Understanding the Pricing of a Web Data Extraction Service
Updated: Feb 5
Data extraction at scale from multiple websites is technically complex and challenging to solve (especially if you are building things from scratch). Self-service tools and Data as a service using self-service tools tend to fail when the project turns complex.
It is not always feasible for companies to set up an in-house team to do data extraction at scale, especially when they are having tight deadlines. There are a few things people get confused about when it comes to web data scraping. Many of them find it difficult to understand the pricing logic of a web scraping project.
This blog educates readers about the factors governing the cost of a web data extraction service.
Major Cost Driving Factors of a Web Data Extraction Service
1. Infrastructure costs
You can write a web scraper script and run it on your terminal. It won’t cost you much. However commercial web scraping platforms don’t work that way.
An ideal commercial web scraping platform needs the ability to deploy and run crawlers, pattern change detectors, Q&A systems, schedules etc. All of these tools need to be integrated properly to get data reliability.
Building and maintaining such a platform requires a lot of resources in terms of human capital and money. Even at small scales, you’ll need to fire up a lot of these systems in parallel. This is a major cost driver for commercial web scraping projects.
2. The volume of data
Handling a huge volume of data is a pain. When it comes to web scraping – Crawling, extracting and parsing data at a considerable scale requires sophisticated infrastructure comprising of good computing power, robustness, agility and scalability.
There are physical costs of running the storage infrastructure to handle the massive volume of data. Usually, companies use cloud storages like Amazon or Azure to do this but that scale – the bill can get messy if you don’t have the proper optimizations.
The bigger and more advanced the data requirement is, the more work needs to be put into the project, increasing the costs.
3. Data warehousing
Websites like Twitter and LinkedIn contain hundreds of millions of records. The vast scale data getting extracted from these, need to be stored first before doing the processing. The data needs to be checked for quality as it is being crawled and reject those does not meet the Q&A guidelines.
The records which do not meet the quality guidelines affect the overall integrity of the data.
Making sure that the data meets quality guidelines while crawling is painful because it needs to be performed in real time. Faulty data can cause severe problems if you are using any ML or AI technologies on top of data.
Therefore, Data warehousing becomes an integral step to provide formatted, structured data. If the Data Warehousing infrastructure is not built correctly, searching, filtering, and exporting data becomes cumbersome and time-consuming. The Data Warehousing infrastructure is scalable, correctly fault-tolerant and secure.
4. Dealing with Complex Anti- Scraping Technologies
Websites implement anti-scraping technologies that make web data extraction service difficult and costly. LinkedIn and Amazon are good examples of employing such technologies.
It takes a considerable investment in time and money to develop a technical solution that can work around cutting-edge anti-scraping technologies at scale.
5. Scraper Maintenance
Every website will change its design now and then, and so should the web scrapers.
Scrapers usually need adjustments every few weeks, as a minor change in the target website affecting the fields you scrape, might either give you incomplete data or crash the scraper, depending on the logic of the scraper. This solidifies why the web scraping project is a service and not a product.
The scraping service companies cannot just create the scraper and sell it to the customers to deploy on their systems. The scraper needs to be looked after, continuously modified to cope up with the changes being made on the target systems. Apart from that, scraper maintenance also involves ensuring that the scraper is not being obstructed or blocked by the anti-scraping systems of target websites. All this requires a continuous investment of time, hardware and man-hours.
6. Demand Frequency and Volume
Depending on your business case – you’ll be needing the data frequently. The frequency of data extraction has a direct effect on the price. Every time you crawl the data – a server is being used for that on Amazon / Azure cloud.
Note: Moreover, how frequently the scraper is running directly impacts the scraper detection probability and being blocked by that source.
The more frequently the data needs to be delivered, the more frequent the crawler needs to be run, directly increasing the web data extraction service project costs.
1. Custom solutions
Large scale data extraction projects come with customs requirements. They need to integrate data flow across multiple systems and software which requires building a custom solution.
Every company has its unique type of data needs, which means there cannot be a readymade web scraping solution that could be utilised for all types of data needs. Data scrapers need to be explicitly programmed to the data format needed and the source it is needed from.
An example would be extracting data from a website and importing it to a tool like Tableau automatically.
Moreover, it’s not only about scrapers. Different companies utilise different data transfer and storage services, which means that even the warehousing and transfer architecture needs to be custom designed. To fulfil the data needs to the best extent, whole data pipelines need to be custom built for the project systems.
Depending on the customization, the pricing can vary.
2. How do you want to manage the project
In a web data extraction service project, the process is not as simple as to fetch the data and deliver it.
In between these steps, there are several important, but looked over, things that need to be taken care of. This means quality checks, normalisation of data, structuring the data, platform configuration to name a few. These steps may seem to be additional and non-obligatory. There are companies that provide you with just a tool but you need to do all the data integrity checks manually.
However, if the extracted data is delivered in the form it is obtained, the company would have no use of it in raw form. Here come DaaS companies which do all the work behind curtains and delivers clean data.
This additional processing over raw data is a factor in the pricing.
3. Data Coverage
This is mostly seen in companies looking to get data from multiple sources to create a unique data set. Here are a couple of examples.
1. Companies in the marketing/lead generation space – If you are trying to build a huge database of people, you can get data from a couple of huge sources like LinkedIn, CrunchBase, Angellist etc to make a big list.
2. Companies in the Hotel/travel space – Companies in the hotel/travel space who want to understand how competitive they are by scraping data from websites. They can either scrape data from an aggregator like Kayak / Trivago or get data from 30+ individual websites. Both methods will produce similar results but the effort and the resources required for extracting data will be extremely different.
The target websites need to be selected with care. There are thousands of websites that contain the data from the same field, for, eg Social Media data or e-commerce data. This calls for exhaustive research to be done beforehand to identify which websites are suited best to get the data according to the company needs. Moreover, a website should not be left out of scraping just because it has a better and more complex anti-scraping system, even though it contains relevant data. Therefore, it becomes critical to ensure that the users don’t miss out on data that could affect their business decisions.
4. What kind of customer support you need
A web data extraction service project is completely customer-centric. For large scale projects, the kind of support you need is an important factor when considering a web data extraction service. It becomes essential to work with the customer to understand the dynamics of the customers’ team and the way they are solving the problem.
A scalable and repeatable process can be built instead of pushing the customer to adapt to an existing process. A company’s needs for data evolves with time. The format or the type of data or the sources may vary with time and this needs to be in open communication between both the parties as any failure in conveying of needs and understandings can cause severe losses on either side.
If there are any issues in the data or changes in deliverables or any changes are observed, both the customers and the service providers team need to be on the same page, making Support a crucial part of the deal. You can choose email support or dedicated support depending on your budget. Dedicated support is always preferred for large scale projects.
Looking for a reliable web data extraction service to meet your business’s data needs? Get in touch with Datahut, your web scraping experts.