10 Top Data Extraction Tools in 2023: Importance, Categories & Understanding
Data Extraction is the process of retrieving information from sources that may not be structured or easily accessible. It can be done manually but is often performed with the help of specialized software or tools. Businesses use this data for further analysis or to make better decisions. This helps them save time and money. There are many data extraction tools available in the market, each with its own set of features. It can be difficult to choose the right one for your needs.
Data extraction can be a very burdensome and time-consuming task as it requires one to sift through a lot of data that may not be useful. Therefore, to solve this problem, we have compiled a list of the 10 best data extraction tools for you to use in 2023, but before moving on to the list let's understand more about data extraction.
What is Data Extraction?
Data extraction is the process of extracting data from various sources for further processing and analysis. This data can be in the form of unstructured, semi-structured, or structured data. Companies extract data for different purposes, such as business intelligence, data migration, or data replication. Extracting data is the first step in the ETL process, and it is important to have a synchronized tool in order to effectively analyze the data. A data extraction tool can help prepare the data for further analysis and provide insights that would otherwise be unavailable.
Understanding Data Extraction Process and ETL
The importance of data extraction is best understood when considering the ETL process as a whole. ETL allows companies and organizations to streamline data management by consolidating data from different sources into a centralized location and assimilating different types of data into a common format. The first step in the ETL process is data extraction, which can be done in a number of ways. The most common method is to use an extract, transform, and load (ETL) tool. Other methods include using custom scripts or programming languages such as SQL.
The data extraction process using an ETL tool usually has the following steps:
Data is extracted from one or more sources or systems. This process locates and identifies relevant data, then prepares it for processing or transformation. Extraction allows many different kinds of data to be combined and ultimately mined for business intelligence. This makes it possible to gain valuable insights from large and complex data sets.
The next phase after data extraction is transformation. Here, the data is sorted, organized, and cleansed. This may involve deleting duplicate entries, removing or enriching missing values, and performing audits. The goal is to produce data that is reliable, consistent, and usable.
The transformed data is loaded into the target system, which can be a data warehouse, database, or analytics platform. The data is now ready for further analysis or reporting. The ETL process is an important part of data management and helps ensure that data is accurate, consistent, and available when needed.
Methods of Data Extraction
There are many different methods of data extraction, depending on the type and format of the data sources. For digital data sources, common methods include web scraping and database exporting. For physical sources, common methods include manual selection and OCR (optical character recognition). Web scraping is a process of extracting data from websites, typically through automated means. Database exporting is the process of extracting data from a database in its native format (usually CSV or XML).
Manual selection is the process of manually selecting data from a physical source, such as a book or newspaper. OCR is a process of automatically extracting text from images, such as scanned documents. Each method has its own advantages and disadvantages. Web scraping and database exporting are typically fast and accurate but can be blocked by website owners or database administrators. Manual selection is slower but allows for greater flexibility in selecting data. OCR is fast and accurate but only works on text-based sources.
In general, the best method of data extraction depends on the specific needs of the project. For large-scale projects that require a lot of data, automated methods like web scraping and database exporting are typically the best option. For smaller projects or projects with specific data requirements, manual selection may be the best option. OCR can be used for any text-based sources but is not suitable for other types of data.
Types of Data Structures used in Data Extraction
There are three common types of data structures:
1. Unstructured data:
This is data that does not have a predefined format and can be in any form, such as text, images, audio, or video. It is typically unorganized and difficult to process.
2. Semi-structured data:
This type of data has some structure but not as much as structured data. It can be in the form of XML, JSON, or CSV files.
3. Structured data:
This is data that is organised into a predefined format, such as a database table. It is easier to process than unstructured data and can be queried using SQL. Data extraction can be performed on all three types of data structures. However, it is usually more difficult to extract data from unstructured sources.
Types of Data Extraction
Now that you have gained a basic understanding of how Data Extraction actually works let’s take a look at the different types of Data Extraction techniques commonly used in the market. The Data Extraction methods can be mainly divided into Logical and Physical. These further include various types, as detailed below:
1) Logical Data Extraction
A) Full Extraction
B) Incremental Extraction
2) Physical Data Extraction
A) Online Extraction
B) Offline Extraction
1) Logical Data Extraction
Logical extraction is the most commonly used data extraction method, and it can be split into two types:
A) Full Extraction
Usually, this method occurs during the initial data load. Here, the entire data set is extracted from the source at once without making changes to the last successful extraction. As a result, there's no need to monitor changes to the data source because this process captures all of the presently available information on the system.
B) Incremental Extraction
In contrast, Incremental extraction only fetches data that has changed since the last successful extraction. This is done by recording a timestamp or a watermark for every row of data extracted from the source. The next time we run an extraction process, we check for any new or updated records against this watermark/timestamp. So, in this way, we can extract only the changed data, which is more efficient than extracting the entire data set every time.
2) Physical Data Extraction
Physical extraction involves extracting data directly from the source system through its APIs or by connecting to the database. This is different from logical extraction, where data is first copied to an intermediate system (usually a staging area) and then extracted from there. There are two types of physical data extraction:
A) Online Extraction
You can use this process to directly transfer data from the source to your Data Warehouse. In order for this method to work, you will need extraction tools that connect directly to the source system. Instead of connecting it directly, you could also link it transitional system instead. The transitional system is a copy of the source system except with more structured data.
B) Offline Extraction
With this method, the data is explicitly saved in an external location instead of being accessed directly from the original source. The data for this process can be either structured or unstructured; however, it must first go through extraction routines. Some of the file structures that it uses are a flat file, a dump file, or remotely extracting database transaction logs.
