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Models

Segments

Overview

Sprinkle has redesigned and renamed cube feature into models with more simplicity and implementing additional features.

Models, a powerful feature in Sprinkle that helps in presenting multi-dimensional view of table. It can be created without any SQL queries, it is configured with simple point and click user interface.

Primarily Models comprises date dimension, dimensions, measures. Dimensions are the attributes on which the measures are calculated or grouped, and date dimensions are basically used to constrict the data within a timeframe.

Segments --> Models

The model page consists of 4 tabs, namely, Table, Column, Expression and Joins.

Table

The table tab contains an overview about the model, the name of the schema and the table on which the user wants to create a model. For creating a model, a table has to be selected and its columns needs to be mapped to Measures and Dimensions fields.

Columns

In the column tab, the list of columns of a given table will be catalogued. Here, the user selects dimensions, measures at different aggregates like sum, average, count, distinct count, etc whereas in date dimensions, the time frame can be set for yearly, quarterly, monthly, weekly, daily and hourly basis.

Expression

Sprinkle allows users to create calculated columns at model level. These calculated columns might be a dimension expression or measure expression on a given table. The tool also supports dynamic bucketing where users can give just the ranges and variable name and the buckets are created easily.

Joins

Joins is a feature which allows the user to join the fact table (from which the model is created) with other models. All the dimensions present in the joined Model can be used in this model. For example, an Order Model having customerId as one of the columns can be joined to Customer table. Similarly it can also be joined to Product table.

User Attributes

User attributes are used to create relationships between users and artifacts. Sprinkle supports user attribute concept. This keeps the relevant users informed when an artifact is updated; Say, the user has two showrooms for their business, one in Bangalore and the other in Mumbai. There will be store managers respectively who take care of the operations. With Sprinkle, while creating reports the user can access the user attribute feature to perform restrictions where one store manager would be able to access just their operational store’s data but not others’.

This helps in detailed understanding of reports to the concerned person and also channelizing the information.

import requests
from requests.auth import HTTPBasicAuth

auth =  HTTPBasicAuth(<API_KEY>, <API_SECRET>)
response = requests.get("https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE_ID>", auth)

print(response.content)

library('httr')

username = '<API KEY>'
password = '<API SECRET>'

temp = GET("https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>",
           authenticate(username,password, type = "basic"))

temp = content(temp, 'text')
temp = textConnection(temp)
temp = read.csv(temp)

/*Download the Data*/

filename resp temp;
proc http
url="https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>"
   method= "GET"  
   WEBUSERNAME = "<API KEY>"
   WEBPASSWORD = "<API SECRET>"
   out=resp;
run;

/*Import the data in to csv dataset*/
proc import
   file=resp
   out=csvresp
   dbms=csv;
run;

/*Print the data */
PROC PRINT DATA=csvresp;
RUN;

import requests
import json

url='http://hostname/api/v0.4/createCSV'

username='API_KEY'
password='API_SECRET'

files={'file':open('FILE_PATH.csv','rb')}
values={'projectname':PROJECT_NAME','name':'CSV_DATASOURCE_NAME'}

r=requests.post(url, files=files, data=values, auth=(username,password))

res_json=json.loads(r.text)

print(res_json['success'])

import requests
import json

url='http://hostname/api/v0.4/updateCSV'

username='API_KEY'
password='API_SECRET'

files={'file':open('FILE_PATH.csv','rb')}
values={'projectname':PROJECT_NAME','name':'CSV_DATASOURCE_NAME'}

r=requests.post(url, files=files, data=values, auth=(username,password))

res_json=json.loads(r.text)

print(res_json['success'])

import requests

url='https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>'

username='API_KEY'
password='API_SECRET'

r=requests.get(url,auth=(username,password))
print(r)
print(r.text)

import requests
import pandas as pd

url='https://<hostname>/api/v0.4/explores/infoByFolder/<SPACE_ID>'

username='API_KEY'
password='API_SECRET'

r=requests.get(url,auth=(username,password)).json()
df = pd.DataFrame(r)
print(df)

import requests
import pandas as pd

url='https://<hostname>/api/v0.4/folders/byOrgName/<ORG_NAME>'

username='API_KEY'
password='API_SECRET'

r=requests.get(url,auth=(username,password)).json()
df = pd.DataFrame(r)
print(df.loc[:,['name','id']])

import requests

import pandas as pd

import io

url='https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>'

secret='API_SECRET'

r=requests.get(url,headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

df = pd.read_csv(io.StringIO(r.text),sep=',')

import requests

import pandas as pd

import io

url='https://<hostname>/api/v0.4/segment/streamresult/<SEGMENT ID>'

secret='API_SECRET'

r=requests.get(url,headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

df = pd.read_csv(io.StringIO(r.text),sep=',')

import requests

import json

url='http://hostname/api/v.o4/createCSV'

files={'file':open('path/file.csv’')}

values={'projectname':PROJECT_NAME,'name':'csv_datasource_name/table_name'}

secret='API_SECRET'

r=requests.post(url, files=files, data=values, headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

res_json=json.loads(r.text)

import requests

import json

url='http://hostname/api/v.o4/updateCSV'

files={'file':open('path/file.csv’')}

values={'projectname':PROJECT_NAME,'name':'csv_datasource_name/table_name'}

secret='API_SECRET'

r=requests.post(url, files=files, data=values,headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

res_json=json.loads(r.text)