A Comprehensive Guide to Snowflake Cloud Data Warehouse

Snowflake is a data warehouse that is provided as a Software-as-a-Service (SaaS). It is more flexible than the conventional data warehouse and is quicker and more user-friendly.  

Snowflake database is cloud-based data storage and enables businesses to store and analyze their data using cloud-based tools. Since Snowflake is offered as a SaaS, the Snowflake Computing team handles the total infrastructure, optimization, availability, data protection, and more.

The unique feature that lets Snowflake stand out among others is that it is a cloud-based data warehouse with separate storage and computing. Each can be scaled up or down independently and cost-effectively and you will be paying only for what you use. There are other cloud-based service offerings but it is the scalability factor that puts Snowflake ahead of the rest.   

   

The Snowflake Database Architecture

The architecture of Snowflake is compatible with both structured and semi-structured data and is a fusion of the traditional shared-disk database architectures and shared-nothing database architectures. As in shared-disk architecture, Snowflake makes use of a central data repository for persisted data and users can access it from all compute nodes within the data warehouse. Again, as in shared-nothing architectures, Snowflake processes queries using MPP (massively parallel processing) compute clusters. Here, each node in the cluster stores a certain set of the entire data locally. The advantage here is that users get the management simplicity of shared-data architecture along with the optimized performance and scale-out of a shared-nothing architecture. 

There are three components of the Snowflake database architecture.

• Database storage – When data is loaded into Snowflake, it manages every aspect of storage – file size, organizing data, structure, statistics, metadata, and compression. You cannot directly access or see the data objects stored by Snowflake but can do so through SQL query operations run through Snowflake. 

• Query processing – Execution of queries is carried out in the processing layer using “virtual warehouses”. Each of them is an MPP compute cluster comprising multiple compute nodes allocated by Snowflake. The clusters are independent of each other and do not share compute resources with other virtual warehouses. 

• Cloud Services – This layer is a collection of services that synchronize activities across Snowflake and processing of all user requests from login to query dispatch is tied together by its different components. The services include authentication, infrastructure management, query parsing and optimization, access control and metadata management. 

The Major Benefits of Snowflake:

The benefits accrued from choosing Snowflake as a cloud database depends on the specific needs of an organization. However, the most significant and the main ones are given here.

• Snowflake leverages the standard SQL query language and since most teams use this language, Snowflake can be easily set up and running quickly.

• Common data formats like JSON, Avro, Parquet, ORC, and XML are supported by Snowflake. It is easy to store structured, unstructured, and semi-structured data and process any other type of data that can exist in a single data warehouse.  

• Snowflake provides a high level of user experience to data engineers and data analysts. While the data engineer can use the administrative interface for loading data and working on it from the application side, the analyst can consume and derive vital insights from the processed data. 

• A critical benefit of the Snowflake database is its ability to warehouse scaling. This enables it to handle the needs of the warehouse without having to redistribute the data. 

• Snowflake brings all the benefits of a virtual data warehouse. It can be started, stopped, or scaled at any time without impacting queries that are running. It can be set to auto-suspend or auto-resume to enable database operations to be suspended during a period of inactivity and resumed when a query is submitted. Finally, the database can be set to scale automatically with minimum and maximum cluster size. For example, you can set minimum 1 and maximum 3 so that depending on the load, provision can be made by Snowflake to vary between 1 to 3 multi-cluster warehouses only.   

Data Analytics on Snowflake:

There are several factors to be considered for running analytics on the Snowflake database. It includes scalability, cost, and maintenance as well as ease of accessibility to processed data for gaining incisive business insights. Snowflake is maximized for data analytics and any volume of data can be stored and scrutinized by any number of people or processes without resource contention. It integrates directly with most analytics tools through the Snowflake Partner program and provides quick access to a sophisticated visualization layer from within the database. 

Connecting to Snowflake:

There are several ways to connect to Snowflake – web-based user interface, command-line clients which also manage access, and ODBC and JDBC drivers that can be used by other applications. Native connectors like Python can also be used for connecting to Snowflake. 

Author Bio: BryteFlow is an authority on one of the fastest-moving technology industry components - building efficient automated environments for analytics through leveraging the AWS ecosystem. He has deep industry knowledge and has always championed the cause of data analytics for enterprises and consumer customers. Thomas Flinn is an avid writer and blogger on technology trends.