As the industrial world continues to bask in the glory of Data Science and Big Data, the importance of data is only strengthening and solidifying in the real world. Today, practically every major industry leverages data to gain meaningful industry insights and promote data-driven decision making for businesses. Applications of data science are increasing every day.
In such a scenario, Data Extraction becomes all the more important. The first step to leveraging data begins with data extraction from multiple and disparate sources and then comes the processing and analyzing part.
In this post, we will focus on Data Extraction and talk about some of the best Data Extraction tools available out there!
Table of Contents
What is Data Extraction?
Data Extraction is the technique of retrieving and extracting data from various sources for data processing and analyzing purposes. The extracted data may be structured or unstructured data. The extracted data is migrated and stored into a data warehouse from which it is further analyzed and interpreted for business cases.
To make the extraction process more manageable and efficient, Data Engineers make use of Data Extraction tools. When chosen carefully, Data Extraction tools can help companies reap optimal benefits from data. Don’t get confused data extraction tools with data science tools. To get more idea about data extraction, check out our data science online certifications from top universities.
Without further ado, let’s check out some of the most widely used Data Extraction tools!
Top Data Extraction Tools of 2022
Import.io is a web-based tool that is used for extracting data from websites. The best part about this tool is that you do not need to write any code for retrieving data – Import.io does that by itself. This tool is best suited for equity research, e-commerce and retail, sales and marketing intelligence, and risk management.
The biggest USP of Import.io is helping companies achieve success using “smart data” along with data visualization and reporting features. To use this Data Extraction tool, you don’t require any special skills or expertise. It is very user-friendly and hence, accessible to users of all skill levels.
2. OutWit Hub
One of the most extensively used web scraping and Data Extraction tools in the market, OutWit Hub browses the Web and automatically collects and organizes relevant data from online sources. The tool first segregates web pages into separate elements and then navigates them individually to extract the most relevant data from them. It is primarily used for extracting data tables, images, links, email IDs, and much more.
OutWit Hub is a generic tool that packs in a wide range of usage – right from ad hoc data extraction on distinct research topics to performing SEO analysis on websites. It combines a mix of both simple and advanced functions, including web scraping and data structure recognition. OutWit Hub has an extension for both Chrome and Mozilla Firefox.
With Octoparse, you can extract data in three simple steps – pointing, clicking, ad extracting – without requiring any code. You just have to enter the website URL you wish to scrape and extract data from, then click on the target data, and finally run the extraction function to retrieve the data! It is that simple.
Octoparse allows you to scrape any website. It uses automatic IP rotation to prevent sites from blocking your IP address. This lets you scrape as many websites as you would like. Besides being extremely user-friendly, Octoparse is laden with many advanced features like a 24/7 cloud platform and scraping scheduler. You can also download the extracted data as CSV, Excel, API files or save them directly to your database.
4. Web Scraper
With Web Scraper, you can build site maps from different kinds of selectors which further makes it possible to tailor Data Extraction to disparate site structures. The Cloud Web Scraper service lets you access the extracted data via API or webhooks. Since it has an in-built cloud service, it can scale with your growing business – so you need not worry about outgrowing its services.
You simply have to open a website and click the data you want to extract, and that’s it. ParseHub’s ML relationship engine can screen the page/site to understand the hierarchy of elements and hand out the desired data in seconds.
You can download the extracted data in JSON, Excel, or API formats. Also, you can instruct ParseHub to search through forms and maps, open drop downs, login to websites, and handle websites with infinite scroll, tabs, and pop-ups.
Mailparser is an advanced email parser that can extract data from emails. Email parsing is different from web scraping in the sense that in email parsing instead of extracting data from HTML websites, the tool pulls data from emails.
MailParser is a powerful and easy-to-use tool that lets you extract data without requiring any elaborate coding. It has an all-round tool – the HTTP Webhook that can perform a wide variety of functions.
To use Mailparser, you need to forward the emails to it, and the tool automatically scrapes the data you want to extract based on the custom extraction rules that you feed in the tool during the set-up process. After the data is retrieved, you can export the scraped data either through file downloads/native integrations or through the generic HTTP Webhooks.
DocParser is a Data Extraction tool specifically designed to extract data from business documents. This versatile tool makes use of a custom parsing engine that can support numerous and varied use cases. It extracts all the relevant information (data) from business documents and moves it to the desired location.
DocParser completely eliminates the task of manual data entry and streamlines your business with non-disruptive workflow automation. You can use DocParser for processing invoice and accounts payable; converting purchase & sales orders, and HR forms; extract data from standardized contracts and agreements, among other things.
These are seven top Data Extraction tools should be on your checklist if you work with Big Data or are aspiring to build a career in this field. The biggest advantage of using Data Extraction tools is that they eliminate the manual factor from the equation, thereby saving both time and money.
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In how many ways data can be extracted?
Data extraction is the process of gathering data from various sources for analyzing and processing data. This data can be extracted according to the analysis goals and company needs. There are three possible ways to extract data that are as follows. In Update Notification type of extraction, the source system sends a notification whenever a change has been made in a record. Many databases come with similar functionality to support database replication. Incremental Extraction makes the delta changes in the data. The engineer first needs to add complex data extraction logic in the source system before extracting the data. The extraction tools are programmed to detect any changes made, based on the time and date. Some data sources have no mechanism to identify any changes made to the source data. In that case, a full extraction is the only way left to replicate the source.
What are the applications of OutWit Hub?
OutWit Hub is one of the leading data extraction tools and is known for various applications in multiple domains. Some of these applications are as follows - OutWit allows you to extract the latest news from the search engines using its built-in RSS feed extractor. You can use it for SEO purposes as it can monitor the key elements in the websites or even on selected web pages. Deep web searches, social networking monitoring, and e-commerce are some other applications of OutWit Hub.
Are data mining and data extraction similar?
Many people get confused between data mining and data extraction and end up considering them two different terms for the same process. But this is a wrong deduction. Data mining and data extraction are different from each other right from the definition. Data mining is the process where large chunks of data are analyzed to gather some similarities, patterns, or relationships between different data sets that are missed by the traditional analyses techniques. Data extraction on the other hand extracts the data from the online data sources which is stored in the data warehouses for further processing.