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Customer Transactions

Customer Transactions

Assurez une cohérence irréprochable entre tous vos produits de données

Assurez une cohérence irréprochable entre tous vos produits de données

Par Minseo (Len) Park

Ensure data from recent customer transactions is fresh, complete, and reliable before it is used for banking analytics, financial monitoring, account activity tracking, and downstream reporting.

Data contract description

This data contract is designed for Capital One, where recent customer transaction data serves as a governed operational dataset for downstream reporting, monitoring, and data quality control. By scoping the data to the last month of activity and setting expectations around transaction_date, amount, currency, and the uniqueness of transaction_id with transaction_date, the contract makes sure this transaction stream can be used with confidence for tracking recent account activity, validating whether incoming records fall within expected policy bounds, and supporting shift-left quality checks before issues spread into downstream systems.

Dataset

Defines the dataset this contract applies to, including its source and scope. This is the single point of reference for what data is being validated and protected.

Dataset checks

Validations that apply to the dataset as a whole, including schema stability, freshness, record presence, and cross-row integrity. This contract includes a schema check that disallows extra columns and unexpected column reordering, a row_count check to prevent empty loads, a composite duplicate check across transaction_id and transaction_date, and a freshness check requiring transaction_date to be no older than 30 days. It also defines a dataset-level failed-rows rule tied to the maximum transaction threshold.

Column checks

Column-level rules to ensure completeness, correctness, and consistency for individual fields. This contract enforces that transaction_id, customer_id, transaction_date, and amount are not missing, and it requires transaction_id to be unique at the column level. It constrains amount to a valid range and limits currency to the approved values. Together, these checks help preserve reliable identifiers, usable timestamps, and controlled monetary values for recent transaction data.

customer_transactions_data_contract.yaml

dataset: datasource/database/schema/customer_transactions

filter: "transaction_date >= CURRENT_DATE - INTERVAL '1 month'"

variables:
  MAX_TRANSACTION_AMOUNT:
    default: 10000
checks:
  - schema:
      allow_extra_columns: false
      allow_other_column_order: false
  - row_count:
      threshold:
        must_be_greater_than: 0
  - failed_rows:
      expression: amount <= ${var.MAX_TRANSACTION_AMOUNT}
      name: Transactions exceeding maximum allowed
  - duplicate:
      columns: ['transaction_id', 'transaction_date']     
  - freshness:
      column: transaction_date
      threshold:
        unit: day
        must_be_less_than: 30
columns:
  - name: transaction_id
    data_type: varchar
    checks:
      - missing:
      - duplicate:
  - name: customer_id
    data_type: varchar
    checks:
      - missing:
  - name: transaction_date
    data_type: timestamp
    checks:
      - missing:
  - name: amount
    data_type: decimal
    checks:
      - missing:
      - invalid:
          valid_min: 0
          valid_max: ${var.MAX_TRANSACTION_AMOUNT}
  - name: currency
    data_type: varchar
    checks:
      - invalid:
          valid_values: ['USD', 'EUR', 'GBP', 'JPY'

Data contract description

This data contract is designed for Capital One, where recent customer transaction data serves as a governed operational dataset for downstream reporting, monitoring, and data quality control. By scoping the data to the last month of activity and setting expectations around transaction_date, amount, currency, and the uniqueness of transaction_id with transaction_date, the contract makes sure this transaction stream can be used with confidence for tracking recent account activity, validating whether incoming records fall within expected policy bounds, and supporting shift-left quality checks before issues spread into downstream systems.

Dataset

Defines the dataset this contract applies to, including its source and scope. This is the single point of reference for what data is being validated and protected.

Dataset checks

Validations that apply to the dataset as a whole, including schema stability, freshness, record presence, and cross-row integrity. This contract includes a schema check that disallows extra columns and unexpected column reordering, a row_count check to prevent empty loads, a composite duplicate check across transaction_id and transaction_date, and a freshness check requiring transaction_date to be no older than 30 days. It also defines a dataset-level failed-rows rule tied to the maximum transaction threshold.

