Common Data Framework for Automated Data Munging
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Solution Overview
Problem
The conventional data munging process is inefficient and error-prone, requiring multiple experts and manual intervention, leading to lengthy data processing times and increased chances of errors, especially when handling data from various sources in different formats.
Innovation Solution
The Common Data Framework (CDF) system automates the data munging process by generating a configuration file and data model, using engines like data management, discovery, and mining engines to analyze, reformat, and organize data into a common format, reducing the need for manual intervention and enabling efficient data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual data munging process is used with multiple experts, then data transformation accuracy is improved, but data processing time increases significantly
Solution Approach 1:
The system enables self-service automated data munging through configuration files that allow the system to automatically discover, transform, and load data without requiring manual expert intervention for each data processing task, thereby reducing processing time while maintaining accuracy through consistent automated workflows
Solution Approach 2:
The system changes the operational parameters from manual expert-driven processes to automated configuration-driven processes, using parameterized configuration files that define transformation rules, data sources, and targets, enabling rapid execution while preserving data accuracy through validated transformation logic
2Adaptability or versatility
If manual data munging process is used, then complex data transformations can be handled, but the process becomes error-prone and lengthy
Solution Approach 1:
The configuration file acts as an intermediary that bridges complex data transformation requirements with automated execution, encoding transformation logic in a structured format that reduces manual errors while maintaining the ability to handle complex transformations through predefined patterns and rules
Solution Approach 2:
The system uses templates and reusable configuration patterns that can be copied and adapted for different data transformation scenarios, ensuring consistency and reducing errors through proven transformation logic that has been validated across multiple use cases
3Measurement precision
If multiple experts are involved in data munging, then comprehensive data analysis is achieved, but operational complexity increases
Solution Approach 1:
The system merges the capabilities of multiple experts into a single automated configuration file that encapsulates discovery logic, transformation rules, and loading strategies, consolidating complex multi-step processes into a unified automated workflow that maintains analytical quality while reducing operational complexity
Solution Approach 2:
The configuration file serves multiple functions simultaneously - it defines data sources, transformation logic, target structures, and execution parameters - enabling a single artifact to replace multiple specialized processes and reduce the need for coordinated expert intervention across different stages
4Productivity
If automated data munging is implemented, then processing speed is improved, but adaptability to different data formats may worsen
Solution Approach 1:
The system implements dynamic configuration files that can adapt to different data formats through parameterized definitions and conditional logic, allowing the automated process to adjust transformation rules based on the specific characteristics of each data source while maintaining high processing speed through programmatic execution
Data Source
AI summary
In an exemplary implementation, systems, devices and methods for generating a common data engineering framework include receiving source data having one or more formats from an external source, analyzing the source data, generating a data dictionary having a mapping of data elements of the source data based on the analysis of the source data, generating and storing in memory a configuration file having the data dictionary, and generating and storing in the configuration file a data model logically organizing the data from the data dictionary in a common format.