1 Star 0 Fork 574

qinshouzhi / DataBand_1

forked from 昕有灵犀 / DataBand 
加入 Gitee
与超过 1200万 开发者一起发现、参与优秀开源项目,私有仓库也完全免费 :)
免费加入
克隆/下载
贡献代码
同步代码
取消
提示: 由于 Git 不支持空文件夾,创建文件夹后会生成空的 .keep 文件
Loading...
README
Apache-2.0

DataBand数据帮 轻量级一站式大数据分析平台

项目启动于2020-10-26,持续更新中。

完整开发使用文档

详情开发使用介绍

介绍

DataBand(数据帮),快速采集清洗,任务管理,实时流和批处理数据分析,数据可视化展现,快速数据模板开发,ETL工具集、数据科学等。是轻量级的一站式的大数据平台。 我们致力于通过提供智能应用程序、数据分析和咨询服务来提供最优解决方案。

软件架构

架构

技术栈

存储

  • 分布式存储:HDFS、HBase
  • 行式关系存储:MySQL、Oracle
  • 列式存储:ClickHouse
  • 列族存储:HBase、Cassandra
  • 文档库:ElasticSearch、MongoDB

计算

  • 计算引擎:Presto、Hive
  • 流处理:Storm、Flink

集成:

  • Flume
  • Filebeat
  • Logstash

前端技术栈

  • Vue
  • Element UI

后端技术栈

  • Spring Boot
  • Spring Cloud
  • MyBatis

工程说明

大数据模拟数据源生成数据(数据准备工程)

数据源:

数据源

  • databand-mock-api:接口模拟工具,模拟业务系统api;
  • databand-mock-log:日志模拟工具,手动产生大量的日志数据供调试测试,比如Syslog、log、CSV生成、Json、MySQL注入、RPC写、NetCat等;
  • databand-mock-mq:日志模拟工具,通过MQ写产生大量的日志数据供调试测试,比如RabbitMQ写、Kafka写等;
  • databand-mock-hadoop:大数据日志模拟工具,hdfs和mapreduce相关;

数据采集清洗(采集清洗工程)

ETL

  • databand-etl-mysql_ods:采集清洗mysql数据比如MySQL到ods临时中间库(包括Redis、Kafka等);
  • databand-etl-mysql_olap:采集清洗mysql数据到OLAP数据仓库;
  • databand-etl-mysql_hadoop:采集清洗mysql数据到Hadoop分布式存储;
  • databand-etl-logfile_ods:采集清洗半结构化日志文件,比如json、xml、log、csv文件数据到ods临时中间库;
  • databand-etl-logfile_olap:采集清洗半结构化日志文件数据到OLAP数据仓库;
  • databand-etl-logfile_hadoop:采集清洗日志文件数据到Hadoop分布式存储;
  • databand-etl-mq_ods:通过MQ消费采集数据,入ods库;
  • databand-etl-mq_olap:通过MQ消费采集数据,入OLAP库;
  • databand-etl-mq_hadoop:通过MQ消费采集数据,入Hadoop;- databand-ml:数据科学工程;

数据分析作业(定时作业调度工程)

  • databand-job-springboot:定时任务作业调度服务,支持shell,hive,python,spark-sql,java jar任务。
  • databand-streamjob-springboot:流数据作业,支持kafka数据消费至clickhouse、mysql、es等。

数据分析门户(后端管理和前端展示工程)

  • databand-admin-ui:前后端分离的纯前端UI工程,数据展现(目前未开发);
  • databand-admin-thymeleaf:后端权限、关系、站点配置管理(前后端不分离,正在开发的版本),基于若依框架;
  • databand-admin-api:数据api服务;
  • databand-admin-tools:BI工具集;

实时流数据(2021年-9月更新)

  • databand-rt-flinkstreaming:flink实时数据流处理。主要是PV、UV,涉及窗口、聚合、延时、水印、统计、checkpoint等基本用法;
  • databand-rt-redis:实时处理的一些缓存存储;
  • databand-rt-sparkstreaming:spark实时数据流处理,和flink的功能近似,主要structured streaming;