Importance of Data Extraction Tools
Data extraction tools are important because they automate the process of extracting data from multiple sources. This can save a lot of time and effort, especially when dealing with large data sets. In addition, data extraction tools can help ensure that the extracted data is accurate and of high quality. They can also provide valuable insights that would otherwise be unavailable.
Big Data has become increasingly important in recent years, and data extraction tools have played a vital role in making this possible. Every organization, whether it is a start-up or an MNC, wants to have valuable insights about its customers, products, employees, etc. in order to make better decisions. Data extraction is the process of fetching relevant data from multiple sources and consolidating it into a single database or file. This data will save a lot of time and can be used for further analysis or reporting.
Categories of Data Extraction Tools
To determine the best data extraction tool for a company, it is important to consider the type of service the company provides and the purpose of data extraction. Data extraction tools can be categorized into three categories:
1. Batch Processing Tools
Batch Processing is the best solution for transferring data in cases where the data are stored in obsolete forms or are legacy data. This means that the sources may involve a single or few data units, and they may not be too complex. Batch Processing can also be helpful when moving data within a premise or closed environment. To save time and minimize computing power, this can usually be done during off-work hours.
2. Open Source Tools
Open-source data extraction tools are a good option for companies who want to save money. Company employees typically have the skills necessary to use these tools, and some vendors even offer free versions of their products. Therefore, open-source data extraction tools are a viable option for many companies.
The most common type of data extraction tool available today is the cloud-based data extraction tool. These tools make it easy to connect data sources and destinations without writing any code. This makes it quick and easy for anyone within your organization to access the data. Cloud-based data extraction tools also take away the stress of computing your logic and eliminate the security challenges associated with handling data yourself.
Top 10 Data Extraction Tools in 2023
Boltic is a free-to-use data extraction tool that can be used to extract data from multiple sources, including websites, social media platforms, and databases. It offers a variety of features, such as the ability to create custom data extraction rules, schedule data extraction jobs, and receive real-time notifications when new data is extracted. You can use it to create ETL pipelines or perform data analysis on the extracted data. Boltic also offers a REST API that can be used to integrate it with other applications.
2. Captain Data
Captain Data is a good web scraping tool for sales and marketing teams because it offers a wide range of data extraction and automation scenarios. With Captain Data, you can easily extract structured data from over 30 sources, including LinkedIn, Google, TrustPilot, and more. In addition to being a great web scraping tool, Captain Data is also a complete data automation suite with over 400 ready-to-use workflows. This makes it a great solution for sales and marketing teams who want to scale their lead gen and growth hacking strategies.
Diffbot is a software company that specializes in extracting data from the web for enterprise companies. Diffbot's suite of features turns unstructured web data into structured, contextual databases, making it an invaluable tool for businesses with specific data crawling and scraping needs. Customers appreciate Diffbot for its APIs and advanced technical resources, which make extracting social media data a breeze. However, some reviewers caution that Diffbot has a bit of a learning curve, and recommend taking advantage of the company's two-week free trial with full API access to get a feel for the tool before committing to a paid plan.
Octoparse is an easy-to-use web scraping service that enables users to extract data from websites without needing to code. It offers a free plan with up to 10 crawlers, and the standard plan starts at $75/month. Octoparse's main features include point-and-click data extraction, support for extracting text, links, image URLs, and more, and the ability to schedule and run automated tasks. Octoparse is a great choice for anyone who needs to extract data from websites for lead generation, price monitoring, marketing, or research.
Brightdata is the perfect solution for businesses of all types that want to leverage web data to their advantage. The Brightdata Data Collector uses an API to send data to the desired app, making it easy to collect data at scale with zero infrastructure. It is also compatible with a wide range of applications, making it a versatile tool for businesses. Prices for the Data Collector start at $350 for 100,000-page loads. You can use it for market research, SEO, search engine crawling, and stock market monitoring.
6. Web Scraper
You can easily extract data from any website using the Web Scraper Chrome extension. With just a few clicks, you can download tables and lists in CSV format without having to write any code. The paid plans offer additional features such as automation, more export options, a proxy, parser, and API. Prices start at $50 per month. Whether you need simple data scraping or more advanced features, Web Scraper has a plan to fit your needs.
Simplescraper is a powerful, free web scraping tool that makes it easy to scrape data from websites. With Simplescraper, you can quickly and easily scrape data from thousands of web pages with one click, export it into Google Sheets, and even extract data from behind links with deep scraping. Simplescraper is an incredibly useful tool for anyone who needs to gather data from websites.
8. Scraper API
Puppeteer is a Node library that makes it easy to scrape webpage content. You get access to a high-level API to control Chrome or Chromium via the DevTools Protocol. The puppeteer can also crawl SPAs and generate pre-rendered content, in addition to taking screenshots and PDFs of pages. By default, it runs headless, but you can configure it to run full Chrome or Chromium if needed. Plus, it's possible to build a scraping application between Node.js and Puppeteer.
Benefits of using Data Extraction Tools
There are many benefits of using data extraction tools, including:
- Business process management
- Data extraction, simplified
- Increases Efficiency and Productivity
- Ease of Use
Data extraction tools are essential for anyone who needs to gather data from websites. They come in handy for a variety of purposes, such as lead generation, price monitoring, marketing, and research. You cannot underestimate the power of these tools. When used correctly, they can give you a significant competitive advantage. You can use Boltic to collect data from websites with ease. Boltic can help with your data needs in a number of ways.
We can help you transfer data, transform it into analysis-ready form, and even provide complete automation for your analytics needs! This way, you can focus on other key business activities without having to worry about your data. Boltic is the perfect solution for anyone who wants an all-in-one solution for their data needs!
What is an example of data extraction?
How do I extract data from multiple sources?
How is data extracted in ETL?
What is the best data extraction software?
What is manual data extraction?