S2couple19+gongchuga+indo18+better !exclusive! -

| Step | Description | Tools / Libraries | |------|-------------|-------------------| | | Load s2couple19 , gongchuga , and indo18 into a common staging area. | Python pandas ( read_csv , read_excel , read_json ), Apache Spark (for large data), or ETL tools like Airflow. | | 2. Normalize schema | Align column names, data types, and key fields (e.g., a common id or timestamp). | Pandas ( rename , astype ), dbt for SQL‑based transformations. | | 3. Merge / Join | Perform inner/left joins on the key(s) to produce a unified table combined . | Pandas ( merge ), SQL ( JOIN ), Spark ( join ). | | 4. Enrich / Compute “better” metrics | Add derived columns that represent the “better” metric you care about (e.g., a score, a classification). | Scikit‑learn (for model‑based scores), custom functions, or SQL window functions. | | 5. Store results | Persist the combined dataset for fast reads. | PostgreSQL / MySQL, DuckDB (lightweight), or a Parquet file on S3. | | 6. API layer | Expose a REST/GraphQL endpoint that lets users filter, aggregate, or retrieve rows. | FastAPI (Python), Express (Node.js), Flask, or serverless functions (AWS Lambda). | | 7. Front‑end (optional) | Build a simple dashboard where users can explore the data. | Streamlit, Plotly Dash, or a React‑based UI. | | 8. Monitoring & Logging | Track request latency, error rates, and data freshness. | Prometheus + Grafana, CloudWatch, Sentry. |

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