愿景目标

3年愿景目标 愿景目标

工程细节说明

databand-mock-api (模拟数据源API工程) API模拟工具

  • App.java:一个简单的mock控制台程序

api mock详情介绍

api mock工程源码

databand-mock-log (模拟数据源生成日志数据工程) 日志模拟工具

目前是简单的控制台小程序,直接运行main即可。

  • CsvMock.java:csv文件生成,运行后在"FILE_PATH"定义的文件夹中可找到csv文件:
  • LogMock.java:log文件生成,生成路径见配置文件:logback.xml。 win下默认“c:/logs/”,linux 或 mac下路径请自行修改:
  • JsonMock.java:json文件生成,在"FILE_PATH"定义的文件夹中可找到json文件:
  • XmlMock.java:xml文件生成,在"FILE_PATH"定义的文件夹中可找到json文件:
  • RpcMock.java:rpc输出,运行后可以用flume(或filebeat)进行测试,配置文件见:/flumeConf/avro-memory-log.properties:运行脚本: flume-ng agent --conf conf --conf-file /usr/app/apache-flume-1.8.0-bin/avro-memory-log.properties --name a2 -Dflume.root.logger=INFO,console
  • SyslogMock.java:syslog(udp)输出,运行后可以用flume(或filebeat)进行测试,配置文件见:/flumeConf/syslog-log.properties:
  • TcpMock.java:Tcp输出,运行后可以用flume进行测试,配置文件见:/flumeConf/syslog-log.properties:
  • MySQLMock.java:mysql数据生成,通过list键值对形式对数据表进行写操作。

log mock工程源码

databand-mock-mq (模拟数据源生成日志数据工程) MQ消息模拟生成工具

目前是简单的控制台小程序,直接运行main即可。

  • KafkaProducer.java:Kafka消息生成:
  • KafkaConsumer.java:Kafka消息消费:
  • RabbitMQProducer.java:RabbitMQ消息生成:
  • RabbitMQConsumer.java:RabbitMQ消息消费:

mq mock工程源码

使用说明

数据源日志

类型分为:

  • CSV日志,用于批处理,采用UTF-8字符集,每行(\r\n)表示一条记录,每条记录中各个字段的值使用双引号括起来,并使用逗号(,)分隔;
  • Kafka 日志,用于流处理,生产者策略性的产生一些有偏移属性的带日期时间数据。

业务:

  • a)产品销售日志,采用CSV格式;
  • b)节目播出日志,采用CSV格式;
  • c)搜索热词日志,采用kafka;
  • d)广告播放日志,采用kafka;

数据定义,批处理类型日志,原始数据源为csv,暂时以这两个业务作为批处理数据演示,实际上平台将是与业务无关的,只关注数据流和数据服务。

一、产品销售csv日志: 处理类:org.databandtech.logmock.ProductSalesCSVLog

  • 1 产品id productId
  • 2 产品分类id categoryId
  • 3 型号规格 modelId
  • 4 颜色 color
  • 5 买家id userId
  • 6 购买日期 saleDatetime
  • 7 购买数量 buyCount
  • 8 购买金额 buyTotle
  • 9 折扣金额 buyDiscount
  • 10 城市 cityCode
  • 11 地址 address

二、节目播出csv日志 处理类:org.databandtech.logmock.ShowsCSVLog

  • 1 用户id userId
  • 2 状态类型码 status
  • 3 城市 cityCode
  • 4 区县 areaCode
  • 5 收视开始时间 beginTime
  • 6 收视结束时间 endTime
  • 7 节目ID showId
  • 8 栏目ID columnId
  • 9 频道ID channelId
  • 10 高清标志码 hd
  • 11 节目类型码 showType

状态类型码:

  • 1:"tv_playing"、2:"vod_playing"、3:"browsing"、4:"tvod_playing"、5:"ad_event" 、6:"external_link"、7:"order"

高清标志码:

  • 0:标清、1:高清、2:智能、3:其他

节目类型码:

  • 电视剧:tv、电影:movie、综艺:variety、其他:other

流类型日志,原始数据源为kafka,暂时以这两个业务作为流数据演示,实际上平台将是与业务无关的,只关注数据流和数据服务。

三、搜索热词日志: 处理类:org.databandtech.mockmq.HotWordKafkaLog

Kafka Topic: HOTWORDS

  • 1 KEYWORD 热词
  • 2 USERID 用户id
  • 3 TS 搜索时间

四、广告监测日志 处理类:org.databandtech.mockmq.AdKafkaLog

Kafka Topic: ADMONITOR

  • 1 OS 设备的操作系统类型
  • 2 UID 用户id
  • 3 MAC1 MAC地址
  • 4 MACCN 当前联网形式
  • 5 IP IP
  • 6 ROVINCECODE 所属省份代码
  • 7 CITYCODE 所属城市代码
  • 8 AREACODE 所属区县代码
  • 9 TS 客户端触发的时间
  • 10 ADMID 广告素材
  • 11 ADID 广告主
  • 12 APPNAME 应用名称

分布式存储-原始记录备份

从CSV日志生成的数据源需要做原始文档的备份存储,使用HDFS,而kafka流数据则依据具体情况选择是否存入HDFS或者HIVE,还是直接清洗后,存入ClickHouse等。

