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DataForSEO VS Scrapy

Compare DataForSEO VS Scrapy and see what are their differences

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DataForSEO logo DataForSEO

DataForSEO offers API data for SEO companies that deliver results of tasks for Rank tracking, SERP, Keyword data and On-page APIs.

Scrapy logo Scrapy

Scrapy | A Fast and Powerful Scraping and Web Crawling Framework
  • DataForSEO Landing page
    Landing page //
    2021-10-31
  • Scrapy Landing page
    Landing page //
    2021-10-11

DataForSEO features and specs

  • Comprehensive API Suite
    DataForSEO offers a wide array of APIs including SERP, keyword data, on-page SEO, and backlinks, allowing for extensive data gathering and analysis across various SEO components.
  • Customization
    Users can tailor data requests based on specific needs such as location, device type, language, and more, providing relevant and targeted SEO data.
  • Scalability
    DataForSEO's scalable infrastructure supports both small businesses and large enterprises, making it suitable for varying levels of data demands.
  • Real-Time Data
    Provides real-time or near real-time data, which is crucial for making timely decisions in fast-paced SEO environments.
  • Cost-Effective
    Pay-as-you-go pricing model helps businesses manage costs effectively, ensuring they only pay for the data they need and use.

Possible disadvantages of DataForSEO

  • Complexity
    The vast array of options and parameters for API requests can be overwhelming for beginners or those with limited technical expertise.
  • Rate Limits
    API rate limits may constrain the amount of data that can be pulled in a given time frame, particularly for large-scale data collection projects.
  • Costs for High Volume
    While cost-effective for moderate use, high-volume data demands can escalate costs, potentially making it expensive for large-scale operations.
  • Reliance on API
    Users' dependence on the API for data means that any downtime or issues on DataForSEO's end could impact business operations.
  • Learning Curve
    Users may need to invest time in understanding the API documentation and integrating it into their existing systems, which can slow down initial implementation.

Scrapy features and specs

  • Efficiency
    Scrapy is designed to be efficient and robust, capable of handling multiple tasks simultaneously and scraping large websites in a fast and reliable manner.
  • Built-in Tooling
    Scrapy comes with built-in tools for handling common tasks such as following links, extracting data using XPath and CSS, and exporting data in a variety of formats.
  • Customization
    Scrapy offers extensive customization options, allowing users to build complex spiders and modify their behavior through middleware and pipelines.
  • Python Integration
    Being a Python framework, Scrapy integrates seamlessly with the Python ecosystem, enabling the use of libraries like Pandas, NumPy, and others to process and analyze scraped data.
  • Community Support
    Scrapy has a large and active community, providing extensive documentation, tutorials, and third-party extensions to enhance functionality.
  • Asynchronous Processing
    Scrapy’s asynchronous processing model enhances performance by allowing multiple concurrent requests, reducing the time required for crawling sites.

Possible disadvantages of Scrapy

  • Steep Learning Curve
    For beginners, Scrapy's comprehensive feature set and the need for understanding concepts like XPath and CSS selectors can be challenging.
  • Resource Intensive
    Scrapy can be resource-intensive, potentially consuming significant memory and CPU, which can be problematic for scraping very large websites or running multiple spiders simultaneously.
  • Debugging Complexity
    Debugging Scrapy projects can be complex due to its asynchronous nature and the multiple layers of middleware and pipelines that need to be understood.
  • Overhead for Small Projects
    For simple or small-scale scraping tasks, the overhead of setting up and configuring a Scrapy project might be excessive, with simpler alternatives being more suitable.
  • Limited JavaScript Support
    Scrapy's out-of-the-box support for JavaScript-heavy websites is limited, requiring additional tools like Splash or Selenium, which can complicate the setup.
  • Dependency Management
    Managing Scrapy's dependencies and compatibility with other Python packages can sometimes be challenging, leading to potential conflicts and maintenance overhead.

