Quickest Way To Get Started With BigQuery

Google Cloud’s BigQuery lets you do massive-scale data analysis using SQL and not have to worry about scaling up your infrastructure to support it. This enables you to both find insights in your data that may have been previously undiscovered, and do so rapidly, so you can make fast decisions that grow your business. But how do you get started? Read on for step-by-step instructions on how to set up and connect BigQuery with the rest of your GCP account, no matter what type of project you’re working on or where you are in the world!

What Is BigQuery?

A big data analytics tool from Google that lets you query your data in Google’s cloud. BigQuery analyzes large amounts of data and then delivers answers to complex questions very quickly. It is a highly scalable, super-fast, low-cost analytics data warehouse. It combines compute power with a SQL syntax that makes it easy to analyze billions of rows instantly without having to install and run software or manage infrastructure.

How Does it Work?

The tech behind BigQuery can be complex, but you don’t need to understand all of it to start using it. Essentially, you’re just collecting a lot of data and sending it off to Google's servers. It’s up to Google's engineers to determine what happens with your data. At a basic level, however, here are some things that are worth knowing: You access your data using SQL-like queries using a browser-based interface or by uploading scripts stored in Google Drive. You can also download a file for local analysis or store larger datasets in cloud storage like Amazon S3 or Google Cloud Storage.

What Can I Do with It?

While traditional spreadsheets can be used to examine large volumes of data, they can’t process and analyze that data in real-time. For example, you might have a spreadsheet with sales figures for Q1 and another with projections for Q2. If you want to see what happened in Q2 when compared to Q1 using these two spreadsheets, you’d have to manually compare them. Not only is that labor-intensive and inefficient, but it also provides less actionable insight. However, you could use BigQuery—Google’s cloud-based big data platform—to automate your comparison analysis and get answers immediately.

Where do I sign up?

The best way to get started with BigQuery is to visit Google Cloud Platform and create a free trial account. You’ll need to provide a credit card but if you don’t end up using much of any services, they won’t charge you. During signup, click on Try out our products for free! next to BigQuery: Free Tier. This will walk you through creating a sample dataset that you can then download so that you can play around with it locally before diving into your own data.

Creating a new project in BigQuery

Project names in BigQuery must be made up of alphanumeric characters (no special characters, such as &, /, ! or _ ). Spaces are not allowed. The name must begin with a letter and can only contain letters, numbers, and underscores. Project names cannot be changed after creation, so choose your project name carefully. It is also important to know that by default all resources associated with a project will be deleted when you delete a project. Therefore, to avoid unintended consequences, we recommend users set up any additional datasets, tables, or views outside of projects before populating them. When you need multiple datasets and large-scale sharing between collaborators in addition to easy sharing for yourself, using projects becomes more relevant for you.

Introduction to Google Sheets

Google Sheets is a free, browser-based spreadsheet program that enables you to create spreadsheets and analyze data with ease. In Google Sheets, you can insert rows and columns of data, create tables of numbers, calculate stats, apply formulas, format data for readability, and more. You can easily share your sheets with others via email or embed them directly into web pages.

Getting started with Google Sheets

Before you start using BigQuery to process big data, you need to create a Google Sheets spreadsheet that contains all of your data. But first, we need to ensure that your spreadsheets can make it easy for a computer program like BigQuery to interact with them. All we have to do is format your spreadsheet so that each value becomes its own column in our table and each unique value becomes its own row in our table. This process is known as normalizing your data.