Data Ingestion System for Binary File Transformation
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Solution Overview
Problem
Current data ingestion tools are inadequate for automatically converting upstream source files into appropriate table or database formats for efficient big data analysis, failing to account for data variety and data quality, leading to errors and resource wastage.
Innovation Solution
A data ingestion system that extracts upstream data files based on ingestion parameters, including validation criteria, transformation instructions, and storage instructions, to generate readable tables for downstream analysis, identifying and correcting errors during the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing data ingestion tools are used to ingest source data, then the ingestion process is simple, but data quality errors occur and processing resources are wasted
Solution Approach 1:
The system performs preliminary data quality assessment and validation before the actual ingestion process. It evaluates source data against defined quality criteria, identifies errors and inconsistencies, and resolves them beforehand, ensuring only high-quality data is ingested into the data warehouse, thereby preventing downstream analysis issues
Solution Approach 2:
The system implements feedback mechanisms where data quality metrics are continuously monitored during ingestion. Quality assessment results feed back into the ingestion process, allowing dynamic adjustment of ingestion parameters and automatic rejection or correction of low-quality data, improving overall data reliability
2Reliability
If comprehensive validation criteria are applied during data ingestion, then data quality improves, but processing time increases
Solution Approach 1:
The system applies validation criteria selectively based on data priority and quality requirements. For high-priority or critical data, comprehensive validation is performed, while for lower-priority data, streamlined validation is used. This partial action approach maintains data quality for essential data while reducing overall processing time
Solution Approach 2:
The system performs preliminary quality assessment to identify obviously defective data before applying comprehensive validation. By filtering out clearly erroneous data early using quick checks, the system avoids wasting time on extensive validation of data that would be rejected anyway, thus balancing quality and speed
3Productivity
If data variety is not accounted for during ingestion, then the ingestion process is faster, but data quality errors increase
Solution Approach 1:
The system implements a universal validation framework that can handle multiple data types, formats, and structures through a single unified process. The validation criteria are designed to be type-agnostic, automatically adapting to different data varieties while maintaining consistent quality standards, thus preserving ingestion speed without compromising quality across diverse data sources
Data Source
AI summary
A device automatically extracts a data file from an upstream source based on ingestion parameters. The data file is in a first format that is not readable by a downstream data analysis utility and includes a plurality of data records. Each record includes one or more entries. The ingestion parameters include a file identifier, transformation instructions, and storage instructions for the extracted data file. The device generates table(s) based on the extracted data file by, determining, based on the transformation instructions, a table in which to store each entry and a row and column of the determined table in which to store the entry. Entries are input into the determined tables, rows, and columns. The table(s) are stored in a second format that is readable by the downstream data analysis utility.