Google BigQuery, which is free for 10 gigabytes (GB) per month, is the search giant's ginormous, petabyte (PB)-scale data warehouse for analytics.
It's an enterprise-level, SQL product, and Big Data is in Google's DNA.
All of the company's tools and services are proof of that.
In short, if you want to do anything with data, then you can bet Google has a tool to make it happen.
If you have massive data sets or you're bulking up your data by blending it with public or commercial data sets, then Google BigQuery may be a solid choice.
It is designed to scan terabytes (TBs) in seconds and PBs in minutes.
The largest query to date is 2.1 PBs and Google BigQuery handled it without any issues.
Despite these capabilities, Big Data analytics is challenging and, if you are working with smaller data sets, then it may be overkill.
Still, Google BigQuery is a solid choice that trails just behind Microsoft Azure SQL Database and MongoDB Atlas, the Editors' Choices picks in our DBaaS solutions review roundup.
Pricing Model
Google BigQuery is a serverless data analytics model.
The separation of storage and compute gives you better pricing controls, which tend to be of more interest to people running exceptionally large projects.
Storage is priced at flat rates and compute on usage rates.
The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that.
For example, if you store 1 terabyte (TB) for a month, then the cost would be $20.
Streaming data inserts start at 1 cent per 200 megabytes (MBs).
The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter.
Meta data operations are free.
You also have the option of paying as you go or a monthly flat fee.
Some developers prefer the flat fee for budget anxiety relief.
Since storage is at a flat fee already, this option just means that compute is also on a flat, monthly fee arrangement.
But before you get too excited about signing up for flat-rate pricing, be aware that only accounts with $40,000+ in monthly analytics spend qualify for this option.
Google BigQuery's free tier provides up to 1TB of data analyzed each month and 10GB of data storage, but seriously, if you're well below that mark, then there are other tools better suited to the task, such as Microsoft Azure SQL Database, IBM Db2 on Cloud, or Google Cloud with Google Analytics 360.
Step by Step
You'll need a Google account so set one up if you don't have one already.
You'll need it to register for a Google Cloud Platform account, which will also require a credit card to use the free trial.
But don't worry as you won't be automatically upgraded and billed at the end of the trial period.
You have to manually upgrade for anything to be charged to your credit card.
From the Google Cloud user interface (UI), go to BigQuery.
BigQuery's UI is a bit plain-Jane, but its concision makes it easy to use, too.
Google tells me that it is working on a new UI now.
With the current UI, if you just want to explore, then click on Compose Query, and choose one of the public data sets on the welcome page.
Write a standard SQL query in the query box by using either Query Editor or User-Defined Function (UDF) Editor, and off you go.
The Quickstart guides are useful in transferring data or spinning up a database of your own in Cloud Bigtable, Cloud Spanner, Cloud SQL, or Cloud Datastore (NoSQL database).
BigQuery uses American National Standards Institute (ANSI)-compliant SQL as well as Open Database Connectivity (ODBC) and Java Database Connectivity (JDBC) drivers to integrate with data in other Cloud products and additional types of applications.
Unique SQL implementations designed to smooth querying means there are several SQL dialects, which can be confusing.
I did notice that while the default is "Legacy SQL," I could uncheck the SQL dialect box to revert to true standard SQL.
Google BigQuery also has a streaming ingestion engine for real-time data capture and analysis.
Use the Create Data Set tab under the My First Project pull-down menu to create a data set.
Enter the Data set ID, choose the data location (US, European Union, or Asia-Northeast), and set the data expiration.
Google BigQuery can automatically detect schema.
Once the data set is set up, you're ready to run queries.
There are connectors to most business intelligence (BI) tools.
But you might want to use Data Studio, which is Google's BI visualization tool, and it's free.
The list of Google tools you can use is lengthy.
I recommend you start with reviewing the list of Google Cloud Platform free tiers.
Google Cloud Platform has 15 regions, 45 zones, over 100 points of presence, and a well-provisioned global network with 100,000+ miles of fiber-optic cable.
You get better pricing using the global service, but you are free to specify regions as you wish.
Backups and service-level agreements (SLAs) come under the auspices of Google SQL Cloud.
The full SLA is here.
Cloud SQL keeps seven automated backups for each instance.
First-generation (gen) backups capture everything and are included in your instance costs (on the per usage model).
Their storage space does not count against your allotted storage space.
Second-gen backups captured only the data that has changed and their storage is charged at a reduced rate.
Overall, Google BigQuery is brilliantly designed.
It is better suited for huge data sets and those who are skilled in working with them.
If you're into writing machine learning (ML) apps or designing ML training data, then you're especially going to love this product.
The same is true of developers working on Internet of Things (IoT) apps or any development requiring flexible data ingestion and massive data analysis.
Cons
Built for Big Data so it's overkill for small data sets.
Confusing SQL dialects.
Unwieldy costs without proper attention to tool use and automated scaling.
Flat rate pricing works better.
View More
The Bottom Line
Google BigQuery is a great Database-as-a-Service (DBaaS) solution for cloud native companies and anyone working with machine learning application development or handling massive sets.
