Wednesday, 3 September 2014

How to Build Data Warehouses using Web Scraping

Businesses all over the world are facing an avalanche of information which needs to be collated, organized, analyzed and utilized in an appropriate fashion. Moreover, with each increasing year there is a perceived shortening of the turnaround time for businesses to take decisions based on information they have assimilated. Data Extractors, therefore, have evolved with a more significant role in modern day businesses than just mere collectors or scrapers of unstructured data. They cleanse structure and store contextual data in veritable warehouses, so as to make it available for transformation into useable information as and when the business requires. Data warehouses, therefore, are the curators of information which businesses seek to treasure and to use.

Understanding Data Warehouses
Traditionally, Data Warehouses have been premised on the concept of getting easy access to readily available data. Modern day usage has helped it to evolve as a rich repository to store current and historical data that can be used to conduct data analysis and generate reports. As it also stores historical data, Data Warehouses are used to generate trending reports to help businesses foresee their prospects. In other words, data warehouses are the modern day crystal balls which businesses zealously pore over to foretell their future in the Industry.

Scraping Web Data for Creating Warehouses

The Web, as we know it, is a rich repository of a whole host of information. However, it is not always easy to access this information for the benefit of our businesses through manual processes. The data extractor tools, therefore, have been built to quickly and easily, scrape, cleanse and structure and store it in Data Warehouses so as to be readily available in a useable format.

Web Scraping tools are variously designed to help both programmers as well as non-programmers to retain their comfort zone while collecting data to create the data warehouses. There are several tools with point and click interfaces that ease out the process considerably. You can simply define the type of data you want and the tool will take care of the rest. Also, most tools such as these are able to store the data in the cloud and therefore do not need to maintain costly hardware or whole teams of developers to manage the repository.
Moreover, as most tools use a browser rendering technology, it helps to simulate the web viewing experience of humans thereby easing the usability aspect among business users facilitating the data extraction and storage process further.


The internet as we know it is stocked with valuable data most of which are not always easy to access. Web Data extraction tools have therefore gained popularity among businesses as they browse, search, navigate simulating your experience of web browsing and finally extract data fields specific to your industry and appropriate to your needs. These are stored in repositories for analysis and generation of reports. Thus evolves the need and utility of Data warehouses. As the process of data collection and organization from unstructured to structured form is automated, there is an assurance of accuracy built into the process which enhances the value and credibility of data warehouses. Web Data scraping is no doubt the value enhancers for Data warehouses in the current scenario.

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