数据 · GitHub

An opinionated production-ready SQL-/Swagger-first RESTful JSON API written in Go, highly integrated with VSCode DevContainers by allaboutapps.

• Full local golang service development environment using Docker Compose and VSCode devcontainers that just works with Linux, MacOS and Windows.
• Adheres to the project layout defined in golang-standard/project-layout.

PYTHON E POSTGRESQL - EXTRACT TRANSFORM LOAD - ETL - DADOS PÚBLICOS DA RECEITA FEDERAL DO BRASIL - RFB, INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA - IBGE E AGÊNCIA NACIONAL DO PETRÓLEO, GÁS NATURAL E BIOCOMBUSTÍVEIS - ANP - PYTHON E POSTGRESQL

Run your own PaaS using Docker, Traefik, and great analytics

Apply different analysis & data science tools on your Telegram / Whatsapp chat and get exciting insights!

面向 dbt、SQL 和云数据仓库的开源数据工程智能体执行框架。提供 100 多种工具，支持 10 种数据仓库，并由 AI 驱动。

Stop staring at cron expressions: discover the missing visual calendar that finally lets you manage DAGs like a human.

Cross platform declarative DB framework for apps, inspired by SwiftData, refined by us.

• Zero-Boilerplate Schemas: No need to manually define your database schema (unlike Fluent for example). Vein generates all information it needs automatically from your model declarations using the @Model macro at compile time.
• Identity Map: Ensures a maximum of one in-memory class instance per database row and context.

Amiibo .bin 和 .nfc 数据库

Event-driven OneLake pipeline desktop tool with auto-merge Upsert, Python reporting, and Teams alert

• Event-driven trigger: A OneLake FileCreated event on the Files/inbox folder launches the pipeline automatically as soon as a .csv file lands, with a filter matching files whose subject ends in .csv. No schedule, no manual invocation.
• Filename resolution from the event: A Set variable activity extracts the file name from the trigger event payload, falling back to a pipeline parameter when the run is started manually. The resolved value is reused by every downstream activity.

A lightweight Analytics library that logs events, identifies the user, and dispatches the data at one. 📊

• 🔧 Easy Integration - Single API for multiple analytics services
• 📦 Modular Design - Add only the providers you need

Analyze and predict daily productivity using SQL, machine learning, and psychology. This project combines behavioral data, circadian rhythm analysis, and ElasticNet regression to model focus, stress, and performance, transforming work patterns into actionable insights.

• SQL feature engineering deriving psychology-informed signals (circadian alignment, Yerkes–Dodson arousal, break quality, meeting load, distraction penalty, health score)
• ElasticNet regression baseline with L1/L2 regularization for interpretable coefficients

Predict the profitability of potential coffee shop locations using SQL and Python. Combines data engineering with feature-rich regression modeling, visual analytics, and business insights to support data-driven site selection and retail decision-making.

• SQLite feature store built from CSV inputs
• SQL feature engineering through reusable views, so training and candidate sites are transformed identically (no train/serve skew)

Analyze employee performance and productivity using SQL and Python. Build HR dashboards to evaluate departmental KPIs, efficiency trends, and workload balance. Generates actionable insights through data visualization and KPI aggregation for business decision-making.

• SQL views (src/queries.sql) for department, employee, and daily-level KPI aggregation
• Data-quality validation before load: null checks, rating range, absences domain, non-negative counts

Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.

• SQL (SQLite) window-function feature engineering for per-user and per-day transaction statistics
• Point-in-time features: every aggregate uses ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING, so a transaction never sees its own amount or anything in the future