Google BigQuery, which is free for 10 gigabytes (GB) per month, is the search giant's ginormous, petabyte (PB)-scale data warehouse for analytics.
It's an enterprise-level, SQL product, and Big Data is in Google's DNA.
All of the company's tools and services are proof of that.
In short, if you want to do anything with data, then you can bet Google has a tool to make it happen.
If you have massive data sets or you're bulking up your data by blending it with public or commercial data sets, then Google BigQuery may be a solid choice.
It is designed to scan terabytes (TBs) in seconds and PBs in minutes.
The largest query to date is 2.1 PBs and Google BigQuery handled it without any issues.
Despite these capabilities, Big Data analytics is challenging and, if you are working with smaller data sets, then it may be overkill.
Still, Google BigQuery is a solid choice that trails just behind Microsoft Azure SQL Database and MongoDB Atlas, the Editors' Choices picks in our DBaaS solutions review roundup.
Pricing Model
Google BigQuery is a serverless data analytics model.
The separation of storage and compute gives you better pricing controls, which tend to be of more interest to people running exceptionally large projects.
Storage is priced at flat rates and compute on usage rates.
The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that.
For example, if you store 1 terabyte (TB) for a month, then the cost would be $20.
Streaming data inserts start at 1 cent per 200 megabytes (MBs).
The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter.
Meta data operations are free.
You also have the option of paying as you go or a monthly flat fee.
Some developers prefer the flat fee for budget anxiety relief.
Since storage is at a flat fee already, this option just means that compute is also on a flat, monthly fee arrangement.
But before you get too excited about signing up for flat-rate pricing, be aware that only accounts with $40,000+ in monthly analytics spend qualify for this option.
Google BigQuery's free tier provides up to 1TB of data analyzed each month and 10GB of data storage, but seriously, if you're well below that mark, then there are other tools better suited to the task, such as Microsoft Azure SQL Database, IBM Db2 on Cloud, or Google Cloud with Google Analytics 360.
Step by Step
You'll need a Google account so set one up if you don't have one already.
You'll need it to register for a Google Cloud Platform account, which will also require a credit card to use the free trial.
But don't worry as you won't be automatically upgraded and billed at the end of the trial period.
You have to manually upgrade for anything to be charged to your credit card.
From the Google Cloud user interface (UI), go to BigQuery.
BigQuery's UI is a bit plain-Jane, but its concision makes it easy to use, too.
Google tells me that it is working on a new UI now.
With the current UI, if you just want to explore, then click on Compose Query, and choose one of the public data sets on the welcome page.
Write a standard SQL query in the query box by using either Query Editor or User-Defined Function (UDF) Editor, and off you go.
The Quickstart guides are useful in transferring data or spinning up a database of your own in Cloud Bigtable, Cloud Spanner, Cloud SQL, or Cloud Datastore (NoSQL database).
BigQuery uses American National Standards Institute (ANSI)-compliant SQL as well as Open Database Connectivity (ODBC) and Java Database Connectivity (JDBC) drivers to integrate with data in other Cloud products and additional types of applications.
Unique SQL implementations designed to smooth querying means there are several SQL dialects, which can be confusing.
I did notice that while the default is "Legacy SQL," I could uncheck the SQL dialect box to revert to true standard SQL.
Google BigQuery also has a streaming ingestion engine for real-time data capture and analysis.
Use the Create Data Set tab under the My First Project pull-down menu to create a data set.
Enter the Data set ID, choose the data location (US, European Union, or Asia-Northeast), and set the data expiration.
Google BigQuery can automatically detect schema.
Once the data set is set up, you're ready to run queries.
There are connectors to most business intelligence (BI) tools.
But you might want to use Data Studio, which is Google's BI visualization tool, and it's free.
The list of Google tools you can use is lengthy.
I recommend you start with reviewing the list of Google Cloud Platform free tiers.
Google Cloud Platform has 15 regions, 45 zones, over 100 points of presence, and a well-provisioned global network with 100,000+ miles of fiber-optic cable.
You get better pricing using the global service, but you are free to specify regions as you wish.
Backups and service-level agreements (SLAs) come under the auspices of Google SQL Cloud.
The full SLA is here.
Cloud SQL keeps seven automated backups for each instance.
First-generation (gen) backups capture everything and are included in your instance costs (on the per usage model).
Their storage space does not count against your allotted storage space.
Second-gen backups captured only the data that has changed and their storage is charged at a reduced rate.
Overall, Google BigQuery is brilliantly designed.
It is better suited for huge data sets and those who are skilled in working with them.
If you're into writing machine learning (ML) apps or designing ML training data, then you're especially going to love this product.
The same is true of developers working on Internet of Things (IoT) apps or any development requiring flexible data ingestion and massive data analysis.
Cons
Built for Big Data so it's overkill for small data sets.
Confusing SQL dialects.
Unwieldy costs without proper attention to tool use and automated scaling.
Flat rate pricing works better.
View More
The Bottom Line
Google BigQuery is a great Database-as-a-Service (DBaaS) solution for cloud native companies and anyone working with machine learning application development or handling massive sets.
