Snowflake vs BigQuery: Which Cloud Data Warehouse is Right in 2022

Since the 1980s, the notion of a data warehouse has developed and evolved considerably. Data warehousing has become a unique field due to the corporate world’s rising problems and complexities. It has resulted in improved technology and more stringent data processing.

Data warehouses were started with the initial goal of allowing businesses to keep an analytical data source on hand to answer inquiries and find the right customer insights. However, it has grown into more straightforward access to information about the company data.

Furthermore, the specified end-user has shifted from specialized coders to everyone who can use the drag and drop interface of Power BI or Tableau.

Data warehouses store all types of data from several external and internal sources. In addition, they gather raw data that is analyzed to provide you with rapid answers to organizational questions to make intelligent predictions and financial decisions for extensive growth.

Data warehouses reduce your analytic processing effort by collecting data from all elements of your firm, from technical to marketing, sales, and HR.

Google’s BigQuery and Snowflake are two famous data warehouses. However, suppose your organization plans to invest in a data warehouse or upgrade from the present data warehouse. In that case, you want to identify the finest and most cost-effective solution for your requirements. Then here, we will see a detailed comparison between BigQuery and Snowflake.

What is Snowflake?

Snowflake is a warehousing solution built on Software-as-a-Service (SaaS) that may be operated on any prominent cloud providers, GCP, AWS, and Azure. However, it was designed specifically for the cloud and had a few critical components apart from other cloud data warehouses.

Snowflake was initially released in 2014 and has become a prominent player in the data warehousing business, with a market capitalization of around 40 billion dollars (June 2022).

It has practically no managerial or operational overhead. Instead, Snowflake provides the backend infrastructure as a native SaaS service, allowing you to focus on what matters, gaining insights from your data instead of worrying about the infrastructure.

Advantages of Snowflake

Isolated working environments

Customers may segregate workloads throughout the organization and allow various departments and apps to use Snowflake. As a result, the platform can serve data analysts, managers, data scientists, and programmers in a single platform.

Architecture

Snowflake is a fully managed, highly secured, and ANSI SQL-based data warehouse, and it is a good starting point for organizations looking to migrate data warehouses to the cloud. In addition, it is highly compatible with multi-statement transactions and sophisticated joins. 

Scalability

In Snowflake, query concurrency is virtually limitless. You may scale up as needed, but when that demand decreases, It will automatically scale down to save the cost. Also, everyone can access the required data at the same time.

Data Type Support

You can query on semi-structured data with high performance. Also, Snowflake gives quick access to ORC, JSON, Parquet, and AVRO data, allowing for a more comprehensive picture of your business and consumers, allowing for deeper, more illuminating insights.

What is BigQuery?

Google BigQuery is a serverless cloud-based Platform as a Service(PaaS) data warehouse offering that can be used as an ETL solution. In addition, users may obtain analytical insights from built-in machine learning capabilities.

BigQuery allows data queries utilizing ANSI SQL. In addition, Google BigQuery distributes computing resources automatically. Thus you don’t need to provision instances or Virtual Machines.

Developers can focus on more essential tasks with BigQuery, such as creating queries to evaluate business-critical data instead of infrastructure management.

REST API in BigQuery makes the development of mobile front end and app engine dashboards easy for organizations. Companies may then genuinely harness the value of this data and enable all organizational stakeholders to gain insights from it for extensive growth. BigQuery only runs on Google and does not run on any other cloud provider.

Advantages of BigQuery

Managed Storage

BigQuery provides organized, durable and scalable storage for Data Warehouse, allowing you to minimize data operations drastically. Tables are storage optimized and saved in columnar forms, with each encrypted and compressed.

As tables are duplicated across many data centers, storage is durable and secure. However, you also do need to worry about disaster recovery and data replication since your data is spread over various zones within a region.

Built-in ML Analytics

BigQuery offers excellent AI/ML capabilities and supports various analytical use cases using AutoML tables and BigQuery ML. All this is provided in a user-friendly and code-free graphical interface. AutoML Tables is a wholly automated functionality that finds the optimum ML model for the problem. BIgQuery ML is ideal for rapid development time challenges, such as Naive Bayes, K-means, Logistic Regression, etc. It supports AutoML tables model type in a SQL interface.

Data Ingestion

BigQuery accepts various data input formats such as JSON, CSV, Avro, and Parquet/ORC.

Snowflake vs BigQuery: Which One Should I Choose?

Architecture

Snowflake is a serverless data warehouse platform that separates computing and storage and works on ANSI SQL. Its design combines classic shared-nothing and shared-disk architectures, and it uses a single repository for persistent data to make data available to all computing nodes on the platform.

Snowflake processes all of your queries using Massively Parallel Processing MPP. This implies that each computing cluster stores a local subset of the complete data collection. Snowflake arranges your data into discrete micro-segments for storage, then internally optimized and compressed for columnar storage.

In reality, all data imported into Snowflake is rearranged, streamlined, and compressed into a columnar form before being stored in the cloud. Snowflake automatically manages all data storage elements, including structure, size, statistic, compression, and metadata that are only available via SQL queries and are not directly visible.

In Snowflake, the warehouse is an MPP made of many nodes. These virtual warehouses or clusters of computational resources are used to process data. 

