Updated March 14, 2023
Introduction to Data Extraction Tool
Data generated from conventional and digital sources can be classified into thirteen different types of viz. Structured/unstructured/semi-structured, Big data having high volume/velocity/veracity, Sensor data, Time-stamped data, Open data, Dark data, Operational data, real-time data, Genomics data, High dimension data, Trans-analytic data, Spatiotemporal data, Outdated data, and Trans-analytic data.
Data Extraction is a process by which the data from various sources in the application landscape are culled out periodically or in real-time, massaged to suit the format of the database or data analytics platform, and loaded into the destination database.
Types of Data Extraction tools
Below are the three types of Data Extraction Tool:
1. Batch (Legacy) processing tools
These tools extract the data from OLTP legacy databases during the off-peak hours to reduce the load in the system. BI applications of standard ERP manage this extraction process. This tool is well suited for applications with homogenous data sources hosted on-premises.
2. Open Source Tools
Well suited for low-cost budget applications with needed infrastructure and sufficient knowledge are there. Limited edited of some of the products are available as open source.
3. Cloud-based tools
Modern, latest generation products are hosted in the cloud. It offers real-time extraction of data and flexible modeling of data dynamically. These solutions are highly secured and complaint.
Top 15 Data Extraction Tools
Below are the top 15 tools in data extraction:
- Web Scraper: Very simple to use tool. Email link, Pricing, contact details, images, and pages can be extracted from the web
- OutWitweb: A most popular tool to extract data from websites. Ideal for Tables, Images, and mail id and link extraction from Web.
- Octaparse: It extracts open data from any website without any editing efforts. Data, IP address, phone numbers, Mail ids can be extracted easily.
- Parsehub: Graphic User Interface (GUI) based tool. Images, Document, Phone no and contact can be extracted from the web using this visual tool
- Spinn3r: Has access to all blogs published on the web and the blogs are indexed.
- Fminer: Visual tool to extract data from the web. It also acts as a macro recorder.
- Table Capture: As one browser through Chrome the data gets extracted and it’s a hassle-free way of extracting the data
- Scrapy: Open source extraction tool. Python is used here and it allows to develop their own code to extract data
- Tabula: It is a Desktop App. Run-on MS, Linux, Mac operating system. Has a feature to convert PDF into XLS, csv formats and can be edited there. Used mostly in content creation in Journalism
- Dexi.io: No download required and it can be opened in the browser. Crawlers can be set up to data that can be culled out from web. Browsed data can be directly saved into google drive.
- Import.io: Enable data extraction without having to write any code. Web data, Email iD, Images, and phone numbers can be extracted
- Visual Web ripper: Data extraction can be automated using this tool. Web harvesting is a unique feature here.
- Webhose.io: Used in Business applications to extract data
- ContenGrabber: Extracts data from any website and converts it too in the format you need.
- Data Extractor: It is an SAP product that replaced several T codes in SAP ERP. It allows the extraction of SAP Data into an Excel table.
Data from a Standard ERP can be extracted and imported into another using extraction tool provided in the destination ERP Product. For example, SAP has data connectors to extract data from other ERP products like Oracle, MS Dynamics, Marshall of Ramco, IFS, etc. Similarly, Oracle has its own tools to extract data from other ERPs.
ETL Process (Legacy Applications)
Below is the ETL process of legacy application:
- Extract: In traditional applications, data is culled out from the database maintained by an online transaction system (OLTP). The extraction takes place at a pre-defined interval and is kept in a temporary staging area. The system maintains the log of the data extracted and it is used to extract data further without any omission and duplicates.
- Transform: The data from the OLTP system is not analytics-ready and it requires cleansing, optimizing, calculation, aggregation, and addition of some metadata to make it enriched before it is taken for further processing. Raw data extracted from OLTP is transformed to suit analytics in the staging area.
- Load and then Analyze: Transformed data is loaded into the BI database maintained as part of Online Analytical Processing (OLAP). Plenty of BI Analytics software is there in the market to get insights from BI database for better decision making. Incorporating any change in the design is very difficult and the entire process will have to be rerun
ELT Process (Digital Applications)
Below is the ETL process of digital application:
Extract: Structured and unstructured data from the OLTP system, social media, Web, Email, Video/Audio file, cloud applications are extracted on a real-time basis.
Load: Extracted data is simply loaded into the Analysis database. This database can be located on-premises or in the cloud.
Transform and Analyze: Unique feature in this method is
- Raw data is made available in real-time mode and meaningful real-time analysis of data can be made.
- Transformation of the data and the data modeling takes place in OLAP layer whenever
- The user has the flexibility to model the data the way he wants in a dynamic way as per the business needs at a different point in time and get the insights
- Abundant Storage, plenty of Compute power, and cloud option make this mode faster and robust.
- Many ready to use cloud offerings are available to manage the entire OLAP operations
Key points to be considered in choosing Data Extraction Tools
- Detailed logging of the extraction and loading events should be maintained for auditing and troubleshooting
- Periodical data refresh (Incremental data loading) should take place and an audit trail should be maintained.
- Should support structured and unstructured data files in any format and it should interface with any OLTP system and other latest data
- Necessary interfaces or API should be available to interact with any application and use its data
- Should report the fault or errors at any stage
- Must have good notification process and it should be pro-active
- It should have low latency, high scalability, and 100% accuracy
Conclusion
Data extraction tool helps to cull out data from any source and one will have to choose the right tool depending on the application and the data sources.
Recommended Articles
This is a guide to Data Extraction Tool. Here we discuss an introduction, types, top 15 tools with two different processes in detail points and key points. You can also go through our other related articles to learn more –
