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The web is complex and constantly changing. It is one of the reasons why web data extraction can be difficult, especially in the long term.

In case you missed them, here’s the first part and second part of the series.

We are excited to announce our next Zyte Automatic Extraction API: Product Reviews API (Beta). Using this API, you can get access to product reviews in a structured format, without writing site-specific code.

Web scraping projects usually involve data extraction from many websites.

Today we are delighted to launch a beta of our newest data extraction API: Zyte Automatic Extraction Vehicle API.

We’ve just released a new open-source Scrapy middleware which makes it easy to integrate Zyte Automatic Extraction into your existing Scrapy spider.

In the fifth and final post of this solution architecture series, we will share with you how we architect a web scraping solution, all the core components of a well-optimized solution, and the resources required to execute it.

In the fourth post of this solution architecture series, we will share with you our step-by-step process for evaluating the technical feasibility of a web scraping project.

In this second post in our solution architecture series, we will share with you our step-by-step process for data extraction requirement gathering.

Up until now, your deployment process using Scrapy Cloud has probably been something like this: code and test your spiders locally, commit and push your changes to a GitHub repository, and finally deploy them to Scrapy Cloud using shub deploy.