How To Make Your Data Scraping Run Faster
In this article we will discuss:
- Scraping and parsing typically requires major in-house infrastructure
- Web Scraper IDE automates data scraping, and parsing with zero infrastructure
- Ready-to-use datasets eliminates the need to perform data collection independently
Scraping and parsing typically requires major in-house infrastructure
Scraping, and parsing is a very manual, and tedious process. One may choose to accomplish these tasks using a bot or a web crawler. For those of you who are not totally familiar with how this works, web scraping is a method of performing data collection in which data is copied from the web into a database or spreadsheet for analysis at a later point in time.
Parsing is put into action once the data has already been retrieved. It helps structure large datasets in a way that people can understand, process, and use information in a constructive way. Typically this is accomplished when HTML files are converted into decipherable text, numerical values, and other usable pieces of information.
The biggest issue is that websites keep on changing their structure, by the same token, datasets are constantly changing as well. So when scraping and parsing manually one really needs to be able to keep track of these informational changes as well as ensuring that it is accessible, that being the most difficult part of the data collection process. In order to accomplish this, you need many developers, IT personnel, and servers which some companies do not want to handle.
Web Scraper IDE automates data scraping, and parsing with zero infrastructure
Web Scraper IDE entirely automates the scraping and parsing for you in real-time. This means that you don’t need to build or maintain complex systems in-house. It is an excellent option if you want to outsource your data collection operations when dealing with new target sites (e.g. an eCommerce-focused company that has been collecting data from Marketplace A, and now wants to start collecting data sets from Marketplace B).
The key advantages of using this tool vs doing manual scraping, and parsing include:
- Gaining access to data that is cleaned, matched, synthesized, processed, and structured before delivery, so that you can start using it straight away
- Saving both time, and resources on manual jobs as all data collection is accomplished using our AI, and ML-driven algorithms
- Being able to scale your data collection operations up or down depending on your budget, and constantly changing projects, and objectives
- Leveraging technology that automatically adapts to target site structure changes and blockages
- You are able to gain access to continuously fresh, and up-to-date data points
Ready-to-use datasets eliminates the need to perform data collection independently
If your scraping one popular website such as a:
- Social media network
- Travel/hospitality/car rental platform
- Business/information services directory
Then pre-collected ‘Datasets’ is the way to go. The main advantages of this include:
- Results are retrieved almost immediately (within minutes)
- It is a far more cost-effective option
- It requires zero technical know-how, no DevOps team on staff, nor any data collection infrastructure
Additionally, this solution gives you options that you can play with. For example:
- Option 1: Customize the dataset you need based on parameters that are important to you (e.g. a sub dataset pertaining to football influencers in Spain, for example)
- Option 2: You can completely customize a dataset based on your unique use case, and business strategy (e.g. all volume of a certain cryptocurrency on a specific e-wallet)
The bottom line
Bright Data provides you with a variety of options that are tailored to your current needs. Datasets gives you quick, cost-efficient access while Web Scraper IDE completely automates complex data collection jobs, delivering information directly to team members, systems, and algorithms for your convenience.