Web scraping involves “scraping” the Internet and gathering (and eventually using) the data presented on web pages.
Web scraping allows companies to take unstructured data on the world wide web and turn it into structured data so that it can be consumed by their own applications, providing significant business value.
The world wide web (www) was initially built for humans to consume information created by other humans through connections between each page using links or URLs. This worked great when there were less than a handful “web pages” on the Internet. Creation of content and data was easy and consumption of this information, content or data was also just as easy.
Once the WWW became popular, the content or data or information available on the Internet also proliferated.
People and now machines took over and generate a lot of information every second – think of all the tweets, the Facebook status updates, the selfies and pictures uploaded from millions of camera phones, the cat (or other) videos, the live streams, IoT devices filling up the pipes with their data. The amount of data available today is simply mind-boggling.
Humans can’t consume all this information unless we garner the support of our trusted devices – computers.
Most of this information on the Internet/WWW is unstructured and not fit for machine consumption.
This is where Web Scraping comes in.
Web scraping (or Data Scraping or Data Extraction or Web Data Extraction used synonymously), help turn all this content on the Internet into structured data that can be consumed by other computers and applications thereby creating many innovative, unique, fun, useful, uses, apps and businesses, further fueling the meteoric rise of the the Internet and its indispensability in our everyday lives.
For web scraping to be useful in consuming a significant amount of data, it needs to be automated. Surely, you can copy and paste a web page into an Excel spreadsheet and spend hours formatting it – but that cannot be considered web scraping due to the limited value it provides.
Web scraping uses a few core components/modules/steps to make it useful. Here are the main ones:
This process starts at the source of the data (a website or webpage) and “crawls” the websites or sites for other links that may match particular criteria. This is the process similar to what humans use when they “browse” the Internet – they start at one website and click their way to other pages or sites based on what catches their eye or serves their purpose.
Scraping is the actual process of gathering the data or information on the pages visited by the crawler. In the scenario where a human would be performing the scraping, it would be akin to selecting the information and copying it to the clipboard.
Extracting is the process of taking the scraped data and extracting meaningful data elements out of that mountain of data. The extractor could be extracting names, phone numbers, prices, job descriptions, image information, video details, etc.
The data that has been extracted needs to be presented back to another consumer (computer application) in a format that can be understood by that consumer. Some of the common formats of data are JSON, CSV, XML etc. These formats are structured and each format has its pros and cons which can be read about here.
Finally, after all the data has been scraped, extracted and formatted, it needs to be exported or provided to the consumer. The method of this data delivery can be an API or an export into file storage such as DropBox, Amazon S3, etc. The choice of the method is largely dependent upon the size of the data and the preference of both parties in this exchange.
The act of creating a process for automated data extraction using web scraping isn’t technically complex, but it has various roadblocks which can be read about here and here and in various other articles on our website.
Many companies build their own web scraping departments (just like they run their own IT groups and infrastructure) and lately the trends have been towards using Web Scraping Services (just like the trends to use Outsourced IT, BPO, Infrastructure services – SaaS, PaaS, DaaS, etc).
The benefits are quite significant and the arguments are similar to why companies outsource any part of their operation or use AWS or outsource call centers etc. Companies are advised to focus on their “core competencies” by any management guru or consultant. Web scraping is NOT a core competency for anyone but the companies such as Parse that specialize in it.
Companies that provide such service spend a lot of time doing the same thing over and over and (hopefully) are good at it. Parse has the processes and the technology scalability to handle web scraping tasks that are complex and massive in scale – think millions of pages an hour scale.
The biggest objections companies have in using a Web Scraping or Data Extraction Service are usually around price and control (rather the lack of control). However, when you parse through each objection and analyze them without emotion and using data, web scraping service companies such as Parse provide significant benefits at similar costs and control without all the hassles associated with running your own web scraping operation.
Add the privacy benefits on top of that and most companies have a very compelling argument for at least trying Web scraping services.
Parse has NO long term contracts or annual commitments, so when you add standard formats and communication protocols that can be easily swapped in and out, giving Parse a try for a month has no risk at all.
If it doesn’t work out, your applications can rely on the same standard formats (JSON, CSV) and protocols (DropBox, S3, etc) and use your own service or some other service very easily.
Web scraping at an Enterprise scale requires technologies, skills, and experience that can work at that level.
Whether that is the sheer number of websites that need to be tackled and the manpower required to set them up, or whether it is the volume of pages that need to be scraped or the speed at which they need to be scraped.
Enterprise scale scraping has a unique set of challenges which we have addressed over the years working with some of the biggest global companies to harvest web data at an enterprise scale.
If your planned needs are huge and you are just starting to address them, or whether your current provider cannot handle the enterprise level scalability and quality, it is time to get in touch with us.
We have the experience to handle massive scales while being very cost-effective at the same time – something that cannot be replicated easily within an organization.
We also the industry-specific experience in a variety of industries such as Finance, Retail, Industrial and Manufacturing, Technology, Social Media, Entertainment and Media, Travel and Hospitality, etc which helps us to get started with minimal industry level context.