victorres11

from PyPDF2 import PdfFileReader, PdfFileMerger, PdfFileWriter
from datetime import datetime
import os, os.path
import errno
import sys

INTERNAL_FACTSHEET = "FS_Int"
MS_ESG_Report      = "ESG"
CMP                = "CMP"
REPORT_TYPES = [INTERNAL_FACTSHEET, MS_ESG_Report, CMP]

victorres11

Justin - I wasn’t sure exactly what you wanted, so I just took a stab at previewing some projects and technologies I’ve worked on in the past. It’s pretty widespread as I’m fairly tool agnostic, but would love to understand your pain points/needs to make any examples more tailored to potential opportunities that are relevant to your business. Looking forward to discussing further in greater detail!

Semi-Automated SDR Lead Generation:

Summary: This project was an experiment where I teamed up with the head of sales for an agency, to see if we could somewhat automate leads as he wanted to focus on closing deals more than manually going through his cold outreach. We defined the type of ecommerce companies he wanted to reach out to, and then set up a semi-automated omni channel e-mail/linkedin outreach campaign to see if we could get some intro calls scheduled:

Technologies used:

Web Scraping Services (get leads based on criteria)

Clearbit Data Enrichment (enrich leads)

victorres11

// callback to trigger code upon a landing page form submit - the main call to action.
window.instapageFormSubmitSuccess = function( form ){
   // Instapage is a bit quirky with how it captures values of the form submit, but you should be able to use this code and replace the form fields with whatever your page is capturing.
   var firstName = ijQuery( form ).find( 'input[name="' + window.base64.base64_encode( 'First Name' ) +'"]').val();
   var lastName  = ijQuery( form ).find( 'input[name="' + window.base64.base64_encode( 'Last Name' ) +'"]').val();
   var email     = ijQuery( form ).find( 'input[name="' + window.base64.base64_encode( 'Email') +'"]').val();
   
   // Segment identify call.
   analytics.identify(email, {
       firstName: firstName, 

victorres11

select integration_type, count(distinct(host_id))
  -- , sum(is_ecr_amazon) as is_ecr_amazon_count
  ,CAST((sum(is_ecr_amazon) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_ecr_amazon_pct
  -- , sum(is_gcr_io) as is_ecr_amazon_percent_of_containers
  ,CAST((sum(is_gcr_io) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_gcr_io_pct
  -- , sum(is_quay_io) as is_quay_io_count
  ,CAST((sum(is_quay_io) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_quay_io_pct
  -- , sum(is_dockercloud) as is_dockercloud_count
  ,CAST((sum(is_dockercloud) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_dockercloud_pct
  -- , sum(is_docker_io) as is_docker_io_count

victorres11

select integration_type, count(distinct(host_id))
  -- , sum(is_ecr_amazon) as is_ecr_amazon_count
  ,CAST((sum(is_ecr_amazon) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_ecr_amazon_pct
  -- , sum(is_gcr_io) as is_ecr_amazon_percent_of_containers
  ,CAST((sum(is_gcr_io) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_gcr_io_pct
  -- , sum(is_quay_io) as is_quay_io_count
  ,CAST((sum(is_quay_io) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_quay_io_pct
  -- , sum(is_dockercloud) as is_dockercloud_count
  ,CAST((sum(is_dockercloud) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_dockercloud_pct
  -- , sum(is_docker_io) as is_docker_io_count

victorres11

select integration_type, count(distinct(host_id))
  -- , sum(is_ecr_amazon) as is_ecr_amazon_count
  ,CAST((sum(is_ecr_amazon) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_ecr_amazon_pct
  -- , sum(is_gcr_io) as is_ecr_amazon_percent_of_containers
  ,CAST((sum(is_gcr_io) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_gcr_io_pct
  -- , sum(is_quay_io) as is_quay_io_count
  ,CAST((sum(is_quay_io) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_quay_io_pct
  -- , sum(is_dockercloud) as is_dockercloud_count
  ,CAST((sum(is_dockercloud) * 100.0 / count(distinct(host_id))) AS numeric(10,2)) as is_dockercloud_pct
  -- , sum(is_docker_io) as is_docker_io_count

victorres11

select count(distinct(org_id)) as total_org_count
      -- ,sum(only_latest) as only_latest_count
      ,CAST((sum(only_latest) * 100.0 / count(distinct(org_id))) AS numeric(10,2)) as only_latest_percent_of_orgs
      -- ,sum(only_versioned) as only_versioned_count
      ,CAST((sum(only_versioned) * 100.0 / count(distinct(org_id))) AS numeric(10,2)) as only_versioned_percent_of_orgs
      -- ,sum(both_latest_and_versioned) as both_latest_and_versioned_count
      ,CAST((sum(both_latest_and_versioned) * 100.0 / count(distinct(org_id))) AS numeric(10,2)) as both_latest_and_versioned_percent_of_orgs
      -- ,sum(neither) as neither_count
      ,CAST((sum(neither) * 100.0 / count(distinct(org_id))) AS numeric(10,2)) as neither_percent_of_orgs

victorres11

select location, count(distinct(org_id)) as total_org_count
      ,sum(sha256) as sha256
      ,sum(amazon_ecs_agent) as amazon_ecs_agent
      ,sum(postgres) as postgres
      ,sum(mongo) as mongo
      ,sum(mysql) as mysql
      ,sum(nginx) as nginx
      ,sum(elasticsearch) as elasticsearch
      ,sum(heapster) as heapster
      ,sum(ubuntu) as ubuntu

victorres11

select integration_type, count(distinct(org_id)) as total_org_count
      ,sum(sha256) as sha256
      ,CAST((sum(sha256) * 100.0 / count(distinct(org_id))) AS numeric(10,2)) as sha256_percent_of_orgs
      -- ,sum(amazon_ecs_agent) as amazon_ecs_agent
      ,CAST((sum(amazon_ecs_agent) * 100.0 / count(distinct(org_id))) AS numeric(10,2)) as amazon_ecs_agent_percent_of_orgs
      -- ,sum(postgres) as postgres
      ,CAST((sum(postgres) * 100 / count(distinct(org_id))) AS numeric(10,2)) as postgres_ecs_agent_percent_of_orgs
      -- ,sum(mongo) as mongo
      ,CAST((sum(mongo) * 100 / count(distinct(org_id))) AS numeric(10,2)) as mongo_ecs_agent_percent_of_orgs
      -- ,sum(mysql) as mysql