Unverified50% confidenceSolutionExact time
Building the data pipeline as a production service—versioned artifact storage, replayable cleaning logic, change logs—is a necessary architectural investment.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Fine-Tune an LLM or Build an App? Data Preparation Is the Architecture Core
redditr/LangChain10/19/2026
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UnverifiedThe poster's actual work includes data ingestion platform capability development, ML model engineering, building data pipelines, Spark/PySpark data processing, Shell automation scripts, SFTP workflows, and reporting system construction.72% similarUnverifiedData platforms are increasingly internalizing parsing capabilities for industry-specific formats as native features, rather than having users stitch things together with scripts at the edge.71% similarUnverifiedA data ingestion pipeline generally refers to the process of extracting from raw data sources, transforming, and loading into a target store; in AI applications it may include steps such as document parsing, chunking, and vectorization.71% similarUnverifiedA data factory typically includes a data generation strategy, a data isolation mechanism, and a data cleanup strategy, used to address the creation, management, and cleanup of test data.71% similarUnverifiedSystem design refers to the process of planning the entire software architecture before writing specific code, including service decomposition, data storage and flow, high-concurrency handling, and guarantees of availability and scalability.70% similar
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