title: Slack Json Rest API to big query table using Google cloud function
body: <p>My requirement is to automate data ingestion in big query. For this,as a first step ,I tried fetching data from platform such as Zoom,slack ,using Zoom/slack Rest APIs.For this I have written a cloud function .When tested,am able to Insert data into big query table only if API response size is low, .Big query log shows this warning <em>OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k</em> when I try fetch large api response.Usually each rest API I consume will consist of at least 20000 to 30000 records ,ie 8 to 15mb. Here is the code that I tried ```def snow_bq_push1(request):
  import requests
  import pandas as pd
  import json
  from pandas import json_normalize
  from pandas.io import gbq</p>
  <pre><code>user = 'uname'
  password = 'pwd'
  url = 'url'
  headers = {&quot;Content-Type&quot;:&quot;application/json&quot;, &quot;Accept&quot;:&quot;application/json&quot;}
  
  response = requests.get(url, auth=(user, password), headers = headers)
  
  if response.status_code != 200:
      print('Status:', response.status_code, 'Headers:', response.headers, 'Error Response:', response.json())
      exit()
  
  
  
  dta_ = []
  for i in response.json()['results']:
      dta_.append(i)
  
  df1 = pd.DataFrame(dta_)
  df2 = df1.tail(500)
  
  
  
  
  df2.to_gbq(destination_table='dataset.tablename', project_id= 'projectname', if_exists='replace')
  return '{&quot;status&quot;:&quot;success&quot;}' ``` 
  </code></pre>
  <p><strong>if I change to df1.tail(20000)</strong>,the function will run successfully,but data will not get inserted in the table,it shows the above mentioned warning . I am not sure cloud function would be the right choice for my case . Welcomes other suggestions too</p>