Saved user resources and expenses by embedding duplicate handling into the overall transformation logic as an integrated part of existing pipelines
Dealing with data duplicates is a fundamental function of data management affecting all industries and company types. The handling of duplicates is required for a number of reasons including improving the quality of data sets, the efficiency of data processes, the overall costs of extracting, analyzing, and storing data as well as overall functionality. As machine learning grows in popularity, duplicate data feeds (many of which are still in the learning phase) are becoming more commonplace. Without the proper orchestration of duplicates, data outputs have negative effects and bias influences on the entirety of these original models.
For customers dealing with real-time data transactions and IoT, the most common issue is the oversampling of data sources and sensors, this leads to duplicate issues as the update rates do not align with the business need. Other clients found the need for duplication handling when they realized that their dashboard logic was not aligned with their data flow. An example of this was a manufacturing company’s data flow where results from quality test data were repeatedly added, but the dashboard logic only focused on the ‘first test result.’ In this case, the proper orchestration of data duplications had a direct impact on the truth and quality of the dashboards themselves.
Datorios has assisted many companies in reducing their expenditures and resources used for data orchestration with our simple mechanism for duplicate handling. The Datorios mechanism utilizes a methodology that checks for the repeated appearance of a primary key in a pre-specified time slot, for a certain number of repetitions. In cases where a primary key does not describe the logic of an event being duplicated, data duplications can be defined using a logical combination between keys that can describe the right condition.
By helping Datorios clients reduce the overall number of events with preprocessing pipeline logic, they were now able to find the right rate and could guarantee only necessary data was loaded to the target destination.
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