1.Web scraping is a powerful data sourcing technique that leverages tools and frameworks to scrape data from the public domain.
2.The scraped data can be aggregated and transformed into the meaning format and loaded into any database in a structured format.
3.Web scraping can be done using custom programming or by leveraging many tools.
4.Web scraping is a powerful data extraction mechanism that will accelerate your data journey to annotate them for better grouping, build a cognitive intelligence layer on top of it using AI & ML, and leverage data visualization tools for better insights.
Data Scraping: Easily scrape data from target websites and organize them into a structured data format for annotation and consumption via services.
Building Data Warehouse: Gathering transition data from multiple heterogeneous sources for using it for Sentiment Analysis, getting meaningful insights and visualization.
Data as Service: Leverage cloud services like AWS or Microsoft Azure or GCP to expose scraped and aggregated data as a service to be consumed by applications on demand.
Data Aggregation — 3 Stage Model
Web scraping will be done to scrape and transfer data from a website to a new datastore. The data fetched from multiple source systems may be structured or unstructured data. Then the extracted data will be cleaned up and validated before loading it into a common database.
Stage 1: Extract
This is the first stage of ETL, where data can be fetched from different data repositories of the company.
The data extracted may be unstructured, non-understandable data format.
Stage 2: Transform
In the second stage, the extracted data will be validated, normalized, and homogenized and converted into structured data.
Stage 3: Load
In the final stage of ETL, the normalized data will be loaded into a common database repository.
Data Aggregation — 4 Best Tools
ETL — Extract, Transform, and Load