration towards the bottom of the screen
  2. Click on Flag and click on Generate Validations

Check out the full example under AdvancedODCSSourcePlanRun in the example repo.