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5 Popular Ways of Data Scraping in Data Analytics

Data scraping refers to the process of acquiring data from websites and online sources for data analytics and data science purposes. It can be done by hand or with automated software, depending on the level of automation desired by the user and the amount of information being scraped. Data scraping can provide valuable insights into both your business’s performance and that of your competitors, and it can make it easier to update your products if you scrape competitor information as well as information on your customers and their preferences.

1) Web Crawlers

Web crawlers, or spiders, are computer programs that search through websites. They help you find information that isn’t available on a website’s homepage. An example would be searching for local restaurants to include on a food delivery service like Seamless or Grubhub. There are lots of web crawlers out there and each has its own particular way of grabbing content from a website.
Luckily, you don’t need to program your own web crawler from scratch. There are several companies that have done most of the heavy lifting for you. For example, Yandex is a major Russian search engine that also has their own web crawler . It can be used to grab information from sites in multiple languages and supports various data extraction formats like JSON or XML.

2) Screen Scrapers

Web-Based Screen Scrapers: Web scraping is a popular way to extract data from any web pages. There are many web-based screen scrapers which you can use to scrape through the web pages quickly and easily without needing too much coding knowledge. These screen scrapers are very useful for data extraction purpose. DataScraper is one such screen scraper for extracting data from different websites like Facebook, YouTube, Twitter etc.

These screen scrapers can be programmed to extract data from different websites and save them as different files or dump them into a database. These web-based screen scrapers are extremely easy to use and do not require any knowledge of programming languages like Python, R, etc. And you do not need any server configuration for these either. You can use them for data extraction purpose by just copying paste commands provided by these screen scrapers on your terminal/command prompt.

3) Databases

The most traditional way to get data is by connecting to a database. Data aggregation tools like SQL, Hive, Pig, etc., make it easy to pull out data sets and combine them into a single table that can be analyzed as one. If you’re taking data from a relational database (like MySQL), then there are libraries for just about every language that make connecting with your database and retrieving its information trivial. Just make sure you have permission before accessing someone else’s databases!
If you’re working with non-relational databases (like MongoDB, which are becoming increasingly popular), then things get a little more complicated. While there are still libraries for accessing these databases, they are less polished and don’t provide full access to all of a database’s data. This can make it trickier to get what you need without getting permission from your users first. Additionally, some scraping tools such as NodeXL only support relational data sets. If your data doesn’t fit into that format, then you might need to consider something else.

4) APIs

The quickest way to collect data from a variety of sources is to use one or more APIs, which stands for Application Programming Interface. The term may be confusing because it doesn’t have anything to do with learning to code. It has everything to do with gathering data. In other words, using an API allows you to connect one application (such as Google Drive) with another (such as your own spreadsheet). This allows you access all types of data that may be kept in different locations.
Another useful tool for data gathering is web scraping. Web scraping is accomplished by getting access to data from a website and storing it in a local database or spreadsheet. Because web scraping tools allow you to write scripts that can extract specific pieces of information, they are often used to gather and analyze data for certain types of businesses.

5) Ecommerce Sites

If you’re looking to scrape data off an ecommerce site, then it’s likely that you want to get product information like prices and descriptions. In those cases, there are two popular methods: web scraping and API integration. Both work just fine, but they each have their own strengths and weaknesses. Web scraping is faster but requires more coding; APIs are typically simpler to integrate with but slower. So if speed is your biggest concern, you might lean toward using a web scraper for now; if long-term access is most important, you can use an API instead. Ecommerce websites tend to list their public APIs on their website or with other third-party tools (like ProgrammableWeb).
If you’re scraping an ecommerce site, it may make sense to ask if they offer a public API. Many big ecommerce sites like Amazon and eBay do offer such APIs, but not all do. However, there are many third-party APIs you can use to access some information from many ecommerce sites without any need for web scraping. For example, BigML offers an open-source API that lets you access data on over 1 million products across dozens of popular online retailers such as Macy’s and Best Buy. There are plenty more out there too, so if you don’t see your favorite retailer included yet it may be worth doing a quick Google search to see if one is available.


As you can see, data analytics is a growing market with plenty of room for exploration. If you’re looking to enter into data analytics as a career, these are 5 essential tools for building your skillset and moving your career forward. And if you’re an individual looking to use these tools for private projects, give one or all a try and learn more about what can be done with these powerful tools.

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