Three leading data warehouse solutions often invite comparison: Snowflake vs Redshift vs BigQuery.
All three offer enhanced scalability, lower initial costs, and superior performance.
Each solution has a similar set of features and benefits—and some crucial differences.
This guide compares the big three solutions to help you determine which meets your needs best.
We examine the differences in scalability, security, cost, and other considerations that determine the optimal data warehousing solution for your use case.
Snowflake vs Redshift vs BigQuery: Scalability
A study by IDC reported that the world produced 64 zettabytes of data in 2020—nearly double the 33 zettabytes generated in 2018. The report’s authors predict 175 zettabytes of global data by 2025.
If you expect the same rate of growth for your data, you need to consider the ability of your data warehouse solution to address that expansion.
Your data warehouse will also need to scale to accommodate more users and concurrent users, as well as more complex analytics.
On top of that, the data warehouse must deliver both faster load times and quicker response times simultaneously.
When it comes to handling these scalability challenges, each of the three leading solutions has significant differences.
Snowflake has three separate layers: Cloud Services, Compute, and Data Storage. The layers run separately but are accessible to each other. Each layer can scale up or down independently, according to your changing performance and cost requirements.
Therefore, Snowflake gives you room for seamless, automatic scalability, both vertically and horizontally. In addition, it has a multi-cluster shared data architecture that doesn’t need input from database operators. If your business has limited resources, Snowflake might be a good fit.
Amazon Redshift lets you start with a few hundred gigabytes of data and scale to a petabyte or more. It decoupled its storage and compute layers, so they can be scaled independently.
Redshift gives you two options, depending on your needs. Redshift Serverless allows you to create a data warehouse that scales seamlessly and automatically to handle unexpected spikes and drops in demand. If you expect more predictable workloads, Amazon offers Redshift Provisioned Cluster, which gives you greater control over your Redshift cluster configurations.
BigQuery is billed as hyper-scalable, and given it’s a Google product, that probably isn’t an exaggeration. It can execute SQL queries over petabytes of data and automatically scales according to your current demand.
BigQuery runs on a serverless cloud architecture, eliminating the need to provision hardware, re-configure clusters, or tune performance. It can handle 10 trillion row queries.
Google built machine language tools on top of BigQuery. As a result, BigQuery ML and Cloud Auto ML can take full advantage of BigQuery’s hyper-scalability.
Snowflake vs Redshift vs BigQuery: Security
With the world moving toward cloud data warehousing, protecting and securing data is critical. As you might expect, each of the three solutions takes data security very seriously.
Snowflake secures your data in three ways: data encryption, row-level security (RLS), and column-level security (CLS) for data masking.
It supports end-to-end data encryption, so only authorized users can see your data. It uses a hierarchical key model with four levels of keys: root keys, account master keys, table master keys, and file keys. Master keys rotate every 30 days, and you can set up Snowflake to generate a new encryption key and re-encrypt your day annually.
Snowflake uses Views or Secure Views to provide row-level security. You may also control data security by assigning Access Privileged permission to specific users.
Snowflake also lets you mask columns that hold sensitive data (SSNs, bank account information, and so on). It supports data masking through dynamic data masking and external tokenization. You can apply masking policies based on user role or user group.
As part of Amazon Web Services (AWS), Amazon Redshift has shared security built in. However, Redshift users are responsible for managing their own security in some areas. For example, you’ll need to establish unique sign-in credentials, SSL connections, load data encryption, and more.
Amazon Redshift’s access control consists of cluster management, cluster connectivity, and database access.
Redshift cluster management allows you to create, configure, and delete infrastructure (i.e., Redshift clusters). AWS security credentials govern these operations and can be performed by AWS Identity and Access Management (IAM) users via the console or an API.
Redshift cluster connectivity refers to network access control. It uses security groups based on CIDR (Classless Inter-Domain Routing).
Redshift grants database access per secure object (database, table, column, or view), configured using the SQL GRANT and CREATE commands. AWS IAM users with specific connection strings can gain temporary access.
Google’s BigQuery provides two levels of data security: column-level and row-level.