将CSV日志原始存档进HDFS的方式:

  • 1、直接Put文件目录进hdfs文件系统;
  • 2、使用Flume的spooling-to-hdfs,使用方法见databand-etl-flume中的spooling-memory-hdfs2.properties
  • 3、使用databand-job-springboot定时任务,类型为HdfsBackupJob。

将kafka存进HDFS的方式:

  • 1、使用Flume的kafka-to-hdfs,使用方法见databand-etl-flume中的kafka-flume-hdfs.properties;
  • 2、使用Flink或者Storm导入,例子见databand-etl-storm、databand-etl-flink;
  • 3、使用kafka的客户端库和hdfs客户端库,自行开发。

分布式存储-数据仓库存档

产品表外部表,建表语句

CREATE EXTERNAL TABLE product(address STRING,buycount INT,buydiscount INT,buytotle INT,categoryid STRING,citycode STRING,color STRING,modelid STRING,productid STRING,saledatetime STRING,userid STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n' STORED AS TEXTFILE LOCATION '/home/product';

节目表外部表,建表语句

CREATE EXTERNAL TABLE show(areacode STRING,channelid STRING,citycode STRING,columnid STRING,hd INT,showdatetime STRING,showduration INT,showid STRING,status STRING,userid STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n' STORED AS TEXTFILE LOCATION '/home/show';

– 可以用load data引入数据,覆盖引入: – LOAD DATA LOCAL INPATH '/home/product/2020-12-20.csv' – OVERWRITE INTO TABLE product;

– HDFS 文件 – hive> LOAD DATA INPATH '/home/product/2020-12-20.csv' – > OVERWRITE INTO TABLE product;

– 本地文件 – hive> LOAD DATA LOCAL INPATH '/home/product/2020-12-20.csv' – > OVERWRITE INTO TABLE product;

Count计数语句

  • 计算累计订单数:select count(1) from product;
  • 计算地区为广州的订单数:select count(1) from product where cityCode="广州";
  • 计算节目数:select count(1) from show;
  • 计算日志为破茧的记录数:select count(1) from show where showid="破茧";
  • 计算2020-12月的全部DELL电脑订单金额:select sum(buytotle) from product where modelid="DELL" and instr(saledatetime,"2020-12")>0;

可以测试一下hive输出结果: hivejob

分析规划 - 统计指标规划

产品销售日志 统计规划

X轴维度 - key

  • 时间维度: 年、季、月、周;
  • 产品分类维度:按产品类型,如电视、PC;
  • 按产品型号规格维度;
  • 按城市分组维度;
  • 按购买者维度;

Y轴维度 - value

  • 订单数
  • 订单金额

指标:

  • 产品各分类订单数,product_order_count_by_cate,按年、季、月、周、天;
  • 产品各型号规格订单数,product_order_count_by_model,按年、季、月、天;
  • 各城市分布订单数,product_order_count_by_city,按年、月;
  • top20订购者订单数,product_order_count20_by_user,按年、月;
  • 产品各分类订单金额,product_order_amount_by_cate,按年、季、月、周、天;
  • 产品各型号规格订单金额,product_order_amount_by_model,按年、季、月、天;
  • 各城市分布订单金额,product_order_amount_by_city,按年、月;
  • top20订购者订单金额,product_order_amount20_by_user,按年、月;

节目播出日志 统计规划

X轴维度 - key

  • 时间维度: 年、季、月、周;
  • 城市维度
  • 频道维度
  • 节目维度
  • 用户维度

Y轴维度 - value

  • 播放时长
  • 播放次数

指标:

  • 按城市分组播放时长,show_dration_by_city,按年、季、月、周、天;
  • 按频道分组播放时长,show_dration_by_channel,按年、季、月;
  • 按节目top20播放时长,show_dration20_by_show,按年、月;
  • 按用户top20播放时长,show_dration20_by_user,按年、月;
  • 按城市分组播放次数,show_times_by_city,按年、季、月、周、天;
  • 按频道分组播放次数,show_times_by_channel,按年、季、月;
  • 按节目top20播放次数,show_times20_by_show,按年、月;
  • 按用户top20播放次数,show_times20_by_user,按年、月;

搜索热词日志 统计规划

待完成

广告监测日志 统计规划

待完成

批处理统计分析

产品销售日志 批处理统计分析计算

产品各分类订单数(按天),hive sql:

  • select categoryid,saledatetime,sum(buycount) from product group by categoryid,saledatetime order by saledatetime;

产品各分类订单数(按天,指定某天,用于增量定时任务导出统计)

  • select categoryid,saledatetime,sum(buycount) from product group by categoryid,saledatetime having saledatetime="2020-12-30" order by saledatetime ;

