2019 was an exciting year for Scrapinghub. We created things we have never created before and did things nobody in our industry had ever done before. Let’s revisit what happened in 2019!
Whether you are managing a hedge fund trying to find innovative sources of alpha or are an analyst looking to future proof your company’s financial investments, as big data continues to disrupt the investment research landscape, getting on top of these alternative datasets as early as possible is the key to capturing the immense alpha left in this data.
More and more businesses leverage the power of web scraping. Extracting data from the web is becoming popular. But it doesn't mean that the technical challenges are gone. Building a sustainable web scraping infrastructure takes expertise and experience. Here, at Scrapinghub we scrape 9 billion pages per month. In this article, we are going to summarize what the essential elements of web...
Scaling up your web scraping project is not an easy task. Adding proxies is one of the first actions you will need to take. You will need to manage a healthy proxy pool to avoid bans. There are a lot of proxy services/providers, each having a whole host of different types of proxies. In this blog post, you are going to learn how backconnect proxies work and when you should use them.
Data moves around the marketplace. It can be sourced internally or externally and collected from vendors, manufacturers, retailers, wholesalers, consumers, and other players in the marketplace. This data is then processed and used by businesses in making insights and decisions regarding new business ventures, product ideas, conflict resolution, and process improvement.
Price scraping is something that you need to do if you want to extract pricing data from websites. It might look easy and just a minor technical detail that needs to be handled but in reality, if you don’t know the best way to get those price values from the HTMLs, it can be a headache over time.
In today’s article we will extract real estate listings from one of the biggest real estate sites and then analyze the data. Similar to our previous web data analysis blogpost, I will show you a simple way to extract web data with python and then perform descriptive analysis on the dataset.