Cut Data Integration Time from Months to Days with LLM-Powered Intelligent Ingestion

Background and Challenges

Traditional data‑access processes suffer from high labor cost, long cycles, and poor quality control. Enterprises need a solution that delivers high efficiency, high accuracy, and strong scalability to support rapid business changes.

Traditional Data Integration Process

The conventional full‑manual workflow includes four stages:

Requirement communication (≈2 weeks) : Business and product managers clarify needs, architects assess feasibility.

Development implementation (≈6 weeks) : Business teams develop adapters, architects provide guidance, both sides coordinate integration.

Testing and verification (≈1 week) : Data quality checks, performance stress tests, security compliance.

Production operation (continuous) : Deployment, monitoring, incident handling, regular maintenance.

This approach typically requires four person‑months per business line, takes 2‑3 months to launch, and scales linearly with workload.

Intelligent Transformation Solution

The proposed “Intelligent Data Ingestion” solution centers on a large language model (LLM) and a “code‑as‑asset” philosophy, forming a generate‑execute‑feedback closed loop that automates the entire pipeline from schema recognition to package deployment.

Stage 1: Intelligent Schema Recognition

The model identifies data organization, field semantics, types, and quality issues across heterogeneous sources (CSV, JSON, XML, logs, PDFs). It handles multi‑modal and non‑uniform structures, resolves naming ambiguities such as ctime, create_time, and 生成时间, and detects anomalous values like INT fields containing N/A, NULL, or -.

Stage 2: Structured Extraction and Mapping

Using the recognized schema, the system generates code (e.g., Pandas, PySpark) to flatten nested JSON, map foreign keys (e.g., user_id), and produce high‑performance transformation logic that avoids row‑by‑row processing.

Stage 3: Rule Extraction and Filtering

Natural‑language business requirements are converted into precise SQL or configuration rules (e.g., WHERE date >= '2024-02-17' AND status = 'active'). The model also detects rule conflicts and evaluates rule reasonableness.

Stage 4: Automatic Package Construction and Execution

The outputs of the previous stages are packaged into a versioned, one‑click deployable data‑ingestion bundle (Dockerfile, SQL DDL, Airflow DAG, shell scripts). The workflow ensures atomicity, rollback capability, and CI/CD integration.

Technical Architecture

The architecture consists of two parallel streams:

Horizontal pipeline (core process) : Four progressive stages that deliver business value.

Vertical closed loop (intelligent code loop) : LLM generates code, CodeServer executes it, feedback refines prompts and models, creating a self‑optimizing system.

Key advantages include:

Code assets solidify LLM intelligence, achieving execution speeds hundreds of times faster than raw model calls.

Full traceability and version control enable human review and intervention when needed.

Continuous learning via feedback loops (SFT, RLHF) improves model performance over time.