Bob Oner

@BioLLM_ @IBM @credly Facts. It’s definitely top-tier right now.

@BobOner1991 @IBM @credly Super big one, love to see people using IBM's wonderful quantum program. It's the best platform out there rn

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@IBM 😎

@BobOner1991 💪

@IBM Thanks!😄

@BobOner1991 Awesome work! 🙌

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Published a new PDF-to-clean-data workflow case study. Path: text-based PDF → extraction → normalization → validation → CSV/Excel-style outputs → quality report → client handoff No OCR. No scanned PDFs. No universal parser claim. Sample review required before batch work.

Published a follow-up to my Shopify-style API reporting workflow case study. It focuses on the delivery layer: mock workflow evidence → validation boundary → private connector template → redaction → retry planning → client handoff templates

Published a new Shopify-style API reporting workflow case study. Vertical path: mock API data → pagination → field mapping → reporting tables → validation → CSV/Excel/SQLite exports → report No live Shopify API. No tokens. No store domains. No public runnable connector.

Updated my Python data quality ETL starter to v0.7.0. New optional path: public retail dataset → schema mapping → CLI validation/cleaning → SQLite + quality reports → benchmark report → summary CSV outputs

Updated my Python data quality ETL starter to v0.6.0. New optional path: cleaned data → schema profile → data dictionary → validation summary → feature-ready CSV → AI-ready manifest No LLM API call. No embeddings. No vector DB. No RAG chatbot.

I updated my Python data quality ETL starter to v0.5.0. This version adds a BI-ready reporting path: messy generated order data → validation and cleaning → analytics-ready rows → PostgreSQL reporting tables → lightweight SQL views → optional Metabase local dashboard demo

I updated my Python data quality ETL starter with an analytics-ready path. The workflow now goes beyond a cleaned CSV: messy generated order data → validation and cleaning → cleaned CSV → cleaned Parquet → DuckDB query demo → summary CSV tables → benchmark report

I’m building a focused Python data workflow portfolio: API / CSV / Excel / JSON input → cleaning, validation, transformation → CSV / SQLite / PostgreSQL / reporting-ready output Latest project: Data Quality ETL Starter — a small Python workflow for messy CSV, Excel, JSON.