# geekyouth/SZT-bigdata

深圳地铁大数据客流分析系统🚇🚄🌟

Repository: https://github.com/geekyouth/SZT-bigdata
Canonical: https://ross.abutalabs.com/products/szt-bigdata
Homepage: https://github.com/geekyouth/SZT-bigdata
Language: Scala
License: NOASSERTION
License Family: other
Topics: kafka, szt-bigdata, flink, spark, scala, elasticsearch, kibana, hive, redis, mongodb, hadoop, kylin, mysql, phoenix, hbase, zookeeper, clickhouse, docker, cdh6, springboot
Last push: 2026-05-12T06:07:08+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 82, release rhythm 8, longevity 100
- inputs: {"age_days": 2333, "days_push": 113, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2475, forks 613 (observed 2026-08-28T04:06:54.968380+00:00)

## What it is
A big data passenger flow analysis system for the Shenzhen Metro, built on Shenzhen Tong smart-card swipe data. It demonstrates ETL and analytics pipelines using Flink, Spark, Kafka, Hadoop, Hive, HBase, Elasticsearch, ClickHouse, Redis, and related CDH components.

## Use cases
- analyze metro passenger flow from smart card data
- learn big data ETL pipelines with flink and spark
- practice hadoop hive hbase elasticsearch integration
- build realtime streaming analytics with kafka and flink
- study big data technology stack comparison and selection
- process transit card swipe data at scale

## When to choose
- you want a realistic end-to-end big data learning project covering many popular frameworks
- you need reference code for ETL from kafka/redis into hbase, elasticsearch, hive, or clickhouse
- you are exploring transit or passenger flow analytics

## When to avoid
- you need a production-ready transit analytics product with support
- you want a minimal stack rather than a deliberately broad multi-framework demo
- you require a permissive license for commercial use (license is non-standard)

## Facets
- artifact type: application
- maturity: maintenance
- function: etl, streaming, data-science, analytics, search-engine, caching, database, message-queue, data-visualization
- domain: big-data, analytics, developer-tools, tutorials
- platform: jvm, cloud, self-hosted
- tags: big-data, flink, spark, hadoop, clickhouse, elasticsearch, hbase, hive, kafka, smart-card-data, transit-analytics, learning-project, scala, cdh, data-engineering, transportation, docker, linux

## Member repositories
- geekyouth/SZT-bigdata (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:54.968380+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:28:22.803018+00:00, confidence not recorded.
  - readme: https://github.com/geekyouth/SZT-bigdata (fetched 2026-08-28T04:06:54.968380+00:00, sha aee63f185240)
  - homepage: https://github.com/geekyouth/SZT-bigdata (fetched 2026-08-29T10:10:19.926683+00:00, sha 42b9bb627fcb)
- Data as of 2026-08-30T08:39:29.467469+00:00.