参加《21天晋级大数据实战营》——DAY17轻松探索数据背后的价值 - 数据湖探索实验小结

1、测试数据下载

http://obs-salepredict.obs.cn-north-1.myhwclouds.com/index.html
点击chicago.csv下载原始数据。

2、obs对象存储

3、桶创建

4、上传数据

5、DLI

6、队列

7、创建数据库

8、创建OBS表

CREATE TABLE chicago(
  room_id long,
  survey_id int,
  host_id long,
  room_type string,
  country string,
  city string,
  borough string,
  neighborhood string,
  reviews int,
  verall_satisfaction float,
  accommodates int,
  bedrooms float,
  bathrooms string,
  price float,
  minstay string,
  last_modified string,
  latitude double,
  longitude double,
  location string
) USING csv OPTIONS (path "s3a://dli-demo-richblue88/")

9、SQL编辑与查询

select
  case when price <= 50 then '1 (<50)' when price > 50
  and price <= 100 then '2 (50-100)' when price > 100
  and price <= 150 then '3 (100-150)' when price > 150
  and price <= 200 then '4 (150-200)' when price > 200
  and price <= 250 then '5 (200-250)' when price > 250
  and price <= 300 then '6 (250-300)' when price > 300
  and price <= 500 then '7 (300-500)' else '8 (>500)' end as price_level,
  count(*) / (
    select
      count(*)
    from
      chicago
  ) as percentage
from
  db1.chicago
group by
  price_level
order by
  price_level

图表展示