DataForSEO videos

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Scrapy videos

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Python Scrapy Tutorial - 22 - Web Scraping Amazon

More videos:

  • Demo - Scrapy - Overview and Demo (web crawling and scraping)
  • Review - GFuel LemoNADE Taste Test & Review! | Scrapy

Category Popularity

0-100% (relative to DataForSEO and Scrapy)

DataForSEO

Scrapy

Analytics
100 100%
Analytics
0% 0
Web Scraping
0 0%
Web Scraping
100% 100
Business & Commerce
100 100%
Business & Commerce
0% 0
Data Extraction
0 0%
Data Extraction
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare DataForSEO and Scrapy

DataForSEO Reviews

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Scrapy Reviews

Top 15 Best TinyTask Alternatives in 2022
The software is simply deployable via the cloud, or you can host the spiders on your server using Scrapy. Only the rules need to be written; Scrapy will take care of the rest to separate the facts. With Scrapy’s portability and ability to run on Windows, Linux, Mac, and BSD platforms, new features can be added without affecting the program’s core.
Source: www.dashtech.org

Social recommendations and mentions

Based on our record, Scrapy seems to be a lot more popular than DataForSEO. While we know about 97 links to Scrapy, we've tracked only 9 mentions of DataForSEO. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

DataForSEO mentions (9)

  • Launch HN: Marblism (YC W24) – Generate full-stack web apps from a prompt
    I asked it to integrate with https://dataforseo.com/ api which is 10x cheaper than the ahrefs or semrush apis (it's real data). - Source: Hacker News / 8 months ago
  • Understand your competitors on Google with Python?
    So far I used dataforseo.com to get the data I need (they have a large database with 4.8 billion keywords), and I could create some cool tools with it! I share the first version of the tutorial on my website called amigocci.io but I started to make it only 2 months ago so I'm still figuring it out and trying to find the best way to make analysis with it. Source: almost 2 years ago
  • How Do I Track Keyword Rankings for Free?
    I can't think of any free tools, but there are some APIs out there that are pretty cheap like https://dataforseo.com/ or https://serpapi.com/pricing. Source: about 2 years ago
  • Semrush - Where does it get it's data?
    Dataforseo.com you can get the same data as SEMrush and other similar tools. Source: over 2 years ago
  • Tool just discovered
    I like to think that I'm typically aware of great tools to aid in SEO but I was just informed of one that's a game changer. https://dataforseo.com/. Source: over 2 years ago
View more

Scrapy mentions (97)

  • Current problems and mistakes of web scraping in Python and tricks to solve them!
    One might ask, what about Scrapy? I'll be honest: I don't really keep up with their updates. But I haven't heard about Zyte doing anything to bypass TLS fingerprinting. So out of the box Scrapy will also be blocked, but nothing is stopping you from using curl_cffi in your Scrapy Spider. - Source: dev.to / 9 months ago
  • Automate Spider Creation in Scrapy with Jinja2 and JSON
    Install scrapy (Offical website) either using pip or conda (Follow for detailed instructions):. - Source: dev.to / 9 months ago
  • Analyzing Svenskalag Data using DBT and DuckDB
    Using Scrapy I fetched the data needed (activities and attendance). Scrapy handled authentication using a form request in a very simple way:. - Source: dev.to / 11 months ago
  • Scrapy Vs. Crawlee
    Scrapy is an open-source Python-based web scraping framework that extracts data from websites. With Scrapy, you create spiders, which are autonomous scripts to download and process web content. The limitation of Scrapy is that it does not work very well with JavaScript rendered websites, as it was designed for static HTML pages. We will do a comparison later in the article about this. - Source: dev.to / 12 months ago
  • What is SERP? Meaning, Use Cases and Approaches
    While there is no specific library for SERP, there are some web scraping libraries that can do the Google Search Page Ranking. One of them which is quite famous is Scrapy - It is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It offers rich developer community support and has been used by more than 50+ projects. - Source: dev.to / over 1 year ago
View more

What are some alternatives?

When comparing DataForSEO and Scrapy, you can also consider the following products

DemandSphere - DemandSphere is an advanced-level software that helps marketers to create the best digital appearances of their brands.

Apify - Apify is a web scraping and automation platform that can turn any website into an API.

Searchmetrics Suite - SEO Software

ParseHub - ParseHub is a free web scraping tool. With our advanced web scraper, extracting data is as easy as clicking the data you need.

Direction Local - Quickly Boost Your Local Search Rankings

Octoparse - Octoparse provides easy web scraping for anyone. Our advanced web crawler, allows users to turn web pages into structured spreadsheets within clicks.

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