Simulated Dataset for Cloud ETL Pipeline Design
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Solution Overview
Problem
Accessing on-premises data sources from public cloud platforms for designing data processing pipelines is hindered by network I/O bottlenecks and security concerns, particularly when dealing with sensitive data, as conventional approaches require transferring large amounts of data and pose compliance threats.
Innovation Solution
Generating a simulated dataset based on sample statistics, allowing the design and development of data processing pipelines without transferring actual data, using descriptive statistics to create a pseudo-random dataset that matches the distribution of the on-premises data, thereby reducing data transfer and security risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transferred from on-premises data sources to public cloud platforms for pipeline design, then data processing capabilities are improved, but network I/O performance deteriorates due to bottlenecks
Solution Approach 1:
The patent extracts only the essential statistical characteristics (min, max, mean, standard deviation) from the complete dataset, transferring minimal data from on-premises to cloud while retaining sufficient information for pipeline design and testing. This extraction approach maintains productivity by enabling cloud-based processing while avoiding network I/O bottlenecks by transmitting only compact statistical parameters rather than full datasets.
2Productivity
If sensitive data is transferred to public cloud platforms, then data management capabilities are improved, but security and compliance risks increase
Solution Approach 1:
The patent creates a synthetic copy of the data distribution using statistical parameters (min, max, mean, standard deviation) to generate representative datasets in the cloud. This copy approach enables full data management capabilities including pipeline design, transformation testing, and validation without exposing actual sensitive data to cloud environments, thereby maintaining productivity while eliminating security and compliance risks associated with transferring PII or PHI.
3Measurement precision
If complete datasets are transferred for pipeline testing, then testing accuracy is improved, but data transfer complexity increases
Solution Approach 1:
The patent transforms the complete dataset into a compact parameter representation using four statistical measures (min, max, mean, standard deviation) per attribute. This parameter transformation maintains testing accuracy by preserving the essential distribution characteristics needed for valid pipeline testing, while dramatically reducing data transfer complexity from O(n) where n is the number of records to O(m) where m is the number of attributes, enabling efficient cloud-based pipeline development.
Data Source
AI summary
A system for simulating a dataset based on sample statistics, and generating pipeline instructions for a database using the simulated dataset, is disclosed. The system may be a cloud-based platform. The system can provide improved performance and security while designing and deploying Extract-Transform-Load (ETL) database pipelines to manage on-premises data. The system receives, from a remote client, sample statistics characterizing a distribution of data corresponding to a column of a database table. The system can generate, based on the received sample statistics, a simulated dataset representing the column of the database table. The simulated dataset may be generated using pseudo-random data values from a distribution with statistics matching the sample statistics. The system can build a simulated database relation based at least in part on the simulated dataset. The system can generate and deploy pipeline instructions using the simulated database relation.