How Google Applies Big Data?
Google is an undisputed champion when it comes to big data. They have developed several open source tools and techniques that are extensively used in big data ecosystem. With the help of different big data tools and techniques, Google is now capable of exploring millions of websites and fetch you the right answer or information within milliseconds.
The first question that comes to our mind is how can Google perform such complex operations so efficiently?
The answer is Big data analytics. Google uses Big Data tools and techniques to understand our requirements based on several parameters like search history, locations, trends etc. Then it goes through an algorithm where complex calculations are done and then Google effortlessly displays the sorted or ranked search results in terms of relevancy and authority designed to match the user’s requirement.
In order to execute the complex process of understanding the user’s requirement and preferences, Google has adopted the following techniques-
Indexed pages are the collection of web pages stored to respond to search queries. Indexing is the process of adding web pages into google search index. It involves assigning keywords or phrases to web pages within a metadata tag or meta-tag so that webpage can be retrieved easily with a search engine that is tailored to search the keywords field. Once the meta-tag is created, Google will crawl and index your webpage. It generally takes 4 days to 4 weeks for any new website to be crawled and indexed by Google.
Real-time Data Feeds:-
Although it doesn’t promote itself as such, Google is actually a collection of data and a set of tools for working with it. It has progressed from an index of web pages to a central hub for real-time data feeds on just about anything that can be measured such as weather reports, travel reports, stock market and shares, shopping suggestions, travel suggestions, and several other things.
Big Data analysis which implies utilizing tools intended to deal with and comprehend this massive data becomes an integral factor whenever users carry out a search query. The Google’s algorithms run complex calculations intended to match the questions that user entered with all the available data. It will try to determine whether the user is searching for news, people, facts or statistics, and retrieve the data from the appropriate feed.
Knowledge Graph Pages:-
Google Knowledge Graph is a tool or database which collects all the data and facts about people, places and things along with proper differentiation and relationship between them. It is then later used by Google in solving our queries with useful answers. Google knowledge graph is user-centric and it provides them with useful relevant information quickly and easily.
Literal & Semantic search:-
The main aim of the literal search engine is to find the root of your search phrase by looking for a match for some of the word or entire phrase. The root of the phrase is then examined and explored upon to display better search results. While semantic search engine tries to understand the context of the phrase by analyzing the terms and language in knowledge graph database to directly answer a question with specific information.
Google can keep a track on users across the web by using cookies. If a user is logged or signed into Google and the user is simultaneously browsing other websites, Google can track the websites they are visiting. Google tracks its users across the web by tracking cookies. Thus, Google can collect several data related to users such as their preference, inclination, favorites, requirements etc. Whenever a user searches anything on Google, it incorporates all that information before displaying the results in proper rank.
The moment you sign in into your google account, it uses your search history, trends and location to provide accurate search results. Google collects all the data related to the frequency of sites visited, search phrases used, the timings, data downloaded etc. Google then uses those data to streamline the search results depending upon different scenarios.
The phrases are understood through a system that analyzes their root and relationship based on past search history, trends and relationship to each other.
For complex operations such as translation, Google summons other inbuilt algorithms that are themselves based on Big Data. Google’s translate service analyses millions of other pieces of translated text or speech, to determine the most precise interpretation.
Businesses ranging from small scale to large scale are regularly making use of Big Data analytics whenever they advertise through Google Adwords service. Whenever user surfs through different websites, it learns their preferences, likes, dislikes, inclinations etc. on the basis of which Google shows them several advertisements related to products or services that user might be interested in. Advertisers gain admittance to Big Data analytics when they utilize Google Adwords and other services such as Google Analytics to lure individuals who fit their customer profile to their sites and stores.
Ranking and Prioritizing the Search Results:-
There are numerous different factors that go into the rankings of your search results. Google examines the following features of a website’s content when defining relevance including:
→ Site structure relations
→ Page structure relations
→ External link relevance
→ Internal link relevance
How much Data does Google handles per day?
Google now processes over 40,000 search queries every second on average, which translates to over 3.5 billion searches per day and 1.2 trillion searches per year worldwide.
A place where google stores and handles all its data is a data center. Google doesn’t hold the biggest of data centers but still it handles a huge amount of data. A data center normally holds petabytes to exabytes of data.
Google currently processes over 20 petabytes of data per day through an average of 100,000 MapReduce jobs spread across its massive computing clusters.
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