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Data Analytics, Google Cloud Product Updates, Storage and Data Transfer

Ingest, Store, Query, and More: What BigQuery Can Do for You

By Leah Zitter | October 19, 2021

If you're a web developer, a software engineer, or anyone else working with small batches of data, you know how to use a spreadsheet. The problem arises when you have massive amounts of data that need to be stored, ingested, analyzed, and visualized rapidly. 

More often than not, the product you need to solve this problem is Google Cloud's serverless, fully-managed service, BigQuery. BigQuery deals with megabytes, terabytes, and petabytes of information, helping you store, ingest, stream, and analyze those massive troves of information in seconds.

Small stores can use Excel to classify, analyze and visualize their data. What if your organization is a busy multinational corporation with branches across cities and regions? You need a magical warehouse database you can use to store, sort, and analyze streams of incoming information. That's where BigQuery comes in.


What is BigQuery?


BigQuery is Google Cloud's enterprise data cloud warehouse built to process read-only data. It's fully managed, which means you don't need to set up or install anything, nor do you need a data-based administrator. All you need to do is import and analyze your data.

To communicate with BigQuery, you need to know SQL (Structured Query Language), the standard language for relational databases, used for tasks such as updating, editing or retrieving data from a database.


BigQuery in Action


BigQuery executes three primary actions:

  • Ingestion: uploading data by ingesting it from cloud storage or by streaming it live from Google Cloud partners, such as BigTable, Cloud Storage, Cloud SQL, and Google Drive, enabling real-time insights

  • Storage: storing data in a structured table, using SQL for easy query and data analysis

  • Querying: answering questions about data in BigQuery with SQL

Getting BigQuery up and running is fairly simple. Just follow these steps:

  1. Find BigQuery on the left-side menu of the Google Cloud Platform Console, under "Resources."

  2. Choose one or more of these three options:

  • Load your own data into BigQuery to analyze (and convert that data batch into a common format such as CSV, Parquet, ORC, Avro, or JSON).

  • Use any of the free public datasets hosted by Google Cloud (e.g., the Coronavirus Data in the European Union Open Data Portal).

  • Import your data from an external data source.


BigQuery ML


You can also use BigQuery for your machine learning models. You can train and execute your models on BigQuery data without needing to train and move them around. To get started using BigQuery ML, see Getting started with BigQuery ML using the Cloud Console.

Where can you find BigQuery (and BigQuery ML)? Both BigQuery and BigQuery ML are accessible via:


BigQuery Data Visualization


When the time comes to visualize your data, BigQuery can integrate with several business intelligence tools such as Looker, Tableau, and Data Studio to help you turn complex data into compelling stories.


BigQuery in Practice


Depending on your company's needs, you will want to take advantage of different capabilities of BigQuery for different purposes. Use cases for BigQuery include the following:

  • Real-time fraud detection: BigQuery ingests and analyzes massive amounts of data in real time to identify or prevent unauthorized financial activity.

  • Real-time analytics: BigQuery is immensely useful for businesses or organizations that need to analyze their latest business data as they compile it.

  • Log analysis: BigQuery reviews, interprets, and understands all computer-generated log files.

  • Complex data pipeline processing: BigQuery manages and interprets the steps of one or multiple complex data pipelines generated by source systems or applications.


Best BigQuery Features


BigQuery has a lot to offer. Here are some of the tools BigQuery's platform includes:

  • Real-time analytics that analyzes data on the spot.

  • Logical data warehouses wherein you can process data from external sources, either in BigQuery itself or in Google Drive.

  • Data transfer services where you can import data from external sources including:

    • Google Marketing Platform

    • Google Ads

    • YouTube

    • Partner SaaS applications to BigQuery

    • Teradata

    • Amazon S3

  • Storage compute separation, an option that allows you to choose the storage and processing solution that's best for your project

  • Automatic backup and easy restore, so you don't lose your information. BigQuery also keeps a seven-day history of changes.


BigQuery Pros


  • It's fast. BigQuery processes billions of data rows in seconds.

  • It's easy to set up and simple to use; all you need to do is load your data. BigQuery also integrates easily with other data management solutions like Data Studio and Google Analytics

  • BigQuery is the only data warehouse that handles huge amounts of data.

  • BigQuery gives you real-time feedback that could thwart potential business problems.

  • With BigQuery, you can avoid data silo complications that arise when you have individual teams within your company that have their own data marts. 


BigQuery Cons


  • It falls short when used for constantly changing information.

  • It only works on Google Cloud.

  • It can become costly as data storage and query costs accumulate. PCMag suggests you go for flat pricing to reduce costs.

  • You need to know SQL and its particular technical habits to use BigQuery.

  • BigQuery ML can only be used in the US, Asia, and Europe.


When should you use BigQuery?


BigQuery is best used ad-hoc for massive amounts of data, run for longer than five seconds, that you want analyzed in real time. The more complex the query, the more you'll benefit from BigQuery. At the same time, don't expect the tool to be used as a regular relational database or for CRUD, i.e., to Create, Read, Update, and Delete data.


BigQuery Costs


Multiple costs come with using BigQuery. Here is a breakdown of what you will pay for when you use it:

  • Storage (based on how much data you store): There are two storage rates: active storage ($0.020 per GB), or long-term storage ($0.010 per GB). With both, the first ten GB are free each month.

  • Processing queries: Query costs are either on-demand (i.e., by the amount of data processed per query), or flat-rate.

BigQuery also charges for certain other operations, such as streaming results and use of the BigQuery Storage API. Loading and exporting data is free.

For details, see Data ingestion pricing. This Coupler Guide to BigQuery Cost is also extremely helpful.




With BigQuery, you can assign read or write permissions to specific users, groups or projects, collaborating across teams, and it is thoroughly secure, since it automatically encrypts at-rest and transit data.

If you're a data scientist or web developer running ML or data mining operations, BigQuery may be your best solution for those spiky, massive workloads. It is also useful for anyone handling bloated data batches, within reason. Be wary of those costs. 

Have you ever used BigQuery? How do you use it? Reach out and tell us about your experience!


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