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with yest AS (
    SELECT currency, price
    FROM f_crypto_prices
    WHERE datekey = '20220315')
, today AS (
    SELECT currency, price
    FROM f_crypto_prices
    WHERE datekey = '20220316')

SELECT

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with yest AS (
    SELECT currency, price
    FROM f_crypto_prices
    WHERE TRUNC(datetime, 'day') = '20220315')
, today AS (
    SELECT currency, price
    FROM f_crypto_prices
    WHERE TRUNC(datetime, 'day') = '20220316')

SELECT

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with begin as
  (select currency,
          max(price) as price
   from crypto_prices_delta
   where date_key = {{date_key | sqlsafe}}
     and datetime =
       (select min(datetime)
        from crypto_prices_delta
        where date_key = {{date_key | sqlsafe}})
   group by currency), end as

peacing

import time

counter = 0
while True:
  
  ################
  ## code
  ## to fetch
  ## crypto API
  ## data

peacing

import requests
from datetime import datetime
import pandas as pd

import awswrangler as wr
import boto3

session = boto3.session.Session(aws_access_key_id=key_access_aws, aws_secret_access_key=key_secret_aws)
s3 = session.client('s3')

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# group by month using the loan created date and calculate some stats off the grouped data
def group_functions(x):
    d = {}
    
    d['loan_app_count'] = x['loan_amount'].count()
    d['loan_funded_count'] = x['funded_at'].count()
    d['conversion_rate'] = max(x['funded_at'].count() /x['created_at'].count(), 0)
    d['time_to_conversion_avg'] = x['time_to_conversion'].mean()
    
    return pd.Series(d, index=['loan_app_count', 'loan_funded_count', 

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# group by month using the loan created date and calculate some stats off the grouped data
def group_functions(x):
    d = {}
    
    d['loan_app_count'] = x['loan_amount'].count()
    d['loan_funded_count'] = x['funded_at'].count()
    d['conversion_rate'] = max(x['funded_at'].count() / x['created_at'].count(), 0)
    d['time_to_conversion_avg'] = x['time_to_conversion'].mean()
    
    return pd.Series(d, index=['loan_app_count','loan_funded_count', 

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import pyspark.pandas as ps 

# data path in HDFS
loans_filename = '/FileStore/tables/loans.csv'

loans_df = ps.read_csv(
  loans_filename,
  header=None, 
  names=['loan_amount', 'address', 'created_at', 'funded_at'],        
  infer_datetime_format=True,

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version: "3"
services:
  lakefs-setup:
    image: treeverse/lakefs:latest
    container_name: lakefs-setup
    depends_on:
      - postgres
      - minio-setup
    environment:
      - LAKEFS_AUTH_ENCRYPT_SECRET_KEY=some random secret string

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create table user_sessions
USING DELTA AS
( select user_id, event_date, event_action,
SUM(is_new_session) OVER (ORDER BY user_id, event_date) AS global_session_id,
SUM(is_new_session) OVER (PARTITION BY user_id ORDER BY event_date) AS user_session_id
FROM
(  select *,
  CASE WHEN unix_timestamp(event_date) - unix_timestamp(last_event) >= (24*60*60) OR 
  last_event is NULL THEN 1 ELSE 0 END AS is_new_session
  from (