Column checks

Column-level rules to ensure completeness, correctness, and consistency for individual fields. This contract enforces that transaction_id, customer_id, transaction_date, and amount are not missing, and it requires transaction_id to be unique at the column level. It constrains amount to a valid range and limits currency to the approved values. Together, these checks help preserve reliable identifiers, usable timestamps, and controlled monetary values for recent transaction data.

customer_transactions_data_contract.yaml

dataset: datasource/database/schema/customer_transactions

filter: "transaction_date >= CURRENT_DATE - INTERVAL '1 month'"

variables:
  MAX_TRANSACTION_AMOUNT:
    default: 10000
checks:
  - schema:
      allow_extra_columns: false
      allow_other_column_order: false
  - row_count:
      threshold:
        must_be_greater_than: 0
  - failed_rows:
      expression: amount <= ${var.MAX_TRANSACTION_AMOUNT}
      name: Transactions exceeding maximum allowed
  - duplicate:
      columns: ['transaction_id', 'transaction_date']     
  - freshness:
      column: transaction_date
      threshold:
        unit: day
        must_be_less_than: 30
columns:
  - name: transaction_id
    data_type: varchar
    checks:
      - missing:
      - duplicate:
  - name: customer_id
    data_type: varchar
    checks:
      - missing:
  - name: transaction_date
    data_type: timestamp
    checks:
      - missing:
  - name: amount
    data_type: decimal
    checks:
      - missing:
      - invalid:
          valid_min: 0
          valid_max: ${var.MAX_TRANSACTION_AMOUNT}
  - name: currency
    data_type: varchar
    checks:
      - invalid:
          valid_values: ['USD', 'EUR', 'GBP', 'JPY'

Data contract description

This data contract is designed for Capital One, where recent customer transaction data serves as a governed operational dataset for downstream reporting, monitoring, and data quality control. By scoping the data to the last month of activity and setting expectations around transaction_date, amount, currency, and the uniqueness of transaction_id with transaction_date, the contract makes sure this transaction stream can be used with confidence for tracking recent account activity, validating whether incoming records fall within expected policy bounds, and supporting shift-left quality checks before issues spread into downstream systems.

customer_transactions_data_contract.yaml

dataset: datasource/database/schema/customer_transactions

filter: "transaction_date >= CURRENT_DATE - INTERVAL '1 month'"

variables:
  MAX_TRANSACTION_AMOUNT:
    default: 10000
checks:
  - schema:
      allow_extra_columns: false
      allow_other_column_order: false
  - row_count:
      threshold:
        must_be_greater_than: 0
  - failed_rows:
      expression: amount <= ${var.MAX_TRANSACTION_AMOUNT}
      name: Transactions exceeding maximum allowed
  - duplicate:
      columns: ['transaction_id', 'transaction_date']     
  - freshness:
      column: transaction_date
      threshold:
        unit: day
        must_be_less_than: 30
columns:
  - name: transaction_id
    data_type: varchar
    checks:
      - missing:
      - duplicate:
  - name: customer_id
    data_type: varchar
    checks:
      - missing:
  - name: transaction_date
    data_type: timestamp
    checks:
      - missing:
  - name: amount
    data_type: decimal
    checks:
      - missing:
      - invalid:
          valid_min: 0
          valid_max: ${var.MAX_TRANSACTION_AMOUNT}
  - name: currency
    data_type: varchar
    checks:
      - invalid:
          valid_values: ['USD', 'EUR', 'GBP', 'JPY'

How to Enforce Data Contracts with Soda

Embed data quality through data contracts at any point in your pipeline.

Embed data quality through data contracts at any point in your pipeline.

# pip install soda-{data source} for other data sources

# pip install soda-{data source} for other data sources

pip install soda-postgres

pip install soda-postgres

# verify the contract locally against a data source

# verify the contract locally against a data source

soda contract verify -c contract.yml -ds ds_config.yml

soda contract verify -c contract.yml -ds ds_config.yml

# publish and schedule the contract with Soda Cloud

# publish and schedule the contract with Soda Cloud

soda contract publish -c contract.yml -sc sc_config.yml

soda contract publish -c contract.yml -sc sc_config.yml

Check out the CLI documentation to learn more.

Check out the CLI documentation to learn more.

How to Automatically Create Data Contracts.
In one Click.

Automatically write and publish data contracts using Soda's AI-powered data contract copilot.

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ⓒ 2025 Soda Data Inc. Tous droits réservés.

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