Data Deployment Service Validation for Production Integrity
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Solution Overview
Problem
Existing data store technologies face challenges in preventing noncompliant datasets from being stored in production environments, leading to errors and inconsistencies, especially with large numbers of users submitting changes, which can quickly result in data issues.
Innovation Solution
A two-step data validation process using a preproduction staging environment and a data deployment service that performs consistency checks on temporary datasets before promoting them to a production data store, ensuring compliance with defined dataset specifications through parallel execution of initial checks and batch validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If consistency checks are performed on datasets before promoting to production, then data integrity and reliability are improved, but processing time and throughput are worsened due to validation delays
Solution Approach 1:
The patent performs consistency checks in advance on a copy of the dataset before promoting it to production. This preliminary validation action ensures data integrity is verified beforehand, preventing bad data from reaching production while allowing the actual promotion process to remain efficient through parallel processing and caching mechanisms.
Solution Approach 2:
The patent creates a copy of the dataset in a preproduction environment for validation purposes. This copy allows consistency checks to be performed without blocking the original data flow to production, enabling parallel processing and maintaining throughput while ensuring reliability through thorough validation of the replica.
2Reliability
If multiple consistency checks are executed sequentially, then data validation thoroughness is improved, but processing time increases
Solution Approach 1:
The patent divides the validation process into multiple independent consistency checks that can be executed in parallel. This segmentation allows thorough validation of different data aspects simultaneously rather than sequentially, maintaining validation thoroughness while reducing total processing time through concurrent execution.
Solution Approach 2:
The patent implements continuous validation where consistency checks run concurrently with data processing operations. By making useful action continuous rather than intermittent, the system maintains thorough validation coverage while minimizing idle time and processing delays through overlapping operations.
3Difficulty of detecting and measuring
If a preproduction staging environment is used for validation, then error detection capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a preproduction staging environment as an intermediary layer between data submission and production deployment. This intermediate system provides dedicated error detection and validation capabilities, making error detection more effective while containing complexity in an isolated layer that can be managed independently.
Solution Approach 2:
The patent uses a disposable preproduction staging environment that can be quickly spun up for validation and then discarded or reset. This approach provides robust error detection capabilities while minimizing the permanent complexity footprint, as the validation environment can be instantiated as needed without requiring permanent complex infrastructure.
Data Source
AI summary
A technology is described for deploying datasets to a production data store. An example of the technology may include receiving a request at a data deployment service to submit a dataset to a production data store which is accessible to services that utilize datasets stored in the production data store. A temporary dataset may be created in a preproduction staging store and a first consistency check may be performed against the temporary dataset to determine whether the temporary dataset complies with dataset specifications. The temporary dataset may be included in a batch of temporary datasets cached on the preproduction staging store when the first consistency check is successful, and a second consistency check may be performed against the batch to determine whether the at least one temporary dataset complies with the dataset specifications. The temporary datasets may be stored to the production data store when the second consistency check is successful.