BigQuery in Data Science

Well, sometimes to access big data you have to use a BigQuery. It is important to understand that you are not only storing the data in the cloud, you are also using the data analysis tools in the cloud. You use your computer to control what these cloud computers do with the data. In this article, I will introduce you to the concept of BigQuery in data science.

The Google Cloud Platform

Google’s Cloud is a platform of cloud computing services that run on the same infrastructure as Google Search and YouTube does. This is a cloud strategy that has been used successfully at Amazon and Microsoft. Using your services and data products to build a cloud offering seems to create a good environment for the user and the business to benefit from the advancements and improvements in products and clouds.

Also, Read – Scaling and Normalization in Machine Learning.

Google Cloud Platform has over 100 different APIs and data service products available for data science and artificial intelligence. This main Google API service is known as BigQuery.

BigQuery from Google 

A Representational State Transfer (REST) ​​software system is a set of code that defines a set of communication structures to be used to create web services, typically using HTTP and https requests to communicate. This provides a wide range of interoperability for different computers with different operating systems trying to access the same web service.

BigQuery is based on a RESTful web service (think about contacting web pages with URLs that ask specific questions in a standard format, then getting an answer like a browser gets a web page) and multiple libraries for Python and other languages ​​hide the complexity of queries coming and going.

Abstraction in software systems is essential for making large systems work and for being reasonable in programming. For example, although a web browser uses HTML to display web pages, there are layers and layers of software underneath, doing things like forwarding IP packets or manipulating bits. The cool thing about abstraction here is at the webpage level, we don’t care. We just use it.

BigQuery is a serverless model. This means that BigQuery has roughly the highest level of abstraction in the cloud community, so the user does not have to worry about running VMs (bringing new VMs online in the cloud), RAM, a number of processors, etc. You can go from one to thousands of processors in seconds, paying only for the resources you’re actually using. Be aware that in this book, Google will allow you to use the cloud for free, so you won’t even have to pay at all during your trial.

Signing up on Google for BigQuery

Go to cloud.google.com and sign up for your a trial. While Google does require a credit card to prove you’re not a robot, it won’t charge you even at the end of your trial without you manually switching to a paid account. If you go over $ 300 during your trial (which you shouldn’t), Google will notify you, but won’t charge you.

Also, Read – Neural Network with Python Code Only.

The $ 300 limit for the trial should be enough for you to do a bunch of queries and learn on the BigQuery cloud platform. I hope you liked this article on the concept of BigQuery in Data Science. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Data Science and Machine Learning.

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