Data crawling has become one of the most resourceful tools for companies operating in the online space. Be it staying updated with the prices of products on an Ecommerce website, checking the freshness of a set of URLs, indexing data or building an internal search engine, data crawling is all you need to take care of a variety of use cases that involve navigating through web pages on the internet. Given the scale at which businesses need crawling done, it is not feasible to employ humans to do this tedious task of browsing through web pages. This is where automated data crawling services become invaluable.
Data crawling is done by using a custom built crawler setup for the particular requirement. A crawler is a program written exclusively to navigate through a list of web pages and do predefined actions such as checking the freshness of the pages or scraping data from these pages. Below are the basic steps of a typical data crawling process:
In this step, the list of web pages to be crawled for data extraction is specified. Since the quality of the whole process will depend on the source websites, this has to be done with utmost care. Not to mention, only reliable websites should be included in the source list. Sources should be websites that doesn’t disallow automated crawling in their robots.txt file or the TOS page. Crawling websites that disallow bots can lead to legal complications later on.
Setting up the crawler is the most complicated step in the data crawling process. This requires technically skilled personnel to look into the source code of the web pages and identify the data points to be extracted and write code that can navigate through the pages automatically to carry out these tasks. Especially because websites do not follow a uniform structure that a generic program can handle. However, advanced crawlers have inbuilt AI techniques that help scrape data intelligently without having to manually look into each source.
This is a very important step in data crawling since the initially scraped data would contain noise and duplicate entries. This data cannot be run through an analytics setup or used for any other purpose right away. It has to to be cleansed and de-duplicated using a custom system. Noise comprises of the unwanted elements and parts of code that got scraped along with the required data. Cleaning this up will leave us with just the required data.
Machines are not good at handling unstructured data. This is why structuring the data becomes the final step in data crawling. By giving the data a proper structure also knows as schema, it becomes machine-readable and hence can be analysed using a data analytics system or be imported into your database with ease.
Being a technically challenging niche process, it might not be a good idea for enterprises to jump into the big data bandwagon using an in-house data crawling setup. Since crawling is a resource hungry activity, it could easily hog your productivity. It is better to go with a hosted data crawling service like PromptCloud since you get your data crawling needs covered without having to be involved with any of the technology-intensive tasks in crawling.