Every modern data stack is anchored by a cloud data warehouse. Gaining insights from your data is practically impossible without a cloud-based data warehouse. A data warehouse is fundamentally a platform for analytics that stores data from numerous data sources for analysis. We will see Bigquery v/s Snowflake, in this post.
The snowflake was created by and for the cloud. As a result, it has essentially no management or operational overhead and no baggage. Since Snowflake is a native SaaS service, it takes care of all the backend infrastructure, allowing you to concentrate on what’s really important: drawing conclusions from your data. Due to Snowflake’s tremendous scalability, virtually infinite concurrent searches are possible.
Similar to Snowflake, BigQuery eliminates the need for infrastructure setup and upkeep. Instead, you should concentrate on using regular SQL to find insightful information. There is no other cloud provider that supports Google BigQuery because it is entirely Google-native.
Snowflake is a totally server less solution built on ANSI SQL that completely separates storage from computing. To give you the best of both worlds, its architecture is built on a variety of conventional shared-disk and shared-nothing systems. Using a central repository for persisted data, it makes your data accessible to all computing nodes in the platform. It is important to know about Data warehouse v/s data-lake.
For all of your queries, Snowflake uses MPP (massively parallel processing). This indicates that a subset of the complete data collection is stored locally on each individual compute cluster (virtual machine or server). Your data is separated into micro partitions by Snowflake, which then optimizes and compresses them for columnar storage.
In order to be stored in cloud storage, all of the data that is loaded into Snowflake is actually rearranged, optimized, and compressed into a columnar format. File size, structure, compression, metadata, statistics, and other data items that are only accessible through SQL queries and are not readily visible to you are all components of data storage that Snowflake automatically manages.
Snowflake employs “virtual warehouses” or clusters of computing resources to carry out processing. Each warehouse acts as a different node in an MPP. In order to handle user requests, authentication, infrastructure management, metadata management, query parsing and optimization, access control, etc., Snowflake’s cloud services layer organizes all actions across Snowflake.
Snowflake and Google BigQuery are quite similar in that they both isolate storage from compute and are serverless. As well, it is based on ANSI SQL. Its architecture is very different, though. Many different multi-tenant services, including Dremel, Colossus, Jupiter, and Borg, are used by BigQuery, which is powered by particular Google infrastructure technologies. Dremel, a sizable multi-tenant compute cluster used to process SQL queries, is the computing platform used by Google BigQuery.
BigQuery compresses data into a columnar format for Colossus, Google’s global storage system, in a manner similar to Snowflake. You are not dependent on a single point of failure thanks to Colossus’ handling of data replication, recovery, and distributed management. BigQuery quickly transfers your data between locations using Google’s Jupiter network. BigQuery uses Borg, Google’s alternative to Kubernetes, to manage all hardware resource allocation and orchestration.
Clusters can stop or start during busy or slow periods thanks to Snowflake’s auto-scaling and auto-suspend features. Your users cannot resize nodes using Snowflake, but they can easily resize clusters. Additionally, Snowflake gives you the option to automatically scale up to 10 warehouses with a 20 DML limit per queue per table.
In a similar vein, BigQuery manages everything in the background and automatically provisioned your more compute resources as needed. However, BigQuery has a default limit of 100 concurrent users. You may scale up and down automatically on both platforms depending on demand. Additionally, you may isolate workloads from various businesses in various warehouses using Snowflake, allowing various teams to work independently without encountering concurrency problems.
Safety and Compliance
Data at rest is automatically encrypted by Snowflake. It does, however, offer granular permissions for schemas, tables, views, procedures, and other objects, but not for columns. In contrast, BigQuery offers access controls for tables, views, and individual tables, as well as permissions on datasets.
Since BigQuery is a native Google service, you can also use other Google Cloud services that have security and authentication features built-in to BigQuery, which makes integrations much simpler. There is no built-in virtual private networking offered by Snowflake. The problem can be solved, though, if Snowflake is hosted by AWS, thanks to AWS Private Link.
You can control user roles, permissions, and data security with BigQuery and Snowflake. Each platform automatically scales in the background to meet your needs as your data volume increases and your queries get more complicated. All performance optimization is done automatically.
Additionally, all underlying infrastructure and maintenance are taken care of for you because each solution is provided as a SaaS service. Administrators may individually scale the computation and storage layers with Snowflake, while BigQuery takes care of everything automatically. As a result, workloads can be isolated without requiring the sizing and permission work involved with Snowflake’s virtual warehouses.
Protection of Data
Each of BigQuery and Snowflake does a great job safeguarding your data. Time Travel and Fail-safe are two elements of Snowflake that can aid with this. Snowflake’s Time Travel feature saves a previous version of your data before an update. Time Travel has a one-day standard retention duration, while enterprise customers can request a retention time up to 90 days. It is possible to apply time travel to databases, schemas, and tables.