BigQuery’s column-level security lets you control what data gets filtered and what columns users can access. Once you set up the access policies, BigQuery checks each query to see if the user has the proper access to read each column. This column-level access control is layered over the existing dataset access control. So to access data protected by column-level security, a user needs both dataset and policy tag permission.
Additionally, you can use BigQuery’s row-level security to filter data and limit access to specific rows in a table, depending on the user’s permissions. You can use policy tags to set access controls for projects, datasets, tables, and column-level security. BigQuery’s row-level security gives you fine-grained access control to specific subsets of data in BigQuery tables.
Redshift vs Snowflake vs BigQuery: Price
Pricing is the third critical factor in choosing between Snowflake, Redshift, and BigQuery. While we all want the best tools, we often have to settle for the best tool we can afford. But with data warehousing, pricing depends on your use case. In some cases, Redshift might be the cheapest option; in others, it may be Snowflake or BigQuery.
Snowflake offers four pricing tiers: Standard, Enterprise, Business Critical, and Virtual Private Snowflake (VPS). They also give you two options for data storage: On-Demand Storage (billed month-to-month) and Capacity Storage (paid up front). Pricing also depends on the platform you use (AWS, Azure, or Google Cloud) and the region of the world you’re in.
Essentially, Snowflake charges for data storage, compute, and cloud services. Or, to look at it another way, you pay for the parts of the Snowflake architecture you use: data storage, compute (virtual warehouses), and cloud services like data transfer and serverless capabilities.
To see the latest Snowflake pricing, use their pricing calculator.
Several factors also influence Redshift pricing, depending on your needs. Like Snowflake, Redshift offers an on-demand plan for compute and storage. Alternatively, you can sign up for a one- or three-year subscription.
Knowing which plan is best requires you to have a good idea of what your Redshift usage will be. Redshift offers a two-month free trial that can give you a better idea of your needs.
You can also use Redshift’s pricing calculator to ballpark your costs.
BigQuery also breaks pricing down into compute and storage, although it refers to compute as analysis. They define analysis pricing as “the cost to process queries, including SQL queries, user-defined functions, scripts, and certain data manipulation language (DML) and data definition language (DDL) statements that scan tables.”
BigQuery also charges services like BigQuery Omni, BigQuery ML, BI Engine, and streaming reads and writes.
Rather than a free trial, BigQuery offers free operations and usage tiers. Also long as you stay below the stated limits, you don’t pay.
BigQuery vs Redshift vs Snowflake: Other Considerations
Scalability, security, and price aren’t the only factors to consider when deciding between these three data warehousing solutions. You also need to think about what kind of work you need your data warehouse to do and which solution best suits that work.
Snowflake is best for flexible scalability. It’s excellent for steady and continuous use that requires consistent upscaling and downscaling. If you’re a BI company actively querying vast chunks of data for analysis, Snowflake is a good bet. It’s also great for data-as-a-service (DaaS) companies that grant data access to thousands of clients for UI analysis and data APIs.
Amazon Redshift works well if you require constant computing, such as live dashboards for continuous data streaming and querying that occurs through refreshing. It’s also a strong solution for real-time or close to real-time reporting, such as automated ad-bidding networks, NASDAQ daily reporting, and similar services.
If your business needs involve spiking workloads with high idle times, BigQuery may be your best bet. BigQuery can easily handle machine learning, ad-hoc reporting of complex queries, sales intelligence solutions for data analysis, recommendation models for eCommerce applications, and similar solutions.
Other Data Warehouse Solutions
Snowflake, Amazon Redshift, and Google BigQuery may be the leading data warehouse solutions, but they aren’t the only game in town. If neither of these systems meets your needs, there are additional competitors.
Databricks is a data lakehouse solution that offers robust ELT, data science, and machine learning features, for example.
Azure Synapse Analytics is Microsoft’s data warehouse solution, and IBM offers Db2 Warehouse.
Firebolt is a favorite of data engineers and data analysts because its primary focus is speed, and its order-of-magnitude performance sets it apart from the competition.
Finding the right data warehouse solution is a complex process, but the benefits of making the best choice are well worth the effort.
If you find you need a little help finding or setting up your data warehouse solution, contact us. We may be able to help.