Data aggregation via Web Scraping

Simplify extraction, automate process and empower business with easy access!

Overview

Web scraping services is a powerful technique for gathering data and storing them in a database or source system for future use. ETL stands for Extract Transform Load, is process of fetching data from multiple heterogeneous data repositories, transforming it into more structured and normalized dataset and loading it in a single data store. 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.

Service Offerings

Data Scraping

Easily scrape data from target websites and organize them into structured data format for annotation and consumption via services

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

Building Data Warehouse

Gathering transition data from multiple heterogeneous sources for using it for Sentiment Analysis, getting meaningful insights and visualization.

Data Labeling

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:

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.

Transform

In the second stage, the extracted data will be validated, normalized and homogenized and converted into a structured data.

Load

In the final stage of ETL, the normalized data will be loaded into a common database repository.

Tools we Use

Web Scraping

ETL

Our Engagement Model

Share your specifications based on the questionnaire shared by our team – Source, type of data, data structure etc.

Execute a Proof of Concept (Poc) with smaller scope and illustrate the feasibility of the data extraction and aggregation.

3.Elucidate the broader scope with statement of work either on a fixed bid engagement model or on dedicated resource model (T&M).

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.

We will ensure a formal Non-Disclosure Agreement and Data Security Agreement in place before getting started.