Automate Data Extraction: Parse Google Docs into Google Sheets
The Chaos That Sparked Automation
It was 2 AM. I had just generated 20 blog drafts using ChatGPT’s API and leaned back, expecting usable content. But what I got instead was a wall of unstructured text, just raw data. No headings, No formatting. Simply a chaotic digital mush.
That’s when it hit me — before any backend automation could take place, I had to teach my system how to read and understand this unstructured data or I would have to clean the data manually before the automation kicks in. What I was looking for is parsing this unstructured data into structured data like a spreadsheet. I need this document’s information parsed as I will be using it for generating image prompts to automatically generate images for these paragraphs.
At Brilliant Supply Chain, we’re building a Content Management System (CMS) that automates blog publishing, image generation, and metadata tagging. But the foundation of that entire system begins with one thing:
Why Structure Matters in Automation
The structure of a document determines how easily a machine can process it by identifying consistencies or repeating patterns. By seeing the rough drafts above a human can simply identify the text inside **__** and starting with ## are headings. But computers cannot differentiate it, we have to teach it how to read the data that we are giving it. Simply stating computers are dumb and can only do what they are told. Hence, a consistent pattern or format assists a predictable and repeatable process. Proving the importance of structure for automation.
Link > Learn how a properly formatted doc file appears to a program. Link <
So, the first step in the process was manually editing the drafts, so that our program can differentiate between H2, H3, paragraphs, tables, images, etc. My final blog looked something like this:
I edited the content, added the headings, added tables, and for special conditions where I want the text to have a specific format, I have enclosed the content inside special identifiers like Bic > __ Bic <. That will tell the program to parse this part of the paragraph separately, I will need to perform different operations on this text.
The Situation at Brilliant Supply Chain
We needed a way to:
- Read blog documents stored in Google Drive
- Identify and extract sections of text based on headings and other special sections.
- Store each section into individual columns (starting from column H) in a Google Sheet
- Feed that structured content into our system to generate image prompts for paragraphs using ChatGPT
In short, we needed a parsing engine that worked seamlessly with our file storage system and spreadsheet-based content tracker. This brought us to a natural decision point:
The Experiments Begin
Google Apps Script: Java Script
I began by writing a short Apps Script, built right into Google Sheets where the information will be stored after parsing.
The pseudo code looks something like this:
LOOP through data rows
GET serial, title, and first section
IF invalid, SKIP row
GET subfolder and doc
IF not found, LOG error and SKIP row
PARSE doc paragraphs into sections
WRITE sections to spreadsheet
LOG success message
See how this code works in detail by clicking the link below:
What the program does:
- Finds a folder in a master folder inside google drive
- Opens the subfolder and locates the corresponding doc file containing blog content
- Parse text between heading styles and other special sections like buttons, code, table etc.
- Write those chunks into the Google Sheet in a single row until we have parsed the entire content of the blog
There is a benefit to using this approach. It didn’t require external authentication, and it was easy to deploy within Google’s ecosystem. For smaller, controlled operations — this was perfect. But it comes with its own set of challenges. We are allowed only 90 minutes of processing time daily from google servers with the free google account. Processing activities like editing documents, running programs etc. We can extend it to 3 hours with paid versions and in fact 90 minutes is ample processing time for the entire range of back end operations for a single person.
But I am planning on scaling the system and will have multiple programs and people working on this, hence will put strain on limited resources. Moreover, if I have 1000 blogs to process daily (very unlikely) I will use up all my resources just parsing the documents. Containing the other programs on the google ecosystem. Hence, let’s explore a solution that can be easily scaled.
Python
Python is hands down my favorite tool to play with when working on projects like these as it allows me to pull the processes like parsing, blog generation, or image generation processes etc. on my local computer. Of Course added benefits like more robust and scalable programs, giving me more control over the customization like adding another layer of formatting on the parsed document before the document is automatically uploaded to the website. Further, the cherry on the top is we get to learn about google API and how we authenticate and give permissions to local programs to access google sheets, drive, doc files etc.
Python Pseudo Code looks something like this:
# Authenticate
Authenticate using service account file and scopes
Authorize gspread client
Open Google Sheet by ID and get worksheet
# Process Data
Get all values from worksheet
Get subfolders for parent folder ID
Loop through each row:
Find Google Doc in subfolder
Parse doc content into sections
Update sheet columns with sections
Log result (success or error)
Learn how this program works in detail by clicking the link below:
Which one is easier to implement?
| Feature | Google Apps Script | Python |
|---|---|---|
| Setup Time | Minimal | Moderate (API setup required) |
| File Size Limitations | ~50,000 characters | No limit |
| Runs On | Google Cloud | Local machine |
| External Setup | None | Requires credentials.json |
| Coding Experience Needed | Low | Moderate |
| Best For | Small teams, quick fixes | Large-scale parsing and automation |
What Can We Expect from Each Approach?
Both options get the job done, but their strengths differ depending on the environment.
| Expectation | Google Apps Script | Python |
|---|---|---|
| Cloud Compatibility | Seamless | Requires API integration |
| Local File Control | ❌ | ✅ Full local control |
| Team Collaboration | ✅ Easy to share with team | ❌ Harder without Git or hosting |
| Batch Processing | ❌ Slow for 100+ files | ✅ Fast, even with 1,000+ documents |
| Customization & Scaling | Moderate | High |
If you’re working solo or with a small team on Google Workspace, JavaScript is perfect.
If you want speed, customization, or are working with high volumes — Python is the winner.
Alternate possible solutions
Parsing isn’t just for blogs.
The same process applies to:
- PDF invoices — extracting vendor names, totals, due dates
- Scanned images — pulling text using OCR libraries like Tesseract
- Contract documents — extracting key clauses or dates
- Transcripts or interview recordings — converting them into structured summaries
Conclusion
Document parsing is one of the most powerful enablers of automation. It turns text chaos into structured clarity, and structured clarity into action. In our journey at Brilliant Supply Chain:
- We transformed unstructured blog drafts into organized data
- We built a system to scale content creation and automation
- We learned when to use lightweight tools like Google Apps Script
- And when to switch to power tools like Python for scale
The best part? These systems are just the beginning. Once our content is parsed, we can automate everything that comes after — image generation, publishing, SEO tagging, and more.