Most businesses can achieve their goals by looking at long-term trends and performance reports, however, there are those, for whom real-time data is of paramount importance.
Generally, real-time information is required for real-time analytics in various spheres to produce up-to-date insights into the situation without any delay. Gathering data and analyzing it in the actual moment businesses have more choices available and can facilitate immediate decision-making.
Certain financial institutions need it for credit scoring and consequent decisions whether to extend or discontinue credits and at what conditions. Financial departments often need real-time data for economic indicators analysis or to make a comparison of a budget vs. actual costs.
Real-time analytics at the Points of Sale helps to detect frauds of various kinds.
Customer relationship management backed by real-time analysis may be a perfect example of customer satisfaction optimization and business results enhancement.
Real-time data extracting and analytics is often the key domain in sentiment analysis, belief mining, criminal information investigation, cyber patrolling, market research, and many more.
To discover some knowledge about some person or object from the web, specific online portals, or social media, businesses extensively use real-time data scraping. The results can be further on used for predictive analytics to work out patterns and forecast future trends or outcomes. Though you may not predict the future with 100% accuracy, you will definitely spot the probabilities to consider.
In general, real-time data scraping is the process through which software scrapes data from websites at almost the same time as changes occur there. This process requires a delicate approach. To get data almost at once, your software needs to request the web sources many times. So your real-time crawler could create an additional load on the web source host and can even crash the website. That’s why it’s essential to find the right balance between the delay of getting fresh data and overloading the website servers.
Another approach is real-time web scraping using API – website application programming interface (API)—a special channel made for downloading data directly from websites’ databases. But APIs exist on less than 1% of all websites (mostly on big, well-known web sources like Facebook, Twitter, and others). And another issue is that APIs have a lot of limits regarding data they can give, for instance: the number of records, amount of fields, or speed limitation.
Real-time web data extraction has one more meaning. We have developed a lot of scaping-based solutions for our clients where the end-user requests information and should get it as soon as possible. The speed of getting information is the most valuable aspect of such products.
With a real-time web scraper, E-commerce sites can compare prices up to the moment, sometimes lowering the price by as little as $1, which can sensitively boost sales and result in tremendous profit increase. However, if your company is small, you may fail to understand where to start, where to extract data, and what to do with it.
In such a case you can start from product offer listings, your competitors’ product pages, question and answers sections, then proceed to customer reviews or search engines search results. With a callback data delivery method, the web scraper will notify you that the results are ready, with real-time data delivery, it is retrieved on the same connection. It means that a user submits the request and gets the information back on the same open HTTPS connection.
Imagine you decided to build a product that monitors all airlines and allows your customers to buy flight tickets at the lowest price. The most important thing for you will be the delay between your client asking for tickets for a particular destination and the time when the system provides information. This time is critical because if it is too much, tickets might be bought by other tourists.
Above, we described a real case of one of our clients (read more about that). To create this almost real-time scraping and web monitoring system, we needed about six months. There are a lot of technical pitfalls in developing such a system.
First of all, when you need data quickly, your web scraper may send too many access requests to the target sites, which will result in a slow response from it or even failure. The scraping software you use does not know how to handle such emergencies and needs human interference until the target source recovers.
That’s one of the reasons why such web data software should be maintained almost 24/7. Besides, airlines change their web pages and HTML code quite often. So, we always need to check data quality, otherwise, your customers can be angered due to your service downtime.
Besides, the question of data accuracy and consistency arises in the context of data quality guidelines. You should be extremely careful when scraping information in real-time, since the changes may occur in a blink of an eye and influence overall data integrity, which will entail serious problems if Machine Learning algorithms or AI technologies are used for further processing.
Another difficulty is in the volume of data. As your startup grows, the amount of clients also grows, and the more data you need to scrape in real-time.
To summarize, all kinds of real-time web scraping require complex solutions with continuous maintenance. It can’t be implemented as a data delivery project. It will always require custom software solutions aimed at achieving your business’s goals.
If you need consulting regarding your real-time scraping project, schedule a free expert consultation.
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