Account Balances
Account Balances
Assurez une cohérence irréprochable entre tous vos produits de données
Assurez une cohérence irréprochable entre tous vos produits de données
Ensure Account Balances data is fresh, complete, and reliable before it’s used for financial reporting, reconciliation, and regulatory purposes.
Data contract description
This data contract enforces schema stability, a 24-hour freshness SLA, and required identifiers and balance timestamps to ensure reliable account balance reporting. It prevents duplicate account snapshots, blocks future-dated balances, enforces valid currency formatting, and ensures opening and closing balances are always present. Together, these checks protect financial reporting accuracy, prevent reconciliation discrepancies, and ensure downstream risk, accounting, and regulatory processes rely on consistent point-in-time balance data.
account_balances_data_contract.yaml
dataset: datasource/database/schema/account_balances variables: FRESHNESS_HOURS: default: 24
checks: - schema: allow_extra_columns: false allow_other_column_order: false - row_count: threshold: must_be_greater_than: 0 - freshness: column: balance_date threshold: unit: hour must_be_less_than_or_equal: ${var.FRESHNESS_HOURS} # Dataset integrity rules - failed_rows: name: "balance_date must not be in the future" qualifier: balance_date_not_future expression: balance_date > CURRENT_TIMESTAMP - failed_rows: name: "No duplicate account balance snapshots (account_id + balance_date)" qualifier: duplicate_account_snapshot query: | SELECT account_id, balance_date FROM account_balances GROUP BY account_id, balance_date HAVING COUNT(*) > 1 threshold: must_be: 0 - failed_rows: name: "Opening and closing balances must not be negative beyond allowed overdraft tolerance" qualifier: excessive_negative_balance expression
columns: - name: account_id data_type: string checks: - missing: name: No missing values - invalid: name: "account_id length guardrail" valid_min_length: 1 valid_max_length: 64 - name: balance_date data_type: date checks: - missing: name: No missing values - name: opening_balance data_type: decimal checks: - missing: name: No missing values - name: closing_balance data_type: decimal checks: - missing: name: No missing values - name: currency data_type: string checks: - missing: name: No missing values - invalid: name: "Currency must be ISO-4217 (3 uppercase letters)" valid_format: name: ISO-4217 code regex: "^[A-Z]{3}$"
Data contract description
This data contract enforces schema stability, a 24-hour freshness SLA, and required identifiers and balance timestamps to ensure reliable account balance reporting. It prevents duplicate account snapshots, blocks future-dated balances, enforces valid currency formatting, and ensures opening and closing balances are always present. Together, these checks protect financial reporting accuracy, prevent reconciliation discrepancies, and ensure downstream risk, accounting, and regulatory processes rely on consistent point-in-time balance data.
account_balances_data_contract.yaml
dataset: datasource/database/schema/account_balances variables: FRESHNESS_HOURS: default: 24
checks: - schema: allow_extra_columns: false allow_other_column_order: false - row_count: threshold: must_be_greater_than: 0 - freshness: column: balance_date threshold: unit: hour must_be_less_than_or_equal: ${var.FRESHNESS_HOURS} # Dataset integrity rules - failed_rows: name: "balance_date must not be in the future" qualifier: balance_date_not_future expression: balance_date > CURRENT_TIMESTAMP - failed_rows: name: "No duplicate account balance snapshots (account_id + balance_date)" qualifier: duplicate_account_snapshot query: | SELECT account_id, balance_date FROM account_balances GROUP BY account_id, balance_date HAVING COUNT(*) > 1 threshold: must_be: 0 - failed_rows: name: "Opening and closing balances must not be negative beyond allowed overdraft tolerance" qualifier: excessive_negative_balance expression
columns: - name: account_id data_type: string checks: - missing: name: No missing values - invalid: name: "account_id length guardrail" valid_min_length: 1 valid_max_length: 64 - name: balance_date data_type: date checks: - missing: name: No missing values - name: opening_balance data_type: decimal checks: - missing: name: No missing values - name: closing_balance data_type: decimal checks: - missing: name: No missing values - name: currency data_type: string checks: - missing: name: No missing values - invalid: name: "Currency must be ISO-4217 (3 uppercase letters)" valid_format: name: ISO-4217 code regex: "^[A-Z]{3}$"
Data contract description
This data contract enforces schema stability, a 24-hour freshness SLA, and required identifiers and balance timestamps to ensure reliable account balance reporting. It prevents duplicate account snapshots, blocks future-dated balances, enforces valid currency formatting, and ensures opening and closing balances are always present. Together, these checks protect financial reporting accuracy, prevent reconciliation discrepancies, and ensure downstream risk, accounting, and regulatory processes rely on consistent point-in-time balance data.
account_balances_data_contract.yaml
dataset: datasource/database/schema/account_balances variables: FRESHNESS_HOURS: default: 24
checks: - schema: allow_extra_columns: false allow_other_column_order: false - row_count: threshold: must_be_greater_than: 0 - freshness: column: balance_date threshold: unit: hour must_be_less_than_or_equal: ${var.FRESHNESS_HOURS} # Dataset integrity rules - failed_rows: name: "balance_date must not be in the future" qualifier: balance_date_not_future expression: balance_date > CURRENT_TIMESTAMP - failed_rows: name: "No duplicate account balance snapshots (account_id + balance_date)" qualifier: duplicate_account_snapshot query: | SELECT account_id, balance_date FROM account_balances GROUP BY account_id, balance_date HAVING COUNT(*) > 1 threshold: must_be: 0 - failed_rows: name: "Opening and closing balances must not be negative beyond allowed overdraft tolerance" qualifier: excessive_negative_balance expression
columns: - name: account_id data_type: string checks: - missing: name: No missing values - invalid: name: "account_id length guardrail" valid_min_length: 1 valid_max_length: 64 - name: balance_date data_type: date checks: - missing: name: No missing values - name: opening_balance data_type: decimal checks: - missing: name: No missing values - name: closing_balance data_type: decimal checks: - missing: name: No missing values - name: currency data_type: string checks: - missing: name: No missing values - invalid: name: "Currency must be ISO-4217 (3 uppercase letters)" valid_format: name: ISO-4217 code regex: "^[A-Z]{3}$"
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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Nos recherches ont été publiées dans des revues et conférences de renom, telles que NeurIPs, JAIR et ACML. Les mêmes lieux qui ont fait progresser les fondations de GPT et de l'IA moderne.
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4,4 sur 5
Commencez à faire confiance à vos données. Aujourd'hui.
Trouvez, comprenez et corrigez tout problème de qualité des données en quelques secondes.
Du niveau de la table au niveau des enregistrements.
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4,4 sur 5
Commencez à faire confiance à vos données. Aujourd'hui.
Trouvez, comprenez et corrigez tout problème de qualité des données en quelques secondes.
Du niveau de la table au niveau des enregistrements.
Adopté par
Solutions
4,4 sur 5
Commencez à faire confiance à vos données. Aujourd'hui.
Trouvez, comprenez et corrigez tout problème de qualité des données en quelques secondes.
Du niveau de la table au niveau des enregistrements.
Adopté par
Solutions
Company