Analytics Playground¶
This directory is used for analyzing and testing datasets, specifically the
BigQuery public dataset thelook_ecommerce.
Contents¶
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notebooks/: Interactive Jupyter Notebooks (.ipynb) provided in both English (base name) and Korean (_kosuffix):01_data_profile_quality.ipynb/01_data_profile_quality_ko.ipynb: Automated data profiling and quality scans.02_data_insight.ipynb/02_data_insight_ko.ipynb: Automated column descriptions via Dataplex DataScans.03_dataset_insights.ipynb/03_dataset_insights_ko.ipynb: Exploratory dataset insights and statistics.04_glossary_setup.ipynb/04_glossary_setup_ko.ipynb: Loads the relational business glossary into Dataplex.05_graph_analysis.ipynb/05_graph_analysis_ko.ipynb: Property Graph creation, GQL multi-hop relationship analysis, and native visualization.06_bigquery_ai_ml_demo.ipynb/06_bigquery_ai_ml_demo_ko.ipynb: BigQuery Generative AI & ML analytics (remote LLM, embeddings, vector search).07_bigquery_ai_functions.ipynb/07_bigquery_ai_functions_ko.ipynb: BigQuery high-level AI functions (AI.CLASSIFY, AI.SIMILARITY, AI.IF, AI.SEARCH, Distillation).
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resources/: Supporting configuration and schema mapping files:- agent_test_queries.md / _ko: Verification guide and physical mapping scenarios.
business_glossary.json/business_glossary_ko.json: Custom Business Glossary terms.
Local Development & Testing Guide¶
The notebooks in this project are primarily designed to run in cloud runtimes like Colab Enterprise. However, if you wish to run and test these notebooks locally, please refer to the instructions below.
Dependency Syncing & Virtual Environment¶
You can manage and install the required dependencies using the pyproject.toml
file. We recommend using uv for fast and
reliable environment synchronization.
.venv/: A local Python virtual environment containing the necessary libraries installed via uv.