pg_mooncake is a Postgres extension that adds a custom table access method ('mooncake') backed by Apache Iceberg, enabling real-time analytics on columnar data stored in Iceberg tables. It integrates DuckDB (via pg_duckdb) as the query engine for fast analytics and provides a background worker (moonlink) that streams changes from regular Postgres heap tables to Iceberg tables via logical replication, ensuring sub-second data freshness. The extension exposes SQL procedures to create columnstore mirrors of Postgres tables, manage snapshots, load files, and optimize storage. It is built using the pgrx framework for Rust-based Postgres extensions and supports Postgres versions 14-18. The architecture is a hybrid of a shared library loaded into the Postgres backend and a separate background worker process. The codebase is well-structured and actively developed, though many table access method callbacks are still stubs, indicating it is in an early but functional stage.
This repository is a curated skill library for data analysis workflows, designed to be consumed by AI agents (Claude Code, Codex). It contains 31 skills organized into 6 categories: data quality validation, documentation/knowledge management, data analysis/investigation, data storytelling/visualization, stakeholder communication, and workflow optimization. Each skill is defined by a SKILL.md file with YAML frontmatter, a step-by-step process, required inputs/outputs, and optional scripts, reference materials, and asset templates. The scripts are standalone Python CLIs that generate synthetic data for demo purposes and can be adapted to real datasets. The repo is not a deployable application or a data pipeline; it is a structured knowledge base and toolset for performing data analysis tasks programmatically. It covers common analytical patterns such as null counting, duplicate detection, cohort retention, funnel conversion, A/B testing, time series anomaly detection, metric calculation, and more. The codebase is well-organized and follows consistent conventions, but shows signs of staleness (future dates in README and LICENSE, no recent activity). Dependencies are not pinned, and no environment definitions are provided, limiting reproducibility without manual setup.
universal-analytics is a Node.js library that implements Google's Universal Analytics Measurement Protocol, enabling server-side tracking of pageviews, events, transactions, exceptions, timings, and screenviews. It provides a `Visitor` class that queues hits and sends them as HTTP POST requests to Google Analytics, with support for batching, parameter validation, and persistent session data. The library includes an Express middleware for automatic client ID detection from cookies or sessions. The codebase is small, well-structured, and thoroughly tested. It demonstrates solid engineering practices: clean API design with flexible argument signatures, context-propagation for daisy-chaining, and a simple yet effective request module using native Node.js HTTP/HTTPS libraries. However, the library targets a deprecated service (Universal Analytics, being replaced by GA4), and its dependencies are outdated (e.g., uuid version 14.0.0 does not exist on npm). The last release (0.5.4) and the repository's commit activity suggest it is no longer actively maintained. As a library, it is tightly coupled to the Measurement Protocol specification, but internal components like the UUID utility and HTTP helper are reusable.
The Pentaho Platform is the core backend for the Pentaho Business Analytics server. In production, it serves as a content repository (JCR/Jackrabbit), authentication/authorization system, scheduler (Quartz), and a plugin-based execution engine for BI actions (reports, OLAP, data integration). The system is a monolithic Java EE application deployed on Tomcat, using Spring for dependency injection, Hibernate for persistence, and GWT for the legacy user console. It provides REST and SOAP APIs for repository management, security administration, and job scheduling. The codebase is organized into modules (api, core, extensions, repository, scheduler, user-console) and relies on external libraries such as Apache Jackrabbit, Quartz, Mondrian, Kettle, and Spring Security. Architecturally, the platform is built around a central static service locator (PentahoSystem) and a large set of interfaces for actions, components, and services. It supports multi-tenant repository paths, pluggable authentication (LDAP, JDBC, memory), and a rule-based authorization system. The platform is functionally comprehensive but carries significant technical debt: deprecated interfaces, mixed JUnit versions, outdated front-end technologies (Dojo, GWT), and over-engineered exception hierarchies. Some modules (e.g., authorization decisions, cache expiration, file content decoration) are well-designed and potentially reusable.