| geekyouth/SZT-bigdata |
2,055 |
|
0 |
0 |
over 2 years ago |
0 |
|
13 |
other |
Scala |
| 深圳地铁大数据客流分析系统🚇🚄🌟 |
| devlive-community/datacap |
999 |
|
0 |
0 |
8 months ago |
0 |
|
55 |
apache-2.0 |
Java |
| DataCap is integrated software for data transformation, integration, and visualization. Support a variety of data sources, file types, big data related database, relational database, NoSQL database, etc. Through the software can realize the management of multiple data sources, the data under the source of various operations conversion ... |
| lensesio/stream-reactor |
960 |
|
0 |
1 |
about 2 years ago |
1 |
December 27, 2018 |
75 |
apache-2.0 |
Scala |
| A collection of open source Apache 2.0 Kafka Connector maintained by Lenses.io. |
| realguoshuai/hadoop_study |
817 |
|
0 |
0 |
almost 4 years ago |
0 |
|
21 |
|
Java |
| 定期更新Hadoop生态圈中常用大数据组件文档 重心依次为: Flink Solr Sparksql ES Scala Kafka Hbase/phoenix Redis Kerberos (项目包含hadoop思维导图 印象笔记 Scala版本简单demo 常用工具类 去敏后的train code 持续更新!!!) |
| TurboWay/spiderman |
498 |
|
0 |
0 |
about 3 years ago |
0 |
|
3 |
mit |
Python |
| 基于 scrapy-redis 的通用分布式爬虫框架 |
| HariSekhon/HAProxy-configs |
198 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
mit |
Shell |
| 80+ HAProxy Configs for Hadoop, Big Data, NoSQL, Docker, Kubernetes, Elasticsearch, SolrCloud, HBase, MySQL, PostgreSQL, Apache Drill, Hive, Presto, Impala, Hue, ZooKeeper, SSH, RabbitMQ, Redis, Riak, Cloudera, OpenTSDB, InfluxDB, Prometheus, Kibana, Graphite, Rancher etc. |
| Raray-chuan/xichuan_note |
114 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
|
Java |
| xichuan的学习总结笔记,覆盖了java、spring、java其他常用框架,以及大数据相关组件等📚 |
| singgel/SpringBoot-Templates |
79 |
|
0 |
0 |
over 5 years ago |
0 |
|
5 |
|
Java |
| springboot和dubbo、netty的集成，redis mongodb的nosql模板， kafka rocketmq rabbit的MQ模板， solr solrcloud elasticsearch查询引擎 |
| KinoMin/bigdata-learning-notes |
79 |
|
0 |
0 |
about 2 years ago |
0 |
|
0 |
mit |
|
| snowch/movie-recommender-demo |
50 |
|
0 |
0 |
almost 4 years ago |
0 |
|
1 |
apache-2.0 |
Jupyter Notebook |
| This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop). |
