via Web Scraping
Simplify extraction, automate process & empower business with easy access
Data Aggregation via Web Scraping
Simplify extraction, automate process and empower business with easy access
Web scraping is a powerful data sourcing technique that leverages tools and frameworks to scrape data from the public domain. The scraped data can be aggregated and transformed into the meaning format and loaded into any database in a structured format. Web scraping can be done using custom programming or by leveraging many tools.
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.
Web Scraping and transforming large volume of data into meaningful datasets that can be annotated or labelled for machine learning and visualization
Easily scrape data from target websites and organize them into 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 MS Azure or GCP to expose scraped and aggregated data as service to be consumed by applications on demand.
Label and annotate the data to build machine learning models and cognitive intelligence.
How it works? – 3 Stage Model
Initially our BI analyst team will get the source applications from which data needs to be extracted or scraped. Web scraping will be done to scrape and transfer data from a website to a new datastore. The data fetched from multiple source system may be structured or unstructured data. Then the extracted data will be cleaned up and validated before loading it into a common database.
The process flow involves three main steps:
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.
In the second stage, the extracted data will be validated, normalized and homogenized and converted into a structured data.
In the final stage of ETL, the normalized data will be loaded into a common database repository.
Tools we Use
Our Engagement Model
Non- Disclosure Agreement
We will ensure a formal Non-Disclosure Agreement and Data Security Agreement in place before getting started.
Share your specifications based on the questionnaire shared by our team – Source, type of data, data structure etc.
Proof of Concept
Execute a Proof of Concept (Poc) with smaller scope and illustrate the feasibility of the data extraction and aggregation.
Statement of Work
Elucidate the broader scope with statement of work either on a fixed bid engagement model or on dedicated resource model (T&M).
Free Consulting with Our Team
Validate your approach with a free consulting with our data annotation or AI & ML or Data Visualization team. We are certified partners of AWS and MS Azure and can assist in your Cloud strategy.