Python Data Pipeline Stack Architect
v2.4 Lean Engine
Synthesize high-performance automation workflows based on modern Python data tooling
Templates:
Lean Document Extractor
Fuzzy Deduplication
DuckDB Fast ETL
Pipeline Architecture
Lean Document Extraction
Est. Throughput
~350 docs/min
Peak Memory Footprint
<180MB RAM
Dependency Weight
34 MB (Clean)
Interactive Modular Stages
Simulate Processing
1
Ingest & Unpack
pdfplumber / pypdf (Light OCR/Text)
unstructured (Heavy Local ML)
fastavro / pyarrow (Binary Streaming)
httpx + orjson (Rest API Ingest)
Native stream-level text and bounding box extractor. Zero bulky PyTorch or heavy C++ bindings required.
2
Parsing & Cleansing
polars + pydantic v2 (Rust-Core)
pandas + jsonschema (Standard/Legacy)
duckdb (In-Memory SQL Analytical)
rapidfuzz + polars (Fuzzy Dedup Engine)
Multi-threaded SIMD tabular normalization with zero-copy validation and strict schema enforcement.
3
Vector / Retrieval Prep
fastembed + chromadb (ONNX Runtime)
sentence-transformers + torch (Heavyweight)
lancedb + pyarrow (Zero-copy disk vector)
pyarrow parquet sink (Direct Columnar)
Runs lightweight quantized embeddings without CUDA or PyTorch overhead. Extremely fast on commodity CPUs.
[INIT] Architecture loaded. Ready to run client-side simulation.
Live Tabular & Text Playground
Client-side live transformation preview
RAW UNSTRUCTURED/DIRTY INPUT
Editable sample
NORMALIZED & VECTORIZED OUTPUT
3 records ready
Turnkey Automated Python Script (
pipeline.py
)
Copy Script
Download .py
Export Spec (JSON)
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