There is no such thing as too much data. Well, until you try to gather and process it, of course. Businesses heavily rely on information as it helps them navigate the market, make better decisions, and reach goals. But to acquire the required amount of data and make it understandable and easy to work with is quite a challenge.
That’s why data parsing exists.
What is data parsing?
Data parsing is a process of gathering the needed information from the internet, processing and converting it in a format that will be convenient to work with. It’s a rather complex job that’s hardly feasible for us, humans. But today we have computers. And robots are great at doing boring and monotonous work and, consequently, at working with data.
That’s why there are numerous tools that will gather the information and process it for you. Unfortunately, no matter how much developers work on simplifying the instruments for data parsing, it’s still very hard for an inexperienced person to work with them efficiently. If you’ve never gathered information, you should hire specialists for this job.
What data parsing tools do you need?
You will need several tools to gather and process the information. The first one is, of course, a web scraper — a program that goes through websites and acquires the data you told it to look for. It’s a bot that sends requests to a destination server asking it for specific bits of information.
A scraper basically does the same job you’d do if you were to gather the data manually — enters the website, goes through its content, and picks out the required information. The difference is that a bot can do that much faster than us, and with much better precision.
? Further reading: How to protect your brand with web scraping and proxies
Another two things you will need are parsing libraries and proxies. Let’s talk about them in detail.
What is a data parsing library?
A data parsing library is a set of commands that tells the scraper what to do — how to get the required data and transform it into a convenient, human-readable format. A specialist can write those commands from scratch, but it’s much quicker to utilize a ready-to-use library, especially considering that there are quite a lot of them.
However, some projects with unique goals might require a library written from scratch. Sometimes, it’s easier and faster to create your own parsing library than trying to fit in existing ones so that they could satisfy the needs of the project.
Also, specialists use libraries that contain headers and other miscellaneous data that will help them make requests appear realistic. Such libraries along with proxies will help the scraper to gather the information without any delays.
How can proxies help you?
As we’ve already mentioned, a scraper sends requests to a destination server to acquire the data. But the issue is that most webmasters protect their sites from scraping for numerous reasons — usually, to protect their content from competitors. A default request sent by a scraper looks nothing like a request a real user would send. It lacks such information as user agent and cookies — that can be fixed with libraries. But the biggest problem is that a scraper sends all the requests from the same IP address. It’s the primary reason why a destination server gets suspicious and blocks the bot.
Proxies are servers you can connect to and change your IP address. It happens because you reroute traffic through a proxy server and, thus, pick up its data masking with it your authentic IP. Therefore, you can make the requests your scraper is sending appear realistic.
Residential proxies fit the needs of data parsing perfectly. A residential proxy is a device with a real IP address issued by an ISP. Traffic rerouted through such a gadget looks like it’s sent by a resident of a country where the device is located. That’s why it’s virtually impossible to detect scraping activity if you use residential proxies.
You need to pay attention to one detail choosing proxies — the size of the pool of IPs. Depending on the size of your project, you will need a certain number of proxies. Infatica offers flexible pricing plans that fit different requirements. Also, we can create a personalized plan for you if your project needs a larger pool. And if you’re not sure how many IPs you’ll need, simply contact us, and we will help you to decide.
How much does the parsing of data cost?
It’s hard to even begin estimating the costs. The budget for your parsing project depends on the complexity of the desired data and source websites, and the amount of information you need. These requirements will determine whether you need a powerful and feature-rich scraper or you can feel fine using one of the simple free ones.
Parsing libraries also can be free and paid — it depends on your needs which one to pick. And if you require a unique library build specifically for your project, it will be another significant addition to the costs.
Residential proxies are quite affordable, and we highly recommend you to stick to paid ones. You can find free proxies on the internet, but their quality is usually very poor. They will only slow down the data parsing for you since most of the free IPs are already blocked by many websites.
❓ Further reading: Paid vs. free proxies: What’s the real price to pay?
Finally, the total cost of data parsing relies on the pricing of the data specialist that will work on your project. The amount of money a professional will ask for their job depends on their experience and the complexity of the task.
Data parsing use cases
Businesses can gather information for a variety of needs beginning with basic business intelligence and up to some unique data. Here are the most popular uses for scraping.
If you know what your rivals are up to, you have a chance to build a better strategy to win the competition. With data parsing it’s easy to quickly gather the information about the content other firms on your market create, their ads, special offers, prices, and so on. All this data can offer useful insights and help make thoughtful decisions.
? Further reading: Using proxy servers for better social media marketing
If you want to build a foolproof marketing strategy, you need a lot of information that will help you understand what will be the right thing to do. This can be details about the interests and activity of your target audience — with data parsing it’s quite effortless to study everything about your potential customers. Also, it’s useful to learn about the marketing activity of your competitors as you can get ideas during your study. Moreover, you can study trends on your market to make sure your marketing actions are effective, and to predict possible tendencies.
Such services rely solely on the information they gather from e-commerce websites. It’s a hassle to acquire prices manually, but with data parsing, it’s a piece of cake. Automating data gathering and processing, price aggregators will be able to always offer their visitors only true information.
? Further reading: Client profiles: Price aggregators
Specific business needs
Each company has its own needs for information. For example, law firms can use data parsing to look for similar cases to gain useful insights on how to help their clients. Travel agencies can accelerate their processes by automating the gathering of such data as hotel and flight prices, new tourist routes, restaurants, attractions, and so on. Investors can utilize scraping to look for new opportunities and study candidates for investments. Virtually any firm can come up with its own unique use for data parsing.
If the data a specialist needs for their academic research is online, a web scraper will fetch it very quickly. Thus, research becomes much easier and can be completed faster. Data parsing will be also useful for working with offline digital databases if the required information is located there.
⚡ Further reading: 4 skills that will make you a better data scientist
Data parsing is a multipurpose tool, and everyone can find their own use for it. As the amount of information we generate every day grows, it becomes harder to find specific bits we need. With parsing, big data becomes lesser of a challenge.