Next generation cloud data warehousing technologies like Google BigQuery, Snowflake and Firebolt are becoming more and more popular due to the increasing need for on-demand data analysis, on top of the ever-growing amounts of data being collected, generated and stored. 

Being data-driven and digital transformations in general have also become the new norm and strategy for many companies using it to gain competitive advantage – and for good reasons. New technologies such as Machine Learning, AI, Internet of Things (IoT), on-demand advanced analytics and robotics are on the rise, and data plays an integral role in enabling these technologies.

Because of this, much more on-demand computing power, performance and cost-efficient storage that scales with your business is needed. This is the main reason why more and more companies are shifting towards modern cloud data warehouses like Google BigQuery over more traditional static solutions like PostgreSQL.

BigQuery, Snowflake and Firebolt have separate storage and compute, which means it scales much better when querying vast amounts of data.

In the following article, we lay out the main reasons why we believe choosing BigQuery over PostgreSQL as the main data warehouse for your business is the best option. We cover topics such as performance, ease of use, scalability, cost efficiency as well as some customer stories.

Get The Ultimate Guide to the Modern Data Stack
Find everything you need to know about how to extract, store, model, and activate data in our Ultimate Guide to the Modern Data Stack →

Main differences

Lets first look at how developers in general define BigQuery and PostgreSQL: 

  • "Developers describe Google BigQuery as "Analyze terabytes of data in seconds". Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access – access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python".
  • PostgreSQL is described as "A powerful, open source object-relational database system". PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Now that we understand how each of these warehouses are described in technical terms, let's look into some of the main differences between Google BigQuery and PostgreSQL.

Better performance

The main reason for choosing Google BigQuery over PostgreSQL is performance. Google BigQuery is 100% elastic, meaning that it allocates the necessary resources required on-demand to run your queries in seconds and is highly optimized for query performance. The downside of on-premise or static solutions is that you are essentially “stuck” with the resources allocated to your database server and therefore need server engineers to manage and allocate resources. Even if doing so efficiently you will never reach the amount of computing power that Google BigQuery has since it is essentially unlimited.

Ease of use and scalability

BigQuery is “serverless” or “data warehouse as a service” which gives you low upfront cost, and improved scalability. It scales 1:1 with your needs and you only pay for what you use. It's super easy to set up and has many native integrations and functionalities.

Cost efficient

BigQuery is a cloud-based fully-managed service which means there is no operational overhead and provides extremely high cost effectiveness. The pricing model is also easy to understand. 

  • Compute: You pay $5 per terabyte scanned. First terabyte is free. 
  • Storage: You pay $20 per terabyte per month. First 10 gigabytes of storage is free.

Customer stories

We have seen the same pattern emerging from several customer cases and in our previous work experience. Typically you see a cloud hosted or on premise PostgreSQL solution with a data-read replica connected directly to a BI tool. This architecture can work for smaller data volumes and simple queries. 

However we found that on most use-cases and over time as the volume increases and queries become ever more complex this solution results in inadequate performance and even time out. This makes the classic solutions like PostgreSQL unsuitable for modern data warehousing  purposes.


If you want to make sure you are prepared for the future we advise you to make the shift towards cloud data warehousing as a service and modern technologies like Google BigQuery, Snowflake or Firebolt. 

While PostgreSQL is loved by many and definitely has its place as one of the best advanced classic data warehouse solutions, the two are completely different technologies that have their strengths and weaknesses and are made for different purposes. 

If you'd like help setting up your data warehouse, building your data models, or anything in between, schedule a call with one of Weld's data experts today.