Web Scraping & Bots · Lesson

Storing Data in NoSQL Databases

Learn when and how to persist scraped data in document-oriented NoSQL stores like MongoDB for flexible, schema-light storage.

Storing Data in NoSQL Databases is a free Web Scraping & Bots lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Web Scraping & Bots learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

When SQL Is Not Enough

Scraped data is often irregular: different pages yield different fields, nested structures, and evolving shapes. Rigid SQL schemas can be painful here.

NoSQL document databases store flexible JSON-like records, making them a natural fit for messy web data.

Documents and Collections

In a document store like MongoDB:

A document is one JSON-like record.
A collection groups related documents (like a table).
Documents in one collection need not share the same fields.

{
  "title": "Widget",
  "price": 9.99,
  "tags": ["tools", "sale"],
  "vendor": { "name": "Acme", "rating": 4.5 }
}

Connecting with PyMongo

The pymongo driver connects Python to MongoDB. Grab a database and a collection handle to start writing.

from pymongo import MongoClient

client = MongoClient('mongodb://localhost:27017')
db = client['scraping']
products = db['products']

Inserting Documents

Insert a single scraped record with insert_one or a batch with insert_many. MongoDB assigns an _id automatically.

record = {'title': 'Gadget', 'price': 14.5, 'tags': ['new']}
result = products.insert_one(record)
print(result.inserted_id)

Avoiding Duplicates with Upsert

Re-running a scraper should not create duplicate rows. An upsert updates the matching document or inserts it if absent, keyed by a stable field like the product URL.

products.update_one(
    {'url': record['url']},
    {'$set': record},
    upsert=True
)

Unique Indexes

Enforce uniqueness at the database level with an index. This protects integrity even if your code has a bug.

products.create_index('url', unique=True)

Querying Stored Data

Retrieve records with filter documents. Operators like $gt and $in express conditions.

cheap = products.find({'price': {'$lt': 10}})
for doc in cheap:
    print(doc['title'], doc['price'])

Storing Nested and Array Data

Unlike flat SQL columns, documents keep nested objects and arrays natively. This preserves the original structure of scraped pages without join tables.

review_doc = {
  'product': 'Widget',
  'reviews': [
    {'user': 'a', 'stars': 5},
    {'user': 'b', 'stars': 4}
  ]
}
db['catalog'].insert_one(review_doc)

Bulk Writes for Speed

For large scrapes, batch operations dramatically reduce round trips. Collect writes and flush them together.

from pymongo import UpdateOne

ops = [UpdateOne({'url': r['url']}, {'$set': r}, upsert=True) for r in batch]
products.bulk_write(ops)

SQL vs NoSQL for Scraping

Choose based on your data:

NoSQL for variable, nested, fast-changing records.
SQL when fields are stable and you need joins or strict constraints.

Many pipelines stage raw data in NoSQL, then transform into SQL for analysis.

Adding Timestamps and Metadata

Always stamp each scraped document with when it was captured and its source. This lets you track freshness, debug bad runs, and re-scrape stale records selectively.

from datetime import datetime

record['scraped_at'] = datetime.utcnow()
record['source'] = 'site.com'
products.insert_one(record)

Quick Check

Test your understanding of NoSQL storage.

Recap

You learned to persist scraped data in NoSQL: documents and collections, connecting with PyMongo, upserts and unique indexes to prevent duplicates, querying, nested data, and bulk writes.

Document stores give scrapers flexible, scalable persistence.

Frequently asked questions

Is the “Storing Data in NoSQL Databases” lesson free?

Yes — the full text of “Storing Data in NoSQL Databases” is free to read here on the web, and the Web Scraping & Bots course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Web Scraping & Bots course, upgrade to CoddyKit PRO.

What will I learn in “Storing Data in NoSQL Databases”?

Learn when and how to persist scraped data in document-oriented NoSQL stores like MongoDB for flexible, schema-light storage. You practise Web Scraping & Bots with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Web Scraping & Bots?

No prior experience is required. Web Scraping & Bots on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Storing Data in NoSQL Databases” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Web Scraping & Bots lesson?

Yes. Every Web Scraping & Bots lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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