TikTok users generate tons of data that are valuable for analysis.

Which hashtags are trending now? What is an influencer's engagement rate? What topics are important for a content creator? You can find answers to these and many other questions by analyzing TikTok data. However, for analysis, you need to extract the data in a convenient format. In this blog, we'll explore how to scrape TikTok using Crawlee for Python.

Key steps we'll cover:

1. Project setup
2. Analyzing TikTok and determining a scraping strategy
3. Configuring Crawlee
4. Extracting TikTok data
5. Creating TikTok Actor on the Apify platform
6. Deploying to Apify

Build a price tracker with Crawlee for Python to scrape product details, export data in multiple formats, and send email alerts for price drops, then deploy and schedule it as an Apify Actor.

Bluesky is an emerging social network developed by former members of the Twitter(now X) development team. The platform has been showing significant growth recently, reaching 140.3 million visits according to SimilarWeb. Like X, luesky generates a vast amount of data that can be used for analysis. In this article, we’ll explore how to collect this data using Crawlee for Python.

Key steps we will cover:

1. Project setup
2. Development of the Bluesky crawler in Python
3. Create Apify Actor for Bluesky crawler
4. Conclusion and repository access

Crawlee for Python v0.6 is here, and it's packed with new features and important bug fixes. If you're upgrading from a previous version, please take a moment to review the breaking changes detailed below to ensure a smooth transition.

SuperScraper is an open-source Actor that combines features from various web scraping services, including ScrapingBee, ScrapingAnt, and ScraperAPI.

A key capability is its standby mode, which runs the Actor as a persistent API server. This removes the usual start-up times - a common pain point in many systems - and lets users make direct API calls to interact with the system immediately.

This blog explains how SuperScraper works, highlights its implementation details, and provides code snippets to demonstrate its core functionality.

Crawlee for Python v0.5 is now available! This is our biggest release to date, bringing new ported functionality from the Crawlee for JavaScript, brand-new features that are exclusive to the Python library (for now), a new consolidated package structure, and a bunch of bug fixes and further improvements.

Python developers know the drill: you need reliable company data, and Crunchbase has it. This guide shows you how to build an effective Crunchbase scraper in Python that gets you the data you need.

Crunchbase tracks details that matter: locations, business focus, founders, and investment histories. Manual extraction from such a large dataset isn't practical -automation is essential for transforming this information into an analyzable format.

By the end of this blog, we'll explore three different ways to extract data from Crunchbase using Crawlee for Python. We'll fully implement two of them and discuss the specifics and challenges of the third. This will help us better understand how important it is to properly choose the right data source.

Key steps we'll cover:

1. Project setup
2. Choosing the data source
3. Implementing sitemap-based crawler
4. Analysis of search-based approach and its limitations
5. Implementing the official API crawler
6. Conclusion and repository access

Millions of people use Google Maps daily, leaving behind a goldmine of data just waiting to be analyzed. In this guide, I'll show you how to build a reliable scraper using Crawlee and Python to extract locations, ratings, and reviews from Google Maps, all while handling its dynamic content challenges.

What data will we extract from Google Maps?​

We’ll collect information about hotels in a specific city. You can also customize your search to meet your requirements. For example, you might search for "hotels near me", "5-star hotels in Bombay", or other similar queries.

We’ll extract important data, including the hotel name, rating, review count, price, a link to the hotel page on Google Maps, and all available amenities. Here’s an example of what the extracted data will look like:

{
    "name": "Vividus Hotels, Bangalore",
    "rating": "4.3",
    "reviews": "633",
    "price": "₹3,667",
    "amenities": [
        "Pool available",
        "Free breakfast available",
        "Free Wi-Fi available",
        "Free parking available"
    ],
    "link": "https://www.google.com/maps/place/Vividus+Hotels+,+Bangalore/..."
}

Scraping Google Search delivers essential SERP analysis, SEO optimization, and data collection capabilities. Modern scraping tools make this process faster and more reliable.

In this guide, we'll create a Google Search scraper using Crawlee for Python that can handle result ranking and pagination.

We'll create a scraper that:

• Extracts titles, URLs, and descriptions from search results
• Handles multiple search queries
• Tracks ranking positions
• Processes multiple result pages
• Saves data in a structured format

GraphQL is a query language for getting deeply nested structured data from a website's backend, similar to MongoDB queries.

The request is usually a POST to some general /graphql endpoint with a body like this:

When scraping data from websites using GraphQL, it’s common to inspect the network requests in developer tools to find the exact queries being used. However, on some websites, you might notice that the GraphQL query itself isn’t visible in the request. Instead, you only see a cryptic hash value. This can be confusing and makes it harder to understand how data is being requested from the server.

This is because some websites use a feature called "persisted queries. It's a performance optimization that reduces the amount of data sent with each request by replacing the full query text with a precomputed hash. While this improves website speed and efficiency, it introduces challenges for scraping because the query text isn’t readily available.