How is Data Extraction Done?
Data extraction is done with the process called ETL, which expands for extract, transform and load. It is the main process that is wisely designed for online analytical processing. There are many big data warehouses where this concept is used.
The process starts with scraping or extraction, which requires a source or sources like LinkedIn data scraping, online reviews or any digital transactions. Once the information is captured from those resources in a warehouse, the process goes to the next level. This level involves analytics and then, business intelligence or strategies.
Let’s get to know what type of extraction is possible.Certainly, it needs the goal assessment, at first, which helps in determining what you are likely to take out from it.
What are the types of data extraction?
There are mainly three ways to get scraping done.
· Update notification
It’s easy to get details from the source system that actually circulates notifications. It is where the changes are recorded, which is excellent in providing support to copy your database. There are many SaaS applications that have a very interesting feature called webhook, which offers the similar kind of thing to do. It means that you can create a duplicate of databases with it. This is how you have all updated information in your warehouse.
· Incremental extraction
There are some sources that don’t notify that any update is there. But, there mechanism allows themto identify which details are changed. They provide an extract of that detail.The ETL process also requires determining of those changes to update data.
But, it has a downside. You may not have the deleted details in the source database because it tracks and makes changes, but does not provide any feature to see the record that is no longer available there.
· Full extraction
When you create a duplicate of any databases from the source, the full extraction is done. Since there is no such feature that can identify only the changes for scraping, so the reloading of the entire database takes place.
Simply put, this is the case when you need to have data with all updates for which the full extraction from scratch is done to make them available. It certainly puts a lot of load on the network because the data are massive in size. This is why it is not the best option to opt in for.
How is data extraction done?
The process is more or less similar for it when you do it for a database or a SaaS platform.It is done in three steps:
· Identify changes and filter them out. It can be in the form of new text, tables or columns in the existing database. Programmer scan do it in a better way, as they know how to program codes for tracking and filtering changes in the structure programmatically.
· Find the information that needs to be extracted from the content. The integrated lookup and replication scheme is to be specified before, which involves making multiple copies of the target information and saving them at different locations for easy accessibility.
· Finally, you can get the information that you need through extracting data.
This is how the captured data is loaded into a destination storage, which is meant for BI. You have to specify the location for it, as can happen in Amazon Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, or Google BigQuery.
Role of API
Almost every automated system and stuff needs APIs to scrape records from a particular location. It is the official way of doing so because automated applications use it excellently. Even, you can get benefits from APIs to develop your own applications. If it allows, you don’t need any web scraping then.
The Application Programming Interface (API) is an intermediary that lets two applications to communicate or talk with each other.It allows setting up a connection and placing a request to get the details that are needed.
Challenges in Extraction via API
It’s easier said than done. There are many challenges in contacting APIs and then, pull out any information. Most of the databases that use SQL need it to do so, which may have these challenges:
· Access of Robots.txt denied: Some APIs reject robots.txt access. In that case, you need to personally raise a request to the web owner for granting permission of scraping.
· Different APIs: Some APIs are different for every application, which creates complications.
· Unorganised APIs: Many application owners don’t have it well-documented.
· IP blocking: The IP blocking may also disallow communicating with it.
· Changing APIs:Some dynamic APIs change over time, such as of FB. So, you need to re-raise a request for it.
· CAPTCHA: CAPTCHA is often used to differentiate between a human and a bot. It is used for avoiding non-stop scraping.
· Honeypot trap: It is also a big hurdle that traps frequent scrapers. Once it taps their information, the website can block them.
· Slow loading:Slow loading website may also result in failed communication.
· Signing in: There are some websites that keep the information protected. They require signing in, at first. Once you do so, your browser attaches the cookie.It helps in identifying who has made the request and how many times.
If you have just a few web sources, codifying to extract and copy data is the most effective thing for web scraping.
What if you have a lot of sources?
You need to change your approach then because there would be many APIs that you need to change accordingly. Format of the content may also be dynamic. Scripting may have errors that you may not identify easily. These all reasons may end up in extracting a trash.
In this case, the Cloud-based ETL is a relief. It connects many sources and destinations in no time. You need not write a script to run.Nor do you need to maintain it. So, it is way easier and hassle free task to extract data from many resources for business intelligence.