Google BigQuery is likewise serverless, with storage and computing separated. It’s also built on ANSI SQL. Its architecture, however, is considerably distinct. BigQuery uses various multi-tenant services powered by Google infrastructure technologies such as Borg, Dremel, Jupit Colossus, and Borg.

Dremel is used for computing by BigQuery, which provides the grunt work by converting SQL queries into execution trees. In BigQuery, tree leaves are referred to as “slots.” They read data from storage and perform required computation. The tree’s branches handle all aggregations known as “mixers.” A single person on your team can access hundreds of slots to run queries as needed.

BigQuery, like Snowflake, compresses data into a columnar form for storage in Colossus, Google’s global storage system. Colossus provides data recovery, distributed management, and replication, ensuring no failure.

BigQuery leverages Google’s Jupiter network to transfer data from one place to another swiftly. Finally, BigQuery handles all hardware resource management with Borg, Google’s forerunner to Kubernetes.

Scalability

Snowflake includes an auto-scaling and auto-suspend functionality that allows clusters to stop and restart during busy and idle periods. In addition, you can resize clusters but can not resize nodes in Snowflake.

Also, It can be scaled up to 10 warehouses with a maximum of 20 DML/queue in a single table. BigQuery handles everything behind the scenes, automatically provisioning more compute resources as per the requirements.

BigQuery has a default limit of 100 concurrent users. Both solutions allow you to scale up and down based on your requirements. Furthermore, Snowflake can isolate workloads across organizations in multiple warehouses, allowing different teams to function independently with minimal concurrency concerns.

Security

Snowflake automatically encrypts data in transit and at rest. You do not get granular permissions for columns, but you can access views, schemas, tables, and procedures. BigQuery, on the other hand, gives column-level security and permissions on datasets, specific views, and tables.

As BigQuery is an offering of Google Cloud Platform, you can use the other built-in security and networking features and services. Both Snowflake and BigQuery are PCI DSS, HIPAA, and ISO 27001 compliant. In addition, Snowflake offers two data protection features – Fail-safe and Time Travel.

Time Travel saves a state of your data before it is modified. Time Travel has an average retention time of one day, and Enterprise customers can make it to 90 days. Also, it can recover past data using Fail-safe. This is a fixed period that begins after the time travel retention period ends.

Although you must request that Snowflake commence the recovery, this capability allows Snowflake to get back any data that may have been destroyed or lost due to human errors or operational failures. In addition, Google BigQuery preserves a seven-day history of any updates to its tables. BigQuery also has a table snapshots tool that allows you to keep table data for more than seven days.

Maintenance

Snowflake does not need setup and maintenance of computing and storage because they are separated and managed by the Cloud provider. Still, it does require selecting a Cloud service provider(Snowflake operates on prominent cloud providers like GCP, AWS, and Azure).

Maintenance will be minimal because practically everything is managed by the cloud provider, including an automated and speedy supply of computer resources.

Pricing

Because Snowflake’s pricing approach is based on the individual warehouse, the cost is heavily influenced by your overall consumption. Snowflake offers different warehouse sizes with varying prices and server/cluster counts.

Snowflake Standard Edition costs around one credit per hour for an X-Small Snowflake warehouse. One credit per hour can also be calculated. It is vital to remember that credit fees differ based on the company tier inside Snowflake. The price per credit for Snowflake Standard is $2.

Furthermore, with an increase in warehouse size, credit consumption inside Snowflake increases. BigQuery costs are based on the number of bytes read. Therefore, BigQuery provides both standard and on-demand pricing.

The first one TB of data processing per month is free, and later with on-demand pricing, you pay $5 per TB for the number of bytes handled in a particular query.

You buy Virtual CPUs or dedicated resources to perform your queries with a BigQuery standard or flat-rate pricing approach. For 100 slots, the monthly cost is roughly $2,000.

You can reduce it to $1,700 with annual pricing. For example, Snowflake charges a monthly fee of $23 and $40 per TB for upfront and on-demand users. BigQuery, on the other hand, charges $10 and $20 per TB of inactive and active storage.

Snowflake vs BigQuery: Complete Comparison Overview

	Snowflake	BigQuery
Start Date	2014	2010
Owner	Snowflake	Google
Supported Cloud Infrastructure	AWS, Azure, Google Cloud Platform	Google Cloud Platform only
Type of Services	Software-as-a-Service (SaaS)	Platform as a Service(PaaS)
Storage Format	Columnar micro-partitioned & compressed storage	Columnar & compressed storage (code-named “Capacitor”)
Data Sharing Feature	Yes	No
Security	Snowflake offers granular permissions for tables, schemas, views, procedures, and other objects, but not individual columns.	BigQuery only offers permissions on datasets, not individual tables, views, or columns.
Warm Cache (SSD)	Yes, at micro–the partition level granularity	No
Data Protection	Snowflake has two facilities for data protection: Time Travel and Fail-safe.	BigQuery maintains a complete seven-day history of changes against its tables.

Wrapping Up

Snowflake is best suited for businesses wishing to cut expenses by using the cloud-based data warehouse with practically limitless, automated scaling and respectable performance levels.

On the other hand, Google BigQuery is best suited for enterprises with diverse workloads since it lets you choose how you want to query your data. It is also appropriate for data mining operations.