SlideShare a Scribd company logo
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閃亮的火花
Gil @ Spark.TW
⼆零⼀七年⼗⼆⽉⼆⼗七⽇
https://www.marketingcloud.com/blog/meet-spark-inspiration-information/
1
/ 25
2
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outline
• Introduction to spark
• Introduction to shiny
• shiny X spark
• Case I: Pokemon GO
• Case II:Audience Map
3
/ 25
spark
4
/ 25 https://spark.apache.org/
5
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spark stack
sparkR
pyspark
https://www.safaribooksonline.com/library/view/data-analytics-with/9781491913734/ch04.html
Storage
Cluster
Manager
Processing
Engine
Access &
Interfaces
6
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Key Advantages of Spark & R
+
Fast!
Flexible
Scalable
Statistical!
Interactive
Packages
https://spark-summit.org/2014/wp-content/uploads/2014/07/SparkR-SparkSummit.pdf
7
/ 25
shiny
8
/ 25 https://shiny.rstudio.com/
(official website before 2017/09)
9
/ 25 https://shiny.rstudio.com/
(official website now)
10
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about shiny…
• 新的 framework,觀望⼀一下...
• google trend
• https://trends.google.com.tw/trends/explore?
date=all&q=R%20shiny,d3.js
• production
• https://shiny.rstudio.com/gallery/see-more.html
11
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about shiny…
• like MVC
• demo
http://13.115.248.137:3838/kmeans/
model ⬌ function.R
view ⬌ ui.R
control ⬌ server.R
12
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shiny vs. tableau
shiny tableau
pros
• free
• data science 

packages / algorithms
• scale out
• easy to production
• easy to maintain
cons
• hard to production
• hard to maintain
• not free
• hard to import 

packages / algorithms
• hard to scale out
13
/ 25
shiny vs. javascript+php
• javascript chart framework
• like ds.js, highchart.js, plotly.js, chart.js, echarts.js, …
• R 有相關套件 →	 1+ 1 > 2
• 分⼯工
• ⼀一⼈人團隊:data science + front-end →		不太可能、⽣生命有限,學
海海無涯
• 跨部⾨門合作、找前端⼯工程師
14
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shiny X spark
15
/ 25
shiny X spark
• 既然 shiny 可以⽤用 R 做 web UI、⽽而 spark ⼜又有 sparkR API 可以⽤用
• front-end to back-end
• 為了了視覺化⼀一些想法,省去⼤大量量的 ETL (AWS S3 → MYSQL)
• 快速地獲得資訊、探索性數據分析
• ⼀一般 BI tool
• 不易易 connect unstructured DB
• 無法 trigger job
16
/ 25
Pokemon GO
17
Interactive Maps
Web Framework
Compute Engine
+
Where is the
Dragonite nest ?
+
/ 25
data pipeline brief
structure DB
AWS RDS
ETL
BI tool
raw log
AWS S3
ETL
profile tables
AWS S3
AWS Redshift
data warehouse
AWS Athena
18
/ 25
AWS Redshift
data warehouse
AWS RDS
AWS S3 AWS S3
19
/ 25
“standing on the
shoulders of giants”
20
/ 25
data query ➡ dump ➡ visualization
cache
data query ➡ cache ➡ visualization
• local files
• mysql
• schema
• device id level
• UU monitor
21
/ 25
limitation & plan B (?)
R object
files ?
rpy2 ?
(convert python object to R)
user
API
22
/ 25
23
Control
View
Model
/ 25
24
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thanks
25

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