‍BigQuery vs Snowflake- The Ultimate Guide

If you're wondering which data warehouse to choose between Google BigQuery and Snowflake, then you've come to the right place. Both are great choices for a cloud data warehouse, but there are some important differences to consider. Boltic can help you bring all your data into the data warehouse of your choice without having to write any code. So whether you're choosing BigQuery or Snowflake, we can make the process easy and painless. Let us go deep and understand each of these platforms in detail.

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What is Google BigQuery?

Google BigQuery is a cloud data warehouse that enables you to query data using SQL. It is a serverless platform that can scale to meet the needs of any organization. BigQuery is fully managed and there is no need to worry about provisioning or managing servers. BigQuery uses a columnar storage format which makes it highly efficient for analytical workloads. It also supports partitioning and clustering, which can further improve performance. BigQuery is integrated with many other Google Cloud Platform (GCP) products, making it easy to load data from sources such as Cloud Storage, Bigtable, and Datastore. BigQuery is a good choice for organizations that want to take advantage of the many integrations with GCP products, or for those who are already using other GCP services.

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Why use BigQuery?

Google BigQuery is a powerful tool that can help businesses unlock the value of their data. By using BigQuery, businesses can avoid having to invest precious engineering resources in setting up a centralized data store. Instead, they can focus on building queries to analyze business-critical data. Additionally, BigQuery's REST API enables businesses to easily build App Engine-based dashboards and mobile front-ends. This allows businesses to truly unleash the power of their data and empower all stakeholders to derive insights from it. There are many reasons why you might want to use BigQuery as your data warehouse.

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Here are some of the most important benefits:

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• BigQuery is serverless, so there is no need to provision or manage servers. This can save you a lot of time and money.

• BigQuery is highly scalable and can handle very large data sets.

• BigQuery uses a columnar storage format, which makes it very efficient for analytical workloads.

• BigQuery is integrated with many other GCP products, making it easy to load data from various sources.

• BigQuery is fully managed by Google, so you don't have to worry about software updates or maintenance.

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Advantages of Google BigQuery

1. Managed storage:

BigQuery offers a number of benefits for managing data, including durable and persistent storage, optimized columnar format, compression and encryption, and streaming ingestion. Additionally, BigQuery's data replication and disaster recovery features make it a reliable platform for storing data.

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2. BigQuery removes resource constraints:

Since BigQuery is a fully managed platform, there are no limits on storage or compute resources. This means that organizations can scale their data warehouse solution as needed without worrying about resource constraints.

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3. BigQuery integrates with other GCP products:

BigQuery is integrated with many other GCP products, making it easy to load data from sources such as Cloud Storage, Bigtable, and Datastore. Additionally, BigQuery can be used to query data stored in other GCP products, such as Bigtable and Datastore.

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4. BigQuery supports a wide variety of formats for data ingestion:

BigQuery supports multiple formats for data ingestion, including CSV, JSON, Avro, and cloud-native formats such as Parquet. This makes it easy to load data into BigQuery from a variety of sources.

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5. BigQuery can leverage nested and repeated fields for:

BigQuery has the capability to leverage nested and repeated fields, which can be useful for storing complex data structures. This makes BigQuery a flexible platform that can be used for a variety of workloads.

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What is Snowflake?

Snowflake is a cloud data warehouse that offers a unique combination of features that make it well-suited for modern data workloads. Snowflake is fully managed and offers a serverless architecture, meaning that there is no need to provision or manage servers. Snowflake provides the technology solution to build a scalable, highly resilient cloud environment with the agility your business demands while delivering valuable insights. 

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With Snowflake's unique architecture and the flexibility of the cloud, customers can use Snowflake across many use cases and workloads in their business. Initially starting out as a Data Warehouse, Snowflake has been able to manage more and more data types, and customers have started to use Snowflake as a SQL Data Lake.

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What Makes Snowflake Unique?

Snowflake is a true data warehouse as a service offering and is built for the cloud. This means that it offers a number of benefits over traditional data warehouses, including:

1. Architecture:

Snowflake's architecture is based on a shared data model, which means that data is stored and processed in a central location. This allows for easy scalability and improved performance. Snowflake's micro-partitioning feature enables the management of semi-structured and structured data, making it an ideal platform for handling JSON and Parque files. Snowflake's infinite scalability makes it a powerful tool for handling large amounts of data.

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2. Delivered as-a-service:

Snowflake's data warehousing service is delivered as-a-service, making it easy to use with near-zero management. Once your data is in Snowflake, they take care of the rest, including indexing and pruning. This allows customers to focus on the value within their data.

