Caching is done at two levels – local cache and result cache. This is utilized when a query is essentially re-run to fetch the same result. Query results are stored or cached for a certain timeframe (default is 24 hours). This feature ensures that customers don’t accrue a lot of costs while having many virtual data warehouses operate in parallel. These include the following –īecause it is super easy to spin up multiple virtual data warehouses with the desired number of compute nodes, it is a common practice to divide the workloads into separate clusters based on either Business Units (sales, marketing, etc.) or type of operation (data analytics, ETL/BI loads, etc.) It is also interesting to note that virtual data warehouses can be set to auto-suspend (default is 10 minutes) when they go inactive or in other words, no queries are being executed. to following certain generally applicable best practices.
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The idea of increasing query performance is switched from the traditional manual performance tuning options like indexing, sorting, etc. The Snowflake Features has been designed for simplicity and maximum efficiency via parallel workload execution through the MPP architecture. Know more about Snowflake Data Warehouse architecture here.
As mentioned in the boxes, these are a bunch of services that help tie together the different units of Snowflake ranging from access control/data security to infrastructure and storage management. This setup is more costly but it ensures data integrity and maximum performance.įinally, we have cloud services. It is not uncommon for a firm to have separate virtual data warehouses for individual business units like sales, marketing, finance, etc. These dedicated MPPs are also known as virtual data warehouses. All the SQL queries are part of a particular cluster that consists of several compute nodes (this is customizable) and are executed in a dedicated, MPP environment. Next up is the query processing level, this is where the SQL queries are executed. The data ingestion, compression, and storage are fully managed by Snowflake as a matter of fact, this stored data is not directly accessible to users and can only be accessed via SQL queries. Ingested cloud data is optimized before storing in a columnar format. Here’s a diagram depicting the fundamental Snowflake architecture – Image SourceĪt the storage level, there exists cloud storage that includes both shared-disk (for storing persistent data) as well as shared-nothing (for massively parallel processing or MPP of queries with portions of data stored locally) entities. To know more about Snowflake Data Warehouse, visit this link. Snowflake querying follows the standard ANSI SQL protocol and it supports fully structured as well as semi-structured data like JSON, Parquet, XML, etc. This comes in handy for firms working with multiple cloud providers. The customer can select which cloud provider they want for their Snowflake instance. A Snowflake Data Warehouse instance can easily be deployed on any of the three major cloud providers – The phrase ‘fully managed’ means users shouldn’t be concerned about any of the back-end work like server installation, maintenance, etc. Snowflake Data Warehouse is a fully managed, cloud data warehouse available to customers in the form of Software-as-a-Service (SaaS) or Database-as-a-Service (DaaS). What is Snowflake Data Warehouse? Image Source
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