Binary Data Parser for Big Data Ingestion
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Solution Overview
Problem
Big data ingestion systems face inefficiencies due to limitations in processing binary data files, leading to increased processing time and costs, as well as issues with data consistency and scalability, particularly in distributed processing systems.
Innovation Solution
A system and method for parsing binary big data files by identifying structure and metadata, converting them into load-ready files using XML or JSON formats, which can be processed efficiently on distributed file systems, ensuring data integrity and flexibility across clustered or non-clustered environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed processing systems split input files using record delimiter, then files can be processed in parallel, but binary files require conversion to delimited files first which increases processing time
Solution Approach 1:
The patent extracts the record delimiter identification and parsing logic from the general file processing workflow, creating a specialized binary file parser that directly identifies record boundaries within binary data without requiring conversion to delimited text format. This extraction of the essential parsing function enables parallel processing of binary files while eliminating the time-consuming conversion step.
Solution Approach 2:
The patent introduces an intermediary binary file parser component that sits between the raw binary input and the parallel processing stage. This intermediary performs intelligent parsing of binary records using delimiter detection and metadata extraction, transforming binary data into a format suitable for parallel processing without requiring full conversion to delimited files, thus reducing processing time while enabling parallelism.
2Adaptability or versatility
If custom code is written for each incoming data format, then data ingestion can be customized, but development time is extended
Solution Approach 1:
The patent creates a universal binary file parser that can handle multiple data formats and structures through a single standardized interface. The parser uses configurable parameters and metadata-driven approaches to adapt to different binary formats without requiring custom code development for each format, thus maintaining versatility while eliminating repetitive development work.
Solution Approach 2:
The patent employs parameter-based configuration to handle different data formats. Instead of writing custom code for each format, the system uses configurable parameters such as record length, delimiter types, and metadata schemas that can be adjusted to match different binary formats. This parameter-driven approach maintains adaptability while significantly reducing development time.
3Reliability
If multiple read/write operations are performed during ingestion, then data can be processed and validated, but execution time increases
Solution Approach 1:
The patent performs preliminary validation and metadata extraction during the parsing phase itself, before data is written to output files. The binary file parser validates record structure, checks for required fields, and extracts metadata in a single pass through the input data. This preliminary action ensures data consistency is verified early, eliminating the need for multiple subsequent read/write validation operations and reducing overall execution time.
4Productivity
If binary files are converted to delimited files before processing, then distributed systems can process the data, but additional processing steps are required
Solution Approach 1:
The patent replaces the mechanical conversion process (binary to delimited text) with a logical parsing approach that works directly on binary data. The system uses byte-level pattern matching, metadata interpretation, and structured parsing algorithms to process binary files in a format suitable for distributed processing, eliminating the intermediate conversion step and reducing overall system complexity.
Data Source
AI summary
The system may validate a data source having a structured format and a grammar that includes tags. The system may identify a tag in the grammar. The system may parse the data source to extract attributes and/or values associated with the tags in response to successful validation. The system may also write the attributes and/or values to an output file separated by a preselected delimiter. A configuration file may identify the grammar, the preselected delimiter, and/or the data source. The data source may be in an XML format or a JSON format. The system may generate execution ready code in response to the validating the data source and the grammar. The output file may be a load ready file for ingestion into a big data storage format. The tag may include a parent tag and a sub tag corresponding to a hierarchy in the data source.