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Advantages of Snowflake

1. Architecture:

The architecture of Snowflake enables it to automatically scale up or down in response to changes in demand, without affecting performance. Its micro-partitioning feature allows it to natively manage semi-structured and structured data types such as JSON and Parquet, at an immense scale.

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2. Snowflake is a complete ANSI SQL database and data warehouse:

Snowflake is a complete ANSI SQL database, which means that it supports all of the features that are required for data warehousing workloads. Additionally, Snowflake supports standard SQL data types, making it easy to load data from a variety of sources.

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3. Snowflake is integrated with many cloud data platforms:

Snowflake integrates with many cloud data platforms, including AWS, Azure, and Google Cloud Storage. This makes it easy to load data into Snowflake from a variety of sources.

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4. Snowflake offers a number of features that make it well-suited for modern data workloads:

Snowflake's unique architecture and the flexibility of the cloud make it an ideal platform for modern data workloads. The serverless architecture of Snowflake means that there is no need to provision or manage servers, and the ability to scale up or down in response to changes in demand means that Snowflake can easily handle fluctuating workloads.

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5. Virtually unlimited query concurrency:

Snowflake's query concurrency feature enables it to handle a large number of concurrent queries without affecting performance. This makes Snowflake an ideal platform for workloads that require high levels of parallelism.

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BigQuery vs Snowflake: Factors that drive the decision

When it comes to big data, there are two major contenders: Google BigQuery and Snowflake. Both platforms have their pros and cons. When deciding on the right data warehouse for your business needs and objectives, it is important to consider the features of each option. Both Google BigQuery and Snowflake offer rich feature sets that can support a variety of workloads. In terms of performance, these two data warehouses are closely matched, as they both provide unlimited concurrency and complete elasticity.

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The key factors that will drive the decision between BigQuery and Snowflake are the specific requirements of your workloads, as well as your preferences in terms of pricing and deployment options.

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1. Scalability

When it comes to scalability, both Snowflake and BigQuery offer seamless, non-destructive scaling that can be done vertically and horizontally. However, Snowflake is preferred by companies with little resources since it harnesses all the Cloud tools on offer and doesn't require a database operator. On the other hand, BigQuery gives users the freedom to choose how to scale the processing and memory resources based on their needs. Therefore, BigQuery is more suited for companies who need real-time data execution and scalability up to petabytes of data.

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2. Ease of Use / Data Type Supported

When it comes to ease of use, Snowflake and Google BigQuery differ somewhat. Snowflake requires solid SQL and Data Warehouse knowledge but is still considered intuitive and simple to use. Google BigQuery is also user-friendly but requires common knowledge of SQL commands and ETL tools. Both platforms support JSON and XML data types.

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3. Security

Security is an important consideration when choosing a cloud-based data warehouse. Snowflake provides controlled access management and high-level data security and is compliant with most major data protection standards. Google BigQuery offers column-level security, creating security policies and encrypting all data in transit by default.  As part of the Google Cloud environment, BigQuery is compliant with major security standards such as HIPAA, FedRAMP, PCI DSS, ISO/IEC, SOC 1, 2, and 3.

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4. Architecture

Snowflake's architecture is a hybrid system that combines the best of both shared-disk and shared-nothing database architectures, while Google BigQuery's architecture is based on the Dremel massively parallel processing (MPP) architecture. Both systems are designed for the cloud, but Snowflake's centralized data repository gives it an edge when it comes to accessibility and flexibility. Google BigQuery's compute clusters are more powerful when it comes to processing speed, but its lack of a centralized data repository can be a disadvantage.

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5. Setup / Maintenance and Server Management

Snowflake is a cloud-based data warehouse that does not require the setup of storage and compute power. It is a serverless management system where almost everything is done for you automatically. Maintenance is minimal, and there is no need for sizing in setup. Google BigQuery is a serverless data warehouse that offers on-demand pricing and doesn't require setup or maintenance. 

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There is no need to provision or manage any servers. BigQuery uses a pay-per-use model, so you only pay for the queries that you run.  Snowflake is similar to BigQuery in this regard. Both systems are easy to use and don't require any expertise in server management. The main difference is that Snowflake offers a higher level of automation and is more hands-off than BigQuery.

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6. Backup and Recovery

While both BigQuery and Snowflake offer some form of data backup and recovery, Snowflake's fail-safe technology is designed to recover data that may be lost or damaged due to system failures within a 7-day period. On the other hand, Google BigQuery's services have data backup and disaster recovery mechanisms that allow users to query point-in-time snapshots from 7 days of data changes.