其他分析查询SQL略,按天统计的数据都有了,按周、月、季、年就以此聚合。

导出结果到本地文件,相同记录则覆盖

use default;
-- Save to [LOCAL]
INSERT OVERWRITE LOCAL DIRECTORY '/home/product_order_count_by_cate'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE
-- SQL
select categoryid,saledatetime,sum(buycount) from product group by categoryid,saledatetime order by saledatetime;

导出结果到HDFS,相同记录则覆盖

use default;
-- Save to HDFS
INSERT OVERWRITE DIRECTORY '/home/product_order_count_by_cate'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE
-- SQL
select categoryid,saledatetime,sum(buycount) from product group by categoryid,saledatetime order by saledatetime;

执行完之后可以查看hdfs的记录是否已经保存

  • hadoop fs -tail /home/product_order_count_by_cate/000000_0

hivejobtohdfs

节目播出日志 批处理统计分析计算

批处理定时任务

databand-job-springboot

databand-job-springboot:定时任务作业调度服务,支持Shell,Hadoop MR,HiveSQL,Python,Spark,Flink,JavaJar任务。

  • 注入见TaskConfig的方法scheduledTaskJobMap() 的例子,目前仅提供java注入,未来有数据库加载注入和配置文件注入

定时任务类型分为:JobType:

  • 命令行任务,CommandExecuteJob的实例,
  • 原始记录备份(从本地),从数据源中备份原始数据到HDFS,HdfsBackupJob;
  • 原始记录备份(到本地),从HDFS数据源中备份原始数据到本地文件,HdfsToLocalFileJob;
  • Hive SQL任务,HiveSqlQueryJob,hive执行DQL查询任务,需要返回数据集,并对数据集进行分析数据库存储,存储的数据用于报表图表等展现,必须实现SavableTaskJob接口;
  • Hive SQL任务,HiveSqlExecuteJob,hive执行脚本任务,用于DDL、DML操作,比如load data等;
  • 统计分析计算,Hadoop中运行MR,执行处理,HadoopMRJob;
  • 更多任务类型,不一一列出。

其中每种类型都有针对各个统计指标的实例:JobInstances 和 JobType是多对一的关系。

运行方式:

3、启动单一任务,目前还没有统一的管理界面,未来会开发完善:

流数据任务

databand-streamjob-springboot

databand-streamjob-springboot:流数据持久化任务

类型分为:JobType:

  • 原始记录备份,从数据源中备份原始数据到HDFS,HdfsBackupJob;
  • 流数据存储为MySQL记录(从kafka),KafkaToMysqlJob;
  • 流数据存储为ClickHouse记录(从kafka),KafkaToClickHouseJob;
  • 流数据存储为Kafka记录(从kafka),KafkaToKafkaJob;
  • 流数据存储为Elasticsearch记录(从kafka),KafkaToEsJob;

数据分析门户(后端管理和前端展示工程)

  • databand-admin-ui:前后端分离的纯前端UI工程,数据展现(目前未开发);
  • databand-admin-thymeleaf:后端权限、关系、站点配置管理(前后端不分离,正在开发的版本),基于若依框架;
  • databand-admin-api:数据api服务;
  • databand-admin-tools:BI工具集;

实时流数据介绍(2021年-9月更新)

  • databand-rt-flinkstreaming:flink实时数据流处理。主要是PV、UV,涉及窗口、聚合、延时、水印、统计、checkpoint等基本用法。入口类是KafkaConsumerApp,数据源为kafka,使用databand-mock-mq的EpgVodKafkaProducer可以自动产生kafka模拟数据,模拟数据的数据类型是EpgVod对象的json形式; databand-rt-flinkstreaming
  • databand-rt-redis:实时处理的一些缓存存储;
  • databand-rt-sparkstreaming:spark实时数据流处理,和flink的功能近似,主要structured streaming,入口类是KafkaApp;
项目启动于2020-10-26...更多工程正在开发中,不定期更新,因为都是使用业余时间,如更新稍慢还请谅解,但这个工程一定不会是烂尾工程,长期更新维护,本人的工作重心领域也是大数据。
Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

简介

DataBand(数据帮),快速采集清洗,任务管理,实时流和批处理数据分析,数据可视化展现,快速数据模板开发,ETL工具集、数据科学等。是轻量级的一站式的大数据平台。 展开 收起
Java
Apache-2.0
取消

发行版

暂无发行版

贡献者

全部

近期动态

加载更多
不能加载更多了
Java
1
https://gitee.com/qinshouzhi/databand_1.git
git@gitee.com:qinshouzhi/databand_1.git
qinshouzhi
databand_1
DataBand_1
master

搜索帮助

14c37bed 8189591 565d56ea 8189591