Many of you have experienced the need to overcome latency in getting time-bound response while dealing with massive data sets. Data as we know it has huge latent potential to provide us intuitive and valuable insights useful in solving the business problem. Plenty of these insights cannot be properly extracted due to the lack of infrastructure to store, manage, retrieve and process massive data chunks. Traditionally, people were accustomed to have in-house capability but with the exponential surge in data, soon it became obsolete. Privacy concerns and sensitivity of information in data were the major deterrence factor for enterprises to become pliable towards new advancements in data technology.

Nowadays, buzzwords like BigQuery is getting prevalent with its promising capabilities to address the shortcomings and drawbacks of existing data ecosystem.

What is BigQuery?

BigQuery is Google’s serverless, highly scalable enterprise data warehouse especially designed to make data analysts more productive with unmatched price-performance. Because there is no infrastructure to manage, you can focus on uncovering meaningful insights using familiar SQL without the need for a database administrator.

BigQuery in perspective

With BigQuery one can achieve outputs in unprecedented time with even more accuracy in quality. To emphasis its capability, I have demonstrated an illustration below to design and integrate a simple Django based Web-Service Endpoint using public data set of NYC Taxi and Limousine services.

Note: Click here for link to NYC’s taxi and limousine trips data set.

Design Motivation

Django, a most popular python based web designing framework connects well with both back-end and front-end interface effortlessly. It is free, open source, fast, fully loaded, secure, scalable and versatile.

The framework is primarily driven by the MVC (Model View Controller), an architectural paradigm that allows programmers to keep a web application’s user interface (UI) and business logic layers separated. The approach further helps programmers to simplify and speed up development of large web applications.

BigQuery is introduced and interfaced here as REST API based service to query NYC’s massive data set with standard SQL commands. Google also provides an interactive UI to test and manage massive data queries on cloud. It could be helpful in testing queries on the go before production deployment.