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7. Support for Third-Party Tools

When it comes to third-party support, Snowflake and BigQuery differ quite a bit. Snowflake has Snowsight, which can perform basic data visualizations. However, Google GCP has its own general access visualization/data modeling software called Data Studio. Additionally, in December 2020 GCP acquired Dataform, which is a Javascript-based data modeling solution that can compile queries in real time. This difference in third-party support may be a deciding factor for some organizations when choosing between these two platforms.

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8. Compute Layer

Snowflake is a proprietary computing engine that runs on commodity virtual machines, while BigQuery is powered by Borg, a cybernetic life-form that Google has imprisoned inside data centers around the world. Furthermore, Snowflake features intelligent predicate pushdown and smart caching, while BigQuery is a distributed computation engine. As such, these two platforms offer different strengths and weaknesses that should be considered when making a decision about which one to use.

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9. Mode of Operation / Performance

There are a few key ways in which Snowflake and BigQuery differ in terms of performance. Snowflake separates its compute power from its storage, which allows for concurrent workloads and faster overall performance. Google BigQuery supports the partitioning of storage and computing as separate operations, which also results in improved query performance. However, BigQuery is not as fast as Snowflake when it comes to large data sets. Snowflake is also able to handle more concurrent queries than BigQuery.

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10. Loading of Data

In general, both Snowflake and Google BigQuery support Extract Load Transform (ELT) and Extract Transform Load (ETL) Data Integration methods. However, Snowflake has the added ability to transform data during or after loading, while BigQuery relies on standard SQL dialect for transformation purposes. Additionally, BigQuery uses Data Streaming to load data row by row using Streaming APIs.

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11. Integrations

Snowflake provides native connectivity with various data integration, business intelligence (BI), and analytical tools. This means that users don't have to go through the hassle of connecting to different systems - everything can be done within Snowflake. On the other hand, BigQuery integrates with Google Workspace and Google Cloud Platform systems. While this does provide a wide array of data integration and BI tools, users may find it more difficult to connect to these different systems.

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12. Compute Layer

The working of Snowflake is inspired by other Hybrid Columnar systems like C-Store, and MonetDB. It is a Proprietary Computing Engine that uses virtual machines from AWS, GCP or Azure, depending on the cloud being used. On the other hand, Google BigQuery is powered by Borg which is basically a cybernetic life-form that has been imprisoned by Google inside data centers located all over the world. Both these systems are Distributed Computation Engines.

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13. Storage Layer

Both BigQuery and Snowflake use columnar storage to store data. Columnar storage is a type of data storage where data is stored in columns instead of rows. This type of storage is very efficient when it comes to storing data in a relational database. BigQuery uses a proprietary columnar storage format called Capacitor. Snowflake, on the other hand, uses a columnar storage format called Vectorwise. Both of these formats are efficient in terms of storage and query performance.

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14. Compression

When deciding between BigQuery and Snowflake, it is important to consider how each platform handles compression. Snowflake has its own compression layer that is transparent to users, meaning that users are not charged for bytes scanned. However, this also means that the query planner can use compression and table statistics to scan less data, reducing compute cost. On the other hand, BigQuery uses proprietary compression that is also invisible to users. However, queries are still billed as if you were scanning uncompressed bytes.  Ultimately, the decision between these two platforms depends on your specific needs and preferences.

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BigQuery vs Snowflake: Use Case

When deciding which data warehouse solution to use, it is important to consider the specific needs of your organization. If cost savings are a priority, then Snowflake may be the best option thanks to its competitive pricing and automatic scaling capabilities. On the other hand, if you need a flexible solution that can accommodate different workloads, then Google BigQuery might be a better fit. And if you're interested in data mining, BigQuery is also a good choice. Ultimately, the decision comes down to what your organization needs most.

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Conclusion

Overall, Snowflake and Google BigQuery are both powerful data warehouse solutions. They both offer fast query speeds, scalability, ease of use, and a variety of integrations. However, Snowflake is better suited for businesses with little resources, as it offers seamless, non-destructive scaling that occurs automatically. Additionally, Snowflake's capabilities in terms of data type support and integrations may be slightly more robust than those of BigQuery. Boltic can help you with setting up and managing your data warehouse on either Snowflake or BigQuery. Our strong integration with various data sources and analytical tools can help you get the most out of your data warehouse solution.

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