Robotics Events
Robotics Events
Assurez une cohérence irréprochable entre tous vos produits de données
Assurez une cohérence irréprochable entre tous vos produits de données
Ensure data from robotics operational events is fresh, complete, and reliable before it is used for operational monitoring, event-driven analytics, incident review, and process performance measurement.
Data contract description
This data contract is designed for a robotics startup that relies on machine-generated operational events to measure how its automation systems are performing across facilities, machines, work units, and processes. In this use case, the contract supports a production event stream used for operational analytics and business-critical metrics, where unreliable event delivery or inconsistent structure would slow analysis, weaken visibility into system behavior, and make it harder to support high-stakes customer reporting.
robotics_events_data_contract.yaml
datasetchecks: - schema: allow_extra_columns: false allow_other_column_order: false - row_count: threshold: must_be_greater_than: 0 - freshness: column: ingest_time threshold: unit: hour must_be_less_than: 2
columns: - name: event_id data_type: string checks: - missing: - duplicate: - invalid: name: "Event ID length guardrail" valid_min_length: 1 valid_max_length: 128 - name: event_type data_type: string checks: - missing: - invalid: name: "Valid event types" valid_values: - state_change - cycle_complete - alarm_raised - measurement_recorded - name: event_time data_type: timestamp checks: - missing: - name: ingest_time data_type: timestamp checks: - missing: - name: facility_id data_type: string checks: - missing: - invalid: name: "Facility ID length guardrail" valid_min_length: 1 valid_max_length: 64 - name: machine_id data_type: string checks: - missing: - invalid: name: "Machine ID length guardrail" valid_min_length: 1 valid_max_length: 64 - name: work_unit_id data_type: string checks: - missing: - invalid: name: "Work Unit ID flexibility" valid_min_length: 1 valid_max_length: 64 - name: process_id data_type: string checks: - missing: - invalid: name: "Process ID length guardrail" valid_min_length: 1 valid_max_length: 64
Data contract description
This data contract is designed for a robotics startup that relies on machine-generated operational events to measure how its automation systems are performing across facilities, machines, work units, and processes. In this use case, the contract supports a production event stream used for operational analytics and business-critical metrics, where unreliable event delivery or inconsistent structure would slow analysis, weaken visibility into system behavior, and make it harder to support high-stakes customer reporting.
robotics_events_data_contract.yaml
datasetchecks: - schema: allow_extra_columns: false allow_other_column_order: false - row_count: threshold: must_be_greater_than: 0 - freshness: column: ingest_time threshold: unit: hour must_be_less_than: 2
columns: - name: event_id data_type: string checks: - missing: - duplicate: - invalid: name: "Event ID length guardrail" valid_min_length: 1 valid_max_length: 128 - name: event_type data_type: string checks: - missing: - invalid: name: "Valid event types" valid_values: - state_change - cycle_complete - alarm_raised - measurement_recorded - name: event_time data_type: timestamp checks: - missing: - name: ingest_time data_type: timestamp checks: - missing: - name: facility_id data_type: string checks: - missing: - invalid: name: "Facility ID length guardrail" valid_min_length: 1 valid_max_length: 64 - name: machine_id data_type: string checks: - missing: - invalid: name: "Machine ID length guardrail" valid_min_length: 1 valid_max_length: 64 - name: work_unit_id data_type: string checks: - missing: - invalid: name: "Work Unit ID flexibility" valid_min_length: 1 valid_max_length: 64 - name: process_id data_type: string checks: - missing: - invalid: name: "Process ID length guardrail" valid_min_length: 1 valid_max_length: 64
Data contract description
This data contract is designed for a robotics startup that relies on machine-generated operational events to measure how its automation systems are performing across facilities, machines, work units, and processes. In this use case, the contract supports a production event stream used for operational analytics and business-critical metrics, where unreliable event delivery or inconsistent structure would slow analysis, weaken visibility into system behavior, and make it harder to support high-stakes customer reporting.
robotics_events_data_contract.yaml
datasetchecks: - schema: allow_extra_columns: false allow_other_column_order: false - row_count: threshold: must_be_greater_than: 0 - freshness: column: ingest_time threshold: unit: hour must_be_less_than: 2
columns: - name: event_id data_type: string checks: - missing: - duplicate: - invalid: name: "Event ID length guardrail" valid_min_length: 1 valid_max_length: 128 - name: event_type data_type: string checks: - missing: - invalid: name: "Valid event types" valid_values: - state_change - cycle_complete - alarm_raised - measurement_recorded - name: event_time data_type: timestamp checks: - missing: - name: ingest_time data_type: timestamp checks: - missing: - name: facility_id data_type: string checks: - missing: - invalid: name: "Facility ID length guardrail" valid_min_length: 1 valid_max_length: 64 - name: machine_id data_type: string checks: - missing: - invalid: name: "Machine ID length guardrail" valid_min_length: 1 valid_max_length: 64 - name: work_unit_id data_type: string checks: - missing: - invalid: name: "Work Unit ID flexibility" valid_min_length: 1 valid_max_length: 64 - name: process_id data_type: string checks: - missing: - invalid: name: "Process ID length guardrail" valid_min_length: 1 valid_max_length: 64
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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4,4 sur 5
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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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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
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