BigQuery vs Snowflake - Data Warehouse Comparison

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Krystian Dziubinski

Dec 31, 2021 • 9 min read
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Snowflake and BigQuery are already well-known, modern cloud data warehouse solutions to those who think seriously about big data.

Both solutions present extreme performance, flexibility, and great usability. But, when it comes to the battle of Snowflake vs BigQuery, how do you choose the right data warehouse for a specific project?

In terms of Snowflake vs BigQuery, whether it's batching or streaming, time-series or cross-sectional data, megabytes or petabytes in size, both data warehouses work well to serve even the most complex data analytics, reporting, or prediction data use cases.

Even though these major data warehouse players implement similar principles, there are a few high-level differences worth mentioning, before the final Snowflake vs BigQuery decision is made. The dissimilarities mostly come down to compatibility, pricing and usability.

Although they may seem minor at first glance, for some businesses even the most subtle variance may play a crucial role. Customers should pay most attention to these, because both data warehouses work well in other aspects.

What is a data warehouse?

Data warehouse is a centralized data repository of information that are used for reporting, analysis, and making more informed decisions. Data regularly flows into the warehouse from operational systems, transactional systems, relational databases, and external data sources.

To stay competitive, data and analytics are crucial. Data warehouses store data efficiently and deliver results to users quickly, so they are the ultimate tools for business analysts, data engineers and data scientists, using business intelligence (BI) tools and SQL clients.

Data lake vs data warehouse

Data lakes are highly scalable storage repositories that complement data warehouses. Composed of structured, semi-structured data, and unstructured data formats from different sources, data lakes hold large volumes of raw data in native format until needed for use. Data is stored with a flat architecture and is queried as required.

If your organization needs to collect and store a lot of data, but doesn’t need to process and analyze it all straight away, a data lake is the way to go.

By contrast, data warehouses such as BigQuery and Snowflake process data for advanced querying and analytics. Generally, companies use a combination of a database, data lake, and data warehouse to store and analyze data.

However, any data warehouse solution cannot be used to substitute a relational database, as they are specialized in running analytical queries, not simple CRUD operations and queries.

BigQuery vs Snowflake comparison

Before we compare BigQuery and Snowflake, let’s take a brief look at what each solution offers.

What is Snowflake?

Snowflake is a cloud-based data warehousing solution launched in October 2014. This data warehouse consists of three main components:

  • Database storage
  • Query processing
  • Cloud services

The fully-managed Software-as-a-Service (SaaS) architecture is flexible, and can run on any of the popular cloud providers, including AWS, Azure, and Google Cloud Platform (GCP).

The solution decouples storage and compute functions, allowing clients to use and pay for them separately. With no hardware or software to select, install, configure, or manage, Snowflake users don’t have to dedicate manpower and money to set up, maintain, and support in-house servers. Moreover, it’s simple to move data into Snowflake using an extract, transform, and load (ETL) solution.

What is BigQuery?

BigQuery is a petabyte scale, cloud-based data data warehouse launched in May, 2010 and is integrated into the Google Cloud Platform.

Under the hood BigQuery, is the implementation of many different services Google worked on over the years to serve their vast and complex data centers. It’s a combination of Borg (compute), Colossus (distributed storage), Jupiter (the network), and Dremel (execution engine).

The fully-managed and serverless architecture helps customers manage and analyze data at scale via built-in features such as machine learning, business intelligence, and geospatial analysis.

How to choose the right data warehouse?

Choosing the best data warehouse for your needs and project is key. In terms of Snowflake vs BigQuery, there are a host of advantages and disadvantages to each, from high accessibility and design on the pros side, to cost considerations in the list of cons.

The main differences between both data warehouse solutions are:

  • Integration and Performance (speed, reliability)
  • Ecosystem
  • Database features
  • Maintenance
  • Security
  • Costs

Both services are well-designed and work very well with a huge variety of projects.

In general, BigQuery is easier to start with for small companies, because it’s hugely simple to set up and there’s lots of public data available from the start. Moreover, BigQuery machine learning (ML) makes predictions and simple data science discoveries even easier for teams who are comfortable using SQL syntax.

Additionally, companies using other Google products find integration straightforward. BigQuery also has a range of tools and optimizations for huge enterprises to operate their big data needs.

Meanwhile, Snowflake is great for those who want to avoid vendor lock-in and keep their data separate from big cloud providers. Snowflake simplifies processes, making it easy to implement, and you only pay for what you use. Therefore, small companies won’t have any issues with Snowflake.

Huge enterprise-grade projects are also well-catered for, because Snowflake’s performance is exceptional. Moreover, given the options to control and customize compute costs and performance, it’s possible to optimize overall costs.

Pros of Snowflake

By answering three key questions, the pros of Snowflake are clear:

Key questions Advantages
How can it help your business? Support for different cloud providers (high accessibility)
What problems can you avoid?
  • Well-designed data manipulation services (ELT)
  • Easy start point for virtual data warehousing (you need to select cloud provider, region and you can build solution without specific knowledge of setting up the cloud from a specific provider)
How can it help your users?
  • Great dummy dataset organized with various sizes and structures
  • Very clear and well-designed query profiler
  • Raw data can be stored in S3 and accessed through external tables

Cons of Snowflake

What are the negative aspects of Snowflake? Here are some of them:

  • Users must be careful with time travel options, because these can build up costs very easily.
  • Users must set and choose a virtual warehouse before doing most queries. Only metadata queries are excluded from that obligation.
  • Minimum time interval available for scheduling tasks is one minute.

Pros of BigQuery

By posing the same three questions as above, BigQuery’s advantages are evident:

Key questions Advantages
How can it help your business?
  • Support for AWS s3 storage and Google drive and Google marketing services
  • Lots of free data sets
  • BigQuery machine learning
What problems can you avoid?
  • No virtual warehouse management required
  • Sandbox account
  • BigQuery supports low latency streaming
How can it help your users? Query validation and consumption estimation prior to execution

Cons of BigQuery

However, as all tools, BigQuery is not free of some disadvantages, like:

  • Limitations regarding data export
  • Lack of compute customization for query processing to optimize costs
  • Extra costs for data transfer services for scheduling queries
  • Minimum time interval available for scheduling tasks is 15 minutes.

Snowflake vs BigQuery

Virtual data warehousing is the future. In a nutshell, both data warehouse solutions have a lot in common. The differences between them can be a deciding factor for business.

We believe that Snowflake it's a very promising toolset but regardless of your choice between BigQuery and Snowflake, our team can support your business with a dedicated solution based on any of the two data warehouses and other data science services.

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Krystian Dziubinski

Krystian Dziubinski works as a Senior Data Engineer